WO2022241455A1 - A synthetic circuit for buffering gene dosage variation between individual mammalian cells - Google Patents

A synthetic circuit for buffering gene dosage variation between individual mammalian cells Download PDF

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WO2022241455A1
WO2022241455A1 PCT/US2022/072286 US2022072286W WO2022241455A1 WO 2022241455 A1 WO2022241455 A1 WO 2022241455A1 US 2022072286 W US2022072286 W US 2022072286W WO 2022241455 A1 WO2022241455 A1 WO 2022241455A1
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gene
expression
mirna
mir
cell
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French (fr)
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Francois St-Pierre
Jin Yang
Oleg A. IGOSHIN
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Baylor College Of Medicine
William Marsh Rice University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/63—Introduction of foreign genetic material using vectors; Vectors; Use of hosts therefor; Regulation of expression
    • C12N15/79—Vectors or expression systems specially adapted for eukaryotic hosts
    • C12N15/85—Vectors or expression systems specially adapted for eukaryotic hosts for animal cells
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2310/00—Structure or type of the nucleic acid
    • C12N2310/10—Type of nucleic acid
    • C12N2310/14—Type of nucleic acid interfering N.A.
    • C12N2310/141—MicroRNAs, miRNAs
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2830/00—Vector systems having a special element relevant for transcription
    • C12N2830/001—Vector systems having a special element relevant for transcription controllable enhancer/promoter combination
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2830/00—Vector systems having a special element relevant for transcription
    • C12N2830/001—Vector systems having a special element relevant for transcription controllable enhancer/promoter combination
    • C12N2830/002—Vector systems having a special element relevant for transcription controllable enhancer/promoter combination inducible enhancer/promoter combination, e.g. hypoxia, iron, transcription factor
    • C12N2830/003—Vector systems having a special element relevant for transcription controllable enhancer/promoter combination inducible enhancer/promoter combination, e.g. hypoxia, iron, transcription factor tet inducible

Definitions

  • Embodiments of the disclosure include at least the fields of gene expression, recombinant technology, research, and medicine.
  • BACKGROUND [0004] Expressing genes of interest from synthetic cassettes is critical for studying natural proteins, producing reagents of commercial interest, and constructing synthetic biological circuits. Uniform expression among individual cells is needed when expressing genes whose properties depend on their concentration [1]. For example, many natural and engineered proteins can be nonfunctional or undetectable at low concentrations, and aggregate, mislocalize, or display aberrant function at high expression levels [2–7]. Expression homogeneity would also facilitate the development of synthetic biological circuits with predictable behavior at the single- cell level [8,9].
  • chromosomal expression requires genomic integration to be repeated and validated for each cell type.
  • the limitations of classical expression methods have motivated the development of plasmid-based gene dosage compensation circuits – synthetic circuits that buffer plasmid copy number variation.
  • the per-plasmid expression rate is inversely proportional to the copy number; the total protein expression thus remains constant (Fig.1).
  • the present disclosure concerns systems, methods, and compositions to address such needs in the art.
  • the disclosure regards systems, methods, and compositions that allow controllable variability of copy numbers of vectors (including at least plasmids) and, therefore, enhanced expression homogeneity when compared to known systems.
  • the disclosure provides means for buffering plasmid copy number variation to reduce cell to-cell expression variability within a transfected population.
  • the disclosure concerns protein expression circuits that buffer copy number variation at the single-cell level. These circuits couple two different mechanisms that are each inhibitory loops with specific topologies: (1) a transcriptional negative feedback (NF) loop; and (2) post-transcriptional incoherent feedforward control (IFF).
  • NF transcriptional negative feedback
  • IFF post-transcriptional incoherent feedforward control
  • engineered circuits that utilize both of these mechanisms buffer expression heterogeneity caused by plasmid dosage variation between individual mammalian cells.
  • the identification of such circuits were guided by computational models that provided circuit design and gave mechanistic insight into their underlying efficacy.
  • the circuits can function in multiple cell types and outperform other compensation circuits at the single-cell level.
  • the system when incorporated into replicating plasmids, the system enables long-term gene expression with cell-to-cell variation comparable to chromosomal expression.
  • the system of the disclosure is utilized to generate extrachromosomal cell lines.
  • certain plasmids, episomes can replicate in mammalian cells and are utilized as part of the circuit system that includes low expression heterogeneity as normally seen using chromosomal expression.
  • a gene expression system comprising a negative feedback loop and an incoherent feedforward control for expression of at least one gene of interest, wherein components of the negative feedback loop and incoherent feedforward control are optionally regulated by the same regulatory sequence.
  • the negative feedback loop is a transcriptional negative feedback loop and/or wherein the incoherent feedforward control is a post-transcriptional incoherent feedforward control.
  • the negative feedback loop component may lack regulation by an miRNA, in some cases, and/or the negative feedback loop may comprise a repressor that represses expression of its own gene.
  • the system lacks an incoherent feedforward loop component.
  • the system may be further defined as comprising an expression construct comprising at least two components to regulate production of a gene product from the 1, 2, 3, or more genes of interest, said components comprising: [0012] (a) a sequence encoding a repressor that represses expression of itself and the gene of interest, wherein the expression of the repressor sequence is regulated by a cognate operator site to which the repressor may bind, wherein the sequence encoding the repressor and the gene of interest are expressed from different promoters or are on a multicistronic vector or are separated by a ribosome-skipping sequence or an internal ribosome entry site, [0013] (b) one or more sequences encoding a miRNA located anywhere in the overall sequence of
  • the regulatory sequence that regulates expression of the transcriptional negative feedback loop and post-transcriptional incoherent feedforward control comprises a constitutive, inducible, or tissue-specific promoter.
  • the repressor is a tetracycline repressor, a Lac repressor that binds to one or more lacO operator sites, a dCas9, TALEN, or Zinc finger.
  • the cognate operator site to which the repressor may bind may be one or more copies of tetO2.
  • the miRNA may or may not be flanked by splicing sites, and examples of miRNA include miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
  • the expression construct comprises two non-identical genes of interest, wherein a first gene of interest and a second gene of interest are separated by a ribosome-skipping sequence or an internal ribosome entry site, and wherein said first gene of interest, ribosome-skipping sequence or an internal ribosome entry site, and second gene of interest are downstream from the sequence encoding the repressor and separated from the sequence encoding the repressor by a ribosome-skipping sequence or an internal ribosome entry site.
  • the (a) sequence encoding the miRNA is downstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites.
  • the (a) sequence encoding the miRNA may be upstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites, or the miRNA target sites are elsewhere in the expression construct.
  • the (a) sequence encoding the miRNA is within the sequence encoding the repressor, the ribosome- skipping sequence/ internal ribosome entry site, or the gene of interest flanked by the miRNA target sites.
  • the gene of interest for the system may be a reporter protein, a gene-editing reagent (CRISPR-Cas9 component), a therapeutic protein, an enzyme, an optogenetic reagent, a chemogenetic reagent, or a combination thereof.
  • reporter proteins include a fluorescent protein (blue, cyan, green, yellow, red, far-red, or infrared fluorescent protein), a fluorescent indicator, a bioluminescent protein, or a bioluminescent indicator.
  • the system may be one or more 2A self-cleaving peptides, such as T2A, P2A, E2A, F2A, or a combination thereof.
  • the expression construct may be on a plasmid or episome, each that may be in a cell, such as a mammalian or yeast cell.
  • an extrachromosomal vector such as a plasmid
  • it may be integrated into the chromosome and, in specific cases, such integration may yield gene expression levels that is more independent of the integration site chosen.
  • Embodiments of the disclosure include polynucleotides comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0018] (a) one or more operator sites (e.g., tetO2 or lacO2) that regulate expression of promoter expression; [0019] (b) one or more miRNA target sites; [0020] (c) sequence encoding a repressor; [0021] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0022] (e) one or more genes of interest; [0023] (f) one or more miRNA target sites; and [0024] (g) one or more miRNA-encoding sequences.
  • a one or more operator sites
  • etetO2 or lacO2 e.g., tetO2 or lacO2
  • the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction one or more additional genes of interest.
  • the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction another ribosome-skipping sequence or an internal ribosome entry site and another gene of interest different from the gene in (e).
  • the repressor may be a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger.
  • the sequence encoding the miRNA may or may not be flanked by splicing sites.
  • the miRNA is located at the 5’ or 3’ end of the construct instead of being flanked by splicing sites.
  • miRNAs include miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
  • the expression construct comprises one or more regulatory sequences that regulate expression of the repressor, the gene of interest, and, optionally, also the miRNA. The repressor and miRNA may be expressed from different regulatory sequences than the gene of interest.
  • Embodiments of the disclosure include episomes comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0029] (a) one or more operator sites that regulates expression of a repressor; [0030] (b) one or more miRNA target sites; [0031] (c) sequence encoding a repressor; [0032] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0033] (e) one or more genes of interest; [0034] (f) one or more miRNA target sites; and [0035] (g) one or more miRNA-encoding sequences.
  • the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction multiple iterations of ribosome-skipping sequence or an internal ribosome entry site with another gene of interest different than the gene in (e).
  • the repressor is a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN or Zinc finger
  • the sequence encoding the miRNA may or may not be flanked by splicing sites, given that it may be located at the 5’ or 3’ end of the construct.
  • the expression construct comprises one or more regulatory sequences that regulate expression of the repressor, the gene of interest, and, optionally, also the miRNA.
  • the repressor and miRNA may be expressed from different regulatory sequences than the gene of interest.
  • the ribosome- skipping sequence is at least one 2A self-cleaving peptide, including T2A, P2A, E2A, F2A, or a combination thereof.
  • Embodiments of the disclosure include plasmids, comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0039] (a) one or more operator sites that regulates expression of a repressor; [0040] (b) one or more miRNA target sites; [0041] (c) sequence encoding a repressor; [0042] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0043] (e) one or more genes of interest; [0044] (f) one or more miRNA target sites; and [0045] (g) one or more miRNA-encoding sequences.
  • the expression construct between (e) and (f) may or may not further comprise in a 5’ to 3’ direction one or more other ribosome-skipping sequences or an internal ribosome entry sites associated with another gene of interest different from the gene in (e).
  • the repressor may be a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger.
  • the sequence encoding the miRNA may be flanked by one or more splicing sites.
  • the expression construct comprises a regulatory sequence that regulates expression of the repressor, the gene of interest, and the miRNA, including at least the ribosome-skipping sequence is a 2A self-cleaving peptide.
  • Embodiments of the disclosure include cells, including cell lines, that comprise any system encompassed herein, any polynucleotide encompassed herein, any episome encompassed herein, or any plasmid encompassed herein.
  • the cells may or may not be mammalian cells or yeast cells.
  • the cells are comprised in a suitable cryopreservation medium. Pluralities of the cells are encompassed herein, including compositions that comprise a plurality.
  • Embodiments of the disclosure encompass methods of regulating expression of at least one gene of interest, comprising the steps of: [0049] (1) providing a system, optionally in a cell or cell-free medium, wherein said system comprises a polynucleotide (plasmid or episome, in at least some cases) comprising an expression construct that comprises in a 5’ to 3’ direction: [0050] (a) one or more operator sites that regulates expression of a repressor; [0051] (b) one or more miRNA target site; [0052] (c) sequence encoding a repressor; [0053] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0054] (e) one or more genes of interest; [0055] (f) one or more miRNA target sites; and [0056] (g) one or more miRNA-encoding sequences, wherein said miRNA-encoding sequence is flanked by splicing sites; [0057] (2)
  • the compound that induces expression is doxycycline or a functionally similar compound (tetracycline and/or minocycline).
  • an effective amount of doxycycline may be about 0-1000 ng/mL.
  • the method further comprises the step of transfecting or transducing the cells with an effective amount of the respective plasmid or episome, and an effective amount of the plasmid or episome for transfecting or transducing may be about 1-200 ng per approximately 24,000 cells.
  • a method of reducing gene expression variability between individual cells of a gene of interest comprising the steps of transfecting or transducing a plurality of cells with a plasmid or episome comprising an expression construct that expresses a single transcript encoding one or more components of a transcriptional negative feedback loop and one or more components of a post-transcriptional incoherent feedforward control for expression of the gene of interest, wherein the components of the transcriptional negative feedback loop and the post-transcriptional incoherent feedforward control are regulated by the same regulatory sequence.
  • the expression construct is further defined as expressing a single transcript encoding a tetracycline repressor, a gene product from the gene of interest, and a miRNA flanked by splicing sites, wherein following exposure of an effective amount of an inducer to inhibit the repressor, the transcript is expressed and the miRNA is spliced out to inhibit production of the gene product of interest.
  • FIG. 1a-1b Gene expression from ideal dosage compensation circuits does not vary with plasmid copy number.
  • Figures 2a-2f Combining incoherent feedforward (IFF) and negative feedback (NF) loops is predicted to widen the copy number range with efficient dosage compensation.
  • IFF incoherent feedforward
  • NF negative feedback
  • n 8 (Equalizer-L) or 3 (-M/-H) independent transfections.
  • 100 ng circuit plasmids were used per transfection.
  • (3c) Equalizer plasmids produced lower cell-to-cell expression variability than plasmids with unregulated promoters. p ⁇ 0.01 for all pairs in Tukey’s tests.
  • Cells transfected with Equalizer-L produced similar variability as cells with a chromosomally-integrated unregulated CMV cassette (CMV cell line); p > 0.99, Tukey’s test. Circuit output values were relative to that of Equalizer-L. Mean ⁇ SEM are shown; some error bars are too small to be seen.
  • n 3 (unregulated circuits and Equalizer-M & -H) or 8 (Equalizer-L) independent transfections.
  • n 3 independent cell cultures (CMV cell line).
  • Equalizer-L was induced with 1 ng/mL of doxycycline.
  • Equalizer-L produced lower cell-to-cell variability than the CMV promoter in five cell lines. The black circles are independent transfections.
  • Equalizer-L was induced with 1.0 ng/mL of doxycycline. Mean ⁇ SEM are shown; some error bars are too small to be seen.
  • n 6 per circuit. ****, p ⁇ 0.0001; Sidak’s test. (3e) Representative output-level histograms. Each histogram was normalized to its peak.
  • Equalizer-L was induced with 1 ng/mL of doxycycline.
  • Equalizer-L has superior gene dosage compensation than an alternative circuit that combines post-transcriptional NF and IFF motifs.
  • 5a-5d Circuit schematics. mScarlet-I (RFP) and mCitrine (YFP) are reporters of circuit output and gene dosage, respectively.
  • CMV (c) and OLP (5d) do not have dosage compensation circuitry and are used as controls for Equalizer-L (5a) and HYB (5b), respectively.
  • 5e Representative circuit- output histograms.
  • Equalizer-L was induced with 1 ng/mL of doxycycline.
  • Equalizer-L (5g) showed superior gene dosage compensation than HYB (5h) at the population level.
  • Tukey’s test was used to compare the Equalizer-L episome with the other conditions. ns, not significant; *, p ⁇ 0.05; **, p ⁇ 0.01; ***, p ⁇ 0.001; ****, p ⁇ 0.0001.
  • (6b) Representative images of HEK293 cells expressing EGFP from episomes or the chromosome at 23 days post-transfection. Each image is displayed with a linear lookup table with the minimum set to 0 and the maximum set to the sum of the mean intensity value and three standard deviations (see Methods). This approach enables a qualitative comparison of the cell-to-cell expression variability despite large differences in mean circuit output. Insets, binary masks to help identify regions of the images that correspond to cells (white region).
  • a common input drives the expression (black arrows) of an inhibitor (e.g., miRNA) and an output protein (e.g., EGFP).
  • an inhibitor e.g., miRNA
  • an output protein e.g., EGFP
  • the inhibitor only represses (red blunted arrow) the output protein production and does not repress its own expression.
  • a common input e.g., plasmid
  • drives the expression of an inhibitor e.g., Tet repressor protein
  • an output protein e.g., EGFP
  • FIGS. 8a-8b The gene dosage compensation profiles of NF circuits can be tuned by changing the doxycycline concentration. Predicted gene dosage compensation at different doxycycline concentrations for (8a) an ideal NF circuit or (8b) an NF circuit that is ”leaky”. The leaky NF circuit has incomplete repression at saturating concentrations of TetR.
  • the solid lines correspond to the doxycycline concentrations that predicted the lowest CV with the fitted plasmid copy number distribution described in “Estimating the distribution of plasmid copy numbers in transfected cells” of Example 8, 1ng/mL for the ideal NF and 10ng/mL for leaky NF. The calculation of the predicted gene dosage compensation score is described in the Examples.
  • FIG. 9a-9c A TetR-based negative feedback circuit shows incomplete repression when uninduced.
  • 9a Schematic of an EGFP expression cassette with negative feedback (NF) control.
  • 9b-9c Uninduced NF circuits produce substantial fluorescence.
  • 9b Representative images of HEK293A cells transfected with plasmids encoding the NF circuit without inducer (left) and under saturating amounts of inducer (1000 ng/mL doxycycline, right). Images are shown with the same lookup table. Scale bars, 50 m.
  • the Equalizer circuit is predicted to display improved gene dosage compensation compared with its incoherent feedforward (IFF) subcircuit over a wide range of RISC concentrations and miRNA- target binding affinities.
  • IFF incoherent feedforward
  • RISC abundance was varied 9-fold (from 0.2 to 1.8-fold).
  • the solid lines corresponds to 1.7e+05 RISC complexes/cell.
  • the simulated doxycycline concentration was 1 ng/mL.
  • FIGS 11a-11k Schematics of Equalizer and unregulated expression plasmids.
  • (11a-11e) Schematics of Equalizer plasmids.
  • bGlob intron is the rabbit ⁇ -globin intron II.
  • WPRE is the woodchuck hepatitis virus post-transcriptional regulatory element.
  • bGlob intron and WPRE were placed at the 5’ and 3’ UTRs, respectively, to increase the overall gene expression levels.
  • 11f-11k Schematics of unregulated expression plasmids. For the CMV and PGK unregulated promoters, additional variants were made with tetO2, bGlob intron, and WPRE for closer similarity to the Equalizer circuits.
  • FIG. 13a-13b Mean gene-dosage reporter and mean circuit output levels of Equalizer-L and unregulated promoter circuits at different transfection plasmid doses. (13a & 13b) Data is from the same experiments as Figs.3e-3g.
  • Gene-dosage reporter levels of individual cells were quantified as red fluorescence.
  • 14a Representative histograms of gene-dosage reporter levels. At each plasmid dose, the gene-dosage reporter distributions of the three circuits were similar.
  • FIGS 15a-15c Schematics of Equalizer-L and the standalone NF and IFF circuits
  • Equalizer-L (15b) the negative feedback (NF) circuit
  • IFF incoherent feedforward circuit
  • bGlob intron is the rabbit ⁇ –globin intron II.
  • WPRE is the woodchuck hepatitis virus post-transcriptional regulatory element. bGlob intron and WPRE were placed at the 5’ and 3’ UTRs, respectively, to increase the overall gene expression levels.
  • Equalizer-L produces a narrower circuit output distribution than the standalone NF or IFF circuits in transfected cells.
  • FIG.4b & 4c Representative contour plots of flow cytometry data used in Fig.4b & 4c are shown.
  • the inducer was used at the concentration providing the lowest cell-to-cell output variability: 0.5 ng/mL for the Equalizer-L and 10 ng/mL for the NF circuit.
  • the plasmid expressing mCherry (i.e., an RFP) from the constitutive promoter CMV ( Figure 11k) was co-transfected with the circuit plasmids. Gene-dosage reporter levels were estimated as red fluorescence. Histograms of gene-dosage reporter (top edge) and circuit output (right edge) are shown. The dots indicate data points that were outside the lowest contour lines (i.e., 10% line) [0078] Figure 17.
  • Equalizer-L is predicted to show superior dosage compensation compared with IFF and NF circuits. Equalizer-L and NF circuit were simulated with doxycycline concentrations that were predicted to give the best gene dosage compensation (1 ng/mL for Equalizer-L and 10 ng/mL for NF; see Fig.4b). The predicted gene dosage compensation is defined in the Examples. See “Model description of four key topologies utilized to predict gene dosage compensation” of Example 8 for the model description and Tables 1 and 2 for other parameters used in the simulations. [0079] Figure 18. Equalizer-L shifts the saturation of RISC to higher plasmid copy numbers. The solid lines show the predicted output protein levels. Dashed lines show the abundance of free RNA-induced silencing complex (RISC).
  • RISC free RNA-induced silencing complex
  • 20b HYB-transfected cells (left) produce a bimodal and wide circuit output distribution while Equalizer-L-transfected cells (right) produce a narrow unimodal distribution.
  • the plasmid dose was 50 ng.
  • Histograms of gene-dosage reporter (top edge) and circuit output (right edge) are shown.
  • the non-transfected cell population is included as a reference to show baseline fluorescence levels. Biexponential axes are used.
  • the dots indicate data points outside the lowest contour lines (i.e., 10% line).
  • Figures 22a-22c Schematics of episomal Equalizer-L and unregulated plasmids used to characterize gene dosage compensation of Equalizer-L over a two-month period.
  • bGlob intron is the rabbit ⁇ -globin intron II.
  • bGlob intron was placed at the 5’ UTR to increase the overall gene expression levels.
  • Hph gene encodes the hygromycin-B-phosphotransferase protein, which confers resistance to Hygromycin-B.
  • EBNA-1 is the Epstein-Barr nuclear antigen-1.
  • OriP is the origin of replication of the Epstein-Barr virus. EBNA-1 and OriP are necessary to maintain episomal plasmids in transfected cells.
  • Figures 23a-23c Determination of fluorescence thresholds between expressing and non-expressing cells in flow cytometry experiments with episomes. The boundaries between the quadrants (Q1-4) were set to minimize false positives using cellular fluorescence distributions from (23a) untransfected (GFP- and RFP-) cells, (23b) cells stably expressing EGFP from the genome, and (23c) episomes encoding an EF1 -mCherry expression cassette.
  • FIG. 24 Representative fluorescence images of episome-carrying cells over a two-month period. The same cell populations analyzed by flow cytometry (Fig.24) were also imaged by fluorescence microscopy. To help distinguish dim cells from the background, binary masks were also shown beside each fluorescent image. Magenta denotes cells, and blue denotes background. Two-photon microscopy was used to produce optical sectioning, thereby reducing variations in brightness due to differences in cell thickness. Scale bars, 50 ⁇ m. [0086] Figure 25. Circuit expression distributions from populations of episome-carrying cells over a two-month period.
  • the fluorescence of individual HEK293 cells was determined by flow cytometry. As a control, cells were grown and analyzed from a line containing a chromosomally-integrated CMV-EGFP expression cassette (column 2). This line was not transfected with episomes, but passaged and analyzed on the same days as other episome- transfected cells. Schematics of episome plasmids are shown in Fig.22. Each episome plasmid expressed mCherry (i.e., an RFP) from a constitutive EF1 promoter. Gene-dosage reporter level of individual cells was quantified as red fluorescence of the cells. Circuit output level was defined as EGFP fluorescence.
  • mCherry i.e., an RFP
  • (26d-26e) Reanalysis of (26d) cell-to-cell output variability (bars from left to right correspond to legend from top to bottom) and (26e) circuit output levels shown in Fig.6a & 6c, respectively. Reanalysis was conducted after including GFP+ and RFP- cells (see Fig.23). For all panels, mean values ⁇ SEM are shown. n 4 independent trials.
  • FIG. 27a-27e Equalizer components rewired to match a natural yeast dosage compensation topology is predicted to have less efficient gene dosage compensation than the original Equalizer topology.
  • FIG. 27a A schematic of the yeast galactose (GAL) pathway, a natural gene dosage. GAL1, 3, 4, and 80 are GAL pathway proteins. POI stands for protein of interest. Diagram adapted from Peng et al. [19].
  • (27c) A schematic of Equalizer components rewired to match the yeast GAL pathway topology in (27a). Compared with (7b), the miRNA does not inhibit the POI expression.
  • (d) Alternative rewiring of Equalizer components.
  • the miRNA in (27c) is replaced with a hypothetical TetR inhibitor that binds to TetR with 1:1 stoichiometry.
  • the miRNA was replaced with a TetR-inhibitor because a study has shown that a 1:1 stoichiometry of activator (e.g., TetR- inhibitor) and inhibitor (e.g., TetR) was important for dosage compensation [20].
  • (27e) Simulation results that compare the predicted gene dosage compensation of the circuits illustrated in (27b-27d).
  • Figures 29a-29e Description of topologies utilized to predict gene dosage compensation.
  • Figure 30 Modeling of a transcriptional negative feedback circuit.
  • Figures 31a-31d Estimating the distribution of plasmid copy numbers in transfected cells.
  • MSE Mean Square Error
  • ⁇ Scalar parameter
  • the MSE corresponds to the Mean Square Error of the simulated mean expression of the NF circuit at 8 different inducer concentrations (0, 1, 5, 10, 50, 100, 500, 1000 ng/mL) compared with experimental data.
  • the promoter leakage is defined as the following: leakage/(1+leakage) is the ratio of the expression under saturating TetR concentration and the expression under saturating inducer (doxycycline) concentration.
  • (31d) Dependence of the MSE on the scale parameter ( ⁇ ) at a leakage level of 0.25.
  • FIG. 32a-32b Predicting cell-to-cell variability.
  • Figure 33 Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L.
  • Figures 34a-34c Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L.
  • Equalizer circuits can be developed using alternative NF inhibitors, as shown here using LacI rather than TetR.
  • 34a Cell-to-cell variability as a function of IPTG concentration for both the NF-only circuit and the NF+IFF (Equalizer) design. The Equalizer circuit outperforms the NF-only circuit;
  • 34b Expression (circuit output) vs. IPTG concentration for a representative LacI-based NF-only circuit;
  • 34c Expression (circuit output) vs. IPTG concentration for a representative LacI-based Equalizer circuit.
  • Figures 35a-35e Improving mRNA stability is predicted to increase Equalizer circuit expression level and gene dosage compensation ability.
  • Embodiments of the disclosure encompass plasmid-based synthetic circuits, referred to herein as Equalizers, that buffer copy number variation at the single-cell level.
  • Equalizers couple a transcriptional negative feedback loop with post-transcriptional incoherent feedforward control.
  • the disclosed circuits produce as low cell-to-cell variation as chromosomally integrated genes.
  • episome-encoded Equalizers enable the rapid generation of extrachromosomal cell lines with stable and uniform expression.
  • the disclosure provides these circuits as simple and versatile devices for homogeneous gene expression and that can facilitate the engineering of synthetic circuits that function reliably in every cell. I.
  • Embodiments of the disclosure include systems that comprise synthetic circuits for buffering gene dosage variation between individual mammalian cells.
  • the systems incorporate control at both the transcriptional level and the pos- transcriptional level.
  • the systems of the disclosure utilize a bipartite approach to plasmid copy regulation that also impacts expression control by incorporating two different inhibitory loops: (1) a transcriptional negative feedback (NF) loop; and (2) post- transcriptional incoherent feedforward control (IFF).
  • NF transcriptional negative feedback
  • IFF post- transcriptional incoherent feedforward control
  • the systems utilize component(s) for the NF loop and component(s) for the IFF control on the same polynucleotide molecule and, in at least some cases, expression of the respective component(s) for the NF loop and component(s) for the IFF control is regulated by the same regulatory sequence, such as a promoter of any kind.
  • the components may utilize any proteins that bind a regulatory sequence to inhibit transcription. In specific cases, elements from the tetracycline operon system or lac operon may be utilized.
  • NF loop examples include a dCas9 with or without a coupled transcriptional repressor (coupled with a co-expressed guide RNA), a Zinc finger, or repressors based on transcription activator-like effectors (TALE) scaffolds.
  • TALE transcription activator-like effectors
  • the system includes an expression construct that comprises operably linked sequences including (1) a regulatory sequence to which a repressor can bind, (2) sequence that encodes a repressor, (3) gene of interest sequence that encodes a gene product of interest, wherein the repressor sequence and gene of interest sequence are separated by a ribosome-skipping sequence or an internal ribosome entry site (such as a 2A peptide cleavage sequence), (4) as part of the IFF component, miRNA target sequences that flank the repressor-2A-gene of interest sequences; and (5) sequence that encodes an miRNA that can bind the miRNA target sequences, wherein in at least some cases the miRNA is flanked by splicing sites.
  • operably linked sequences including (1) a regulatory sequence to which a repressor can bind, (2) sequence that encodes a repressor, (3) gene of interest sequence that encodes a gene product of interest, wherein the repressor sequence and gene of interest sequence are separated by a
  • a transcriptional repressor such as tetR
  • tetR is linked by a 2A sequence to a gene of interest from which a gene product of interest may be expressed, and the order of tetR and the gene of interest in a 5’ to 3’ direction may be of any order.
  • the gene product of interest may be of any kind and may be a protein or an RNA.
  • the repressor represses its own transcription by binding to a site upstream of the repressor, which may be referred to as a cognate operator site.
  • the repressor also represses transcription of the gene of interest and the miRNA from the IFF component of the system.
  • the sequence encoding the repressor, a 2A sequence, and the gene of interest are flanked by miRNA target sites, although in alternative cases the miRNA can be positioned so that it is expressed at the 5’ end or the 3’ end of the expressed transcript. Expression from the expression construct, and subsequent splicing at splicing sites that flank an miRNA sequence, produces the miRNA IFF component of the system that is then able to inhibit production of the gene product of interest.
  • a regulatory sequence such as a promoter, regulates expression from the expression construct that produces a transcript that comprises sequence that encodes the repressor, the gene of interest, and the miRNA (that is subsequently spliced out) on the same transcript molecule.
  • the promoter that regulates the expression construct may be of any kind, including constitutive, inducible, tissue-specific, and so on.
  • the promoter is the CMV promoter, PGK promoter, the UBC promoter, the EF1a promoter, the CAG promoter, the hSyn1 promoter, the CaMKII promoter, the GFAP promoter, the TRE promoter, SV40, synthetic promoters of the COMET family, synthetic promoters with cell-state specificity, and so on.
  • the expression construct employs a ribosome-skipping sequence or an internal ribosome entry site.
  • one or more ribosomal skipping sequences such as one or more 2A peptide cleavage sequences, are utilized. Examples of 2A sequences are below, where the GSG sequence is optional.
  • T2A EGRGSLL TCGDVEENPGP (SEQ ID NO:1) [0105] P2A (GSG) ATNFSLLKQAGDVEENPGP (SEQ ID NO:2) [0106] E2A (GSG) QCTNYALLKLAGDVESNPGP (SEQ ID NO:3) [0107] F2A (GSG) VKQTLNFDLLKLAGDVESNPGP (SEQ ID NO:4) [0108] Any gene of interest may be utilized in the system of the disclosure.
  • the gene product produced by the gene of interest may be of any kind and may be utilized for research or therapeutic purposes. In some embodiments, multiple gene products are produced in the system because multiple, nonidentical genes of interest are utilized in the same expression construct.
  • the IFF miRNA component may be an miRNA of any sequence as long as it includes complementarity sequence with its target sites.In specific embodiments it is miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a, for example.
  • the miRNA has known target sequences that are utilized in the expression construct by flanking the repressor-2A-gene of interest combined sequence.
  • the miRNA is flanked by splicing sites such that upon production of a pre-transcript that includes the repressor and gene of interest sequences, the miRNA sequence is spliced out of the pre-transcript so that it can act on its target sites.
  • the IFF miRNA component of the expression construct may be 5’ or 3’ to the NF sequences in a 5’ to 3’ direction of the polynucleotide. In other embodiments, other than using an miRNA the IFF is implemented using transcriptional activators and repressors.
  • RNA interference small interfering RNA (siRNA) and short hairpin RNA (shRNA); (2) endoribonucleases that degrade mRNAs, such as CasE; (3) a transcriptional IFF, in such cases where a promoter expresses an activator that would then turn on the gene of interest expressed from a different promoter that is upregulated by the activator; the activator would also upregulate a third promoter that produces a repressor that would then repress expression of the gene of interest.
  • siRNA small interfering RNA
  • shRNA short hairpin RNA
  • endoribonucleases that degrade mRNAs, such as CasE
  • a transcriptional IFF in such cases where a promoter expresses an activator that would then turn on the gene of interest expressed from a different promoter that is upregulated by the activator; the activator would also upregulate a third promoter that produces a repressor that would then repress expression of the gene of interest.
  • compositions of the disclosure include any entity that comprises the system, including polynucleotides that comprise the expression construct with the system components, cells that comprise the polynucleotides that comprise the expression construct with the system components, vectors that comprise the expression construct with the system components (including plasmids or episomes), and cells that comprise the vectors with the expression construct with the system components.
  • the cells may be of any kind, including prokaryotic or eukaryotic, including mammalian.
  • the system components (which includes equalizer circuits) may be integrated into the genome, e.g., to reduce the gene expression variability caused by genetic and epigenetic differences between integration positions.
  • the methods allow for controllable, substantially uniform production of amounts of gene products of interest, including at least natural or synthetic proteins, such as for their study or for producing substances of commercial interest.
  • the methods of the disclosure allow for reducing variability in copy numbers following transfection of plasmids in mammalian cells.
  • the methods provide for achievement of uniform expression levels in desired cells that comprise the system.
  • Embodiments of the disclosure include methods for regulating gene expression and includes methods of reducing gene expression variability between individual cells.
  • a method of reducing gene expression variability between individual cells of a gene of interest comprising the steps of transfecting or transducing a plurality of cells with a plasmid or episome comprising an expression construct that expresses a single transcript encoding one or more components of a transcriptional negative feedback loop and one or more components of a post-transcriptional incoherent feedforward control for expression of the gene of interest, wherein the components of the transcriptional negative feedback loop and the post- transcriptional incoherent feedforward control are regulated by the same regulatory sequence.
  • the expression construct is further defined as expressing a single transcript encoding a tetracycline repressor, a gene product from the gene of interest, and a miRNA flanked by splicing sites, wherein following exposure of an effective amount of an inducer to inhibit the repressor, the transcript is expressed and the miRNA is spliced out to inhibit production of the gene product of interest.
  • the method includes providing a system in a cell, where the cell comprises a polynucleotide such as a plasmid or episome that includes a particular expression construct comprising one or more components of a transcriptional negative feedback loop and of a post-transcriptional incoherent feedforward control.
  • the expression construct comprises in a 5’ to 3’ direction: (a) an operator site that regulates expression of a repressor; (b) an miRNA target site; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) a gene of interest; (f) an miRNA target site; and (g) an miRNA-encoding sequence, wherein said miRNA-encoding sequence is flanked by splicing sites.
  • the cells are exposed to an effective amount of an inducer compound that induces expression from the expression construct by inhibiting the repressor.
  • the expression from the expression construct produces a transcript that comprises sequence encoding the repressor, sequence encoding a gene product from the gene of interest, and the miRNA-encoding sequence.
  • the miRNA recognizes the miRNA target sites on the transcript that results in inhibition of production of the gene product.
  • the concentration of inducer that is utilized in the method facilitates substantially uniform production of the gene product of interest.
  • an effective amount of doxycycline may be about 0-1000 ng/mL.
  • Ranges for an effective amount of doxycycline include 0.5-1000, 0.5-750, 0.5-500, 0.5-250, 0.5-100, 0.5-50, 0.5-5, 5-1000, 5-750, 5-500, 5-250, 5-100, 5-50, 50-1000, 50-750, 50-500, 50-250, 50-100, 100-1000, 100-750, 100-500, 100-250, 250-1000, 250-750, 250-500, 500-1000, 500-750, or 750-1000 ng/mL.
  • the method further comprises the step of transfecting or transducing desired cells with an effective amount of the plasmid or episome.
  • the methods employ two different inhibitory loops to control expression and production of a gene of interest.
  • the two inhibitory loops are a tetracycline-based transcriptional negative feedback loop and an miRNA-based post-transcriptional incoherent feedforward control.
  • the gene expression is regulated by multiple inhibitory mechanisms, the gene product is nevertheless produced at least as a function of the plasmid concentration. For example, in specific cases, at low plasmid concentration the concentration of miRNA is sufficiently low so that the inhibition loop may be weak enough to allow production of the gene product of interest. In other cases, a higher plasmid concentration produces stronger inhibition and in at least certain embodiments this is how the system compensates for plasmid dosage.
  • the gene expression is regulated by multiple inhibitory mechanisms, the gene product is nevertheless produced at least as a function of incomplete inhibition of transcription of the gene of interest. This is a function of the TetR repressing itself, in specific cases of the system.
  • kits of the Disclosure Any of the system components or compositions described herein may be comprised in a kit.
  • the kit may comprise polynucleotides, cells, primers, buffers, salts, instructions, and so forth for generation or use of the system of the disclosure.
  • an expression construct, vector including the expression construct, or vector for insertion of the expression construct may be included, including reagents to generate them.
  • Certain cells for use of the system may be included, including mammalian cells, for example.
  • compounds that control at least part of the system may be included in the kit, including, for example, an inducer that induces transcription of the expression construct upon repression of the repressor.
  • the components of the kits may be packaged either in aqueous media or in lyophilized form.
  • the container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted.
  • the liquid solution is an aqueous solution, with a sterile aqueous solution being particularly preferred.
  • the components of the kit may be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container means.
  • the kits may comprise a second container means for containing a sterile, pharmaceutically acceptable buffer and/or other diluent.
  • the kit also will generally contain a second, third or other additional container into which the additional components may be separately placed.
  • additional components may be comprised in a vial.
  • Such containers may include injection or blow molded plastic containers into which the desired vials are retained.
  • the kit comprises a plasmid or episome that includes one or more of the expression construct components but lacks the gene of interest such that a user may with standard recombinant technology incorporate their gene of interest into the construct for desired purposes.
  • the kit may also include doxycycline or a functional derivative thereof.
  • the cells may or may not be cryopreserved, for example.
  • EXAMPLES [0126] The following examples are included to demonstrate embodiments of the disclosure. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the methods and compositions of the disclosure. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the disclosure.
  • miRNA microRNA
  • RNA-FF4 a synthetic miRNA with a strong affinity to its target sites [21–23]
  • miR-FF4 target sites miRNA-FF4 target sites
  • GOI gene of interest
  • RISC RNA-Induced Silencing Complexes
  • NF tetracycline repressor protein
  • Fig.2c porcine teschovirus-1
  • tetO2 tetracycline repressor protein
  • Equalizer-M uses miR-FF4 while Equalizer-H uses miR-FF3, a miRNA with lower affinity to its target than miR-FF4 [34] (Fig.11a & 11b).
  • Equalizer-L was also constructed, which encodes miR-FF4 like Equalizer-M but incorporates a second miRNA target site upstream of the start codon (Fig.11c, Fig.2e), an arrangement that can increase miRNA-based inhibition [23].
  • the Equalizer variants and control plasmids were constructed with the enhanced green fluorescent protein (EGFP) as circuit output reporter.
  • EGFP enhanced green fluorescent protein
  • mCherry RFP spectrally compatible fluorescent reporter
  • Fig.11e co-transfected plasmid
  • Fig.11d co-transfected plasmid
  • Fig.11d co-transfected plasmid
  • Fig.11d co-transfected plasmid
  • Fig.11d co-transfected plasmid
  • Fig.11d co-transfected plasmid
  • mCherry fluorescence values were used to approximate the relative number of actively- expressing plasmids.
  • HEK293 cells were transfected with Equalizer plasmids and measured single-cell fluorescence using flow cytometry.
  • the coefficient of variation (CV) was utilized of EGFP fluorescence in mCherry+ cells to measure cell-to-cell variability in circuit output levels.
  • Flow cytometry and microscopy produced similar CVs of circuit output, demonstrating that the CV is robust to differences in the method used to quantify single-cell fluorescence (Fig.12).
  • Flow cytometry was utilized in ensuing experiments, given the high throughput of this technique.
  • the model predicted that doxycycline could be used to tune Equalizers’ gene dosage compensation range and profile (Fig.10a), as previously shown with NF circuits (Fig.8).
  • the cell-to-cell output variability was quantified at several doxycycline concentrations from 0 to 30 ng/mL (Fig.3a).
  • the shape of the CV dependence on inducer concentration was non-monotonic, with intermediate concentrations producing the lowest expression variability.
  • the relative expression levels were quantified over the same range of inducer 132 concentrations (Fig.3b). Induction increased expression of all three Equalizers by a maximum of 4-8 fold. Equalizer-L produced lower expression variability but also lower expression.
  • Equalizer-H produced 5.2 and 22 times higher fluorescence than Equalizer-L at 0 and 30 ng/mL, respectively.
  • UBC ubiquitin C
  • CMV cytomegalovirus
  • the greater variability produced by the PGK and UBC promoters than the CMV promoter may be due to increased burstiness of the weaker PGK and UBC promoters [36], saturation of gene expression due to limited cellular resources when using the strong CMV promoter, or both.
  • Equalizer circuits It was determined to what extent the cell-to-cell variability observed with Equalizer circuits was due to residual dependence on plasmid copy number rather than other sources of variation such as intrinsic noise [36], differences in expression capacity [37], or measurement noise. A condition was generated without copy number variation by chromosomally integrating an EGFP expression cassette with the same promoter (CMV) as the Equalizer circuit.
  • Equalizer-L The variation produced by Equalizer-L and the CMV cell line were both similar (CV ⁇ 71), demonstrating the potency of Equalizer-L at buffering plasmid copy number variability (Fig.3c). Subsequent experiments were conducted solely with Equalizer-L because it was the most effective of the three circuit variants at buffering gene dosage. Henceforth, all experiments with Equalizer-L were performed at the doxycycline concentration producing the lowest cell-to-cell variation (1 ng/ml), unless otherwise noted. [0139] To evaluate whether Equalizer-L’s gene dosage compensation circuitry is functional in multiple cell types, Equalizer-L were tested in multiple commonly used mammalian cell lines derived from different species.
  • Equalizer-L achieved lower cell-to-cell variability in all the cell types tested, including Neuro2A, a line of mouse neuroblasts; CHO-K1, a line of Chinese hamster ovarian cells; COS-7, a line of African green monkey kidney cells; and HeLa, a line of human cervical adenocarcinoma cells (Fig.3d).
  • Neuro2A a line of mouse neuroblasts
  • CHO-K1 a line of Chinese hamster ovarian cells
  • COS-7 a line of African green monkey kidney cells
  • HeLa a line of human cervical adenocarcinoma cells
  • the mean gene-dosage reporter values did not increase linearly with the plasmid dose, leading to a smaller range of gene-dosage reporter values (Fig.13a).
  • the mean circuit output level of Equalizer-L was intermediate between those of the PGK and CMV promoters for 5 of the 6 plasmid doses (Fig.13b). Therefore, the lower output variability produced by Equalizer-L is not simply due to its weaker expression compared with the CMV promoter.
  • the mean values and the overall distribution of gene-dosage reporter levels were similar between all three circuits, demonstrating that the results were not due to differences in transfection efficiency or expression capacity (Fig.14a).
  • the single-cell data was pooled from experiments with each plasmid dose and quantified the mean circuit output of each 5-percentile bin of gene-dosage reporter values.
  • Equalizer-L circuit output increased ⁇ 4-fold compared with ⁇ 90- fold for PGK and ⁇ 50-fold for CMV (Fig.14b).
  • Equalizer-L As an effective gene dosage compensation circuit, it was experimentally confirmed that it outperforms the standalone NF and IFF subcircuits (Fig. 4a, Figs.15-16), as originally predicted (Fig.2, Figs.8 & 10). In this study, both the Equalizer-L and the NF circuit were induced with doxycycline at the concentration producing the lowest cell- to-cell variation (1 and 10 ng/ml for Equalizer-L and NF, respectively; Fig.4b).
  • Equalizer-L was effective at buffering copy number variation across the entire range, although with reduced potency at very low ( ⁇ 5) and very high (> ⁇ 500) plasmid copy numbers (Fig.4d, Fig.17).
  • the NF circuit was limited by poor dosage compensation at both low and high plasmid copy numbers, while the IFF’s gene dosage compensation was predominantly impaired at high plasmid copy numbers.
  • the shape of the predicted NF circuit output is different from that shown above (Fig. 2d, Leaky NF) due to a difference in inducer concentration.
  • Fig.4b was simulated with the inducer concentration producing the lowest cell-to-cell output variability of the (leaky) NF circuit (10 ng/mL), while Fig.2d was generated using a doxycycline concentration optimized for the ideal NF circuit (1 ng/mL).
  • protein expression per plasmid is inversely proportional to the plasmid copy number. In log-log plots, this ideal scaling corresponds to a straight line with a slope parallel to the dotted lines depicted in Fig.4e-4g.
  • Equalizer-L compensated for gene dosage at or near this theoretical ideal across a wider range of plasmid copy numbers than the NF and IFF circuits (Fig.4e).
  • Equalizer-L subcircuit was responsible for gene dosage compensation in different plasmid copy number regimes.
  • dosage compensation was primarily provided by the IFF subcircuit of Equalizer-L (Figure 4f), with negligible contributions from the NF subcircuit ( Figure 4g).
  • the IFF and NF loops acted synergistically to provide overall dosage compensation close to the theoretical ideal.
  • Equalizer-L uses miRNAs solely in its IFF subcircuit
  • HYB utilizes miRNAs to regulate both its NF and IFF subcircuits.
  • the implementation of NF differed between the two circuits.
  • Equalizer-L NF loop
  • TetR directly represses the expression of its own gene and the circuit output.
  • HYB NF loop is mediated 239 by miRNA-based repression of a transactivator that increases the expression of the output protein and the miRNA itself.
  • the HYB circuit also includes a coherent feedforward loop since both the circuit output and its transactivator are encoded on the same plasmid.
  • the Equalizer-L plasmid neither encodes a transactivator nor incorporates a coherent feedforward loop. [0151] The differences between these two circuits presented a unique opportunity to evaluate how gene dosage performance could be affected by circuit design choices. It was noticed that the two systems used different fluorescent protein reporters, thereby complicating their comparison. Therefore, the Equalizer-L and HYB plasmids were modified to express the same reporter: the red fluorescent protein mScarlet-I [38] as the reporter of circuit output, and the yellow fluorescent protein mCitrine [39] as the reporter of plasmid dosage. These fluorescent proteins were chosed because of their monomericity, high brightness, and fast maturation time [38–42].
  • doxycycline was not used in subsequent experiments with HYB and OLP.
  • HYB also produced the highest circuit expression when no inducer was added, as expected for a system that is repressed by doxycycline (Fig.5b).
  • Fig.5e expression from Equalizer-L and CMV was largely bimodal and more steeply dependent on the plasmid dosage (Fig.5e). Increasing the plasmid dose increased the proportion of cells in the high-expression peak. The distributions of gene-dosage reporters were not bimodal and, therefore, could not explain the bimodality of the HYB and OLP circuit output distributions (Fig.20a-20b).
  • the observed bimodality may have occurred because the tetracycline transactivator (tTA) is encoded on the same plasmid as its cognate promoter (TRE). Since the TRE promoter used in both OLP and HYB is highly sensitive to transactivator level (Hill coefficient ⁇ 3.2 [32]), a modest change in plasmid concentration could enable cells to cross the threshold necessary for TRE activation. [0154] Different plasmid doses were transfected and expression heterogeneity was quantified. Equalizer-L reduced cell-to-cell variability to similar levels as previously observed when using EGFP as the circuit output reporter (Figs.5f & 3). However, HYB produced similar expression variability as OLP.
  • the resulting mean circuit output levels were quantified of populations of cells as a function of mean gene-dosage reporter levels.
  • Equalizer-L showed excellent gene compensation, with only ⁇ 1.2-fold increase in the mean circuit output in response to a ⁇ 60-fold increase in apparent gene dosage (Fig.5g).
  • HYB mean output levels remained nearly proportional to the change in gene dosage (Fig.5h).
  • Gene dosage compensation was quantified at the single-cell level.
  • HYB produced a ⁇ 180-fold change in expression in response to a ⁇ 100-fold increase in plasmid dosage, lower than the 320-fold change observed with OLP (Fig.20c).
  • Equalizer-L increased only by ⁇ 3-fold.
  • CMV increased by ⁇ 14-fold, producing a non-linear response to gene-dosage reporter values.
  • Equalizer-L provides superior dosage compensation than HYB.
  • the inventors reanalyzed results considering only higher-expressing cells from experiments with HYB or OLP. Similar results were obtained as above for both population and single-cell assays (Fig.20d-20f).
  • Fano factors are not easily interpretable when comparing distributions with different means (see “Comparing the coefficient of variation and the Fano factor as measures for quantifying cell-to-cell variability in the experiments” in Example 8).
  • the PGK promoter produced a lower Fano Factor than the CMV promoter (Fig.20i) despite producing larger CV values in the evaluations of cell- to-cell variability (Fig.3f) and a nearly linear dependence on gene dosage (Figure 3g).
  • the inventors also replicated the finding that HYB produces a flatter curve than OLP when these circuits are evaluated by plotting unnormalized circuit output values on a linear axis [15] (Fig. 20j, left).
  • EXAMPLE 5 A REPLICATING VARIANT OF EQUALIZER-L ENABLES SIMPLE, RAPID, AND VERSATILE DEVELOPMENT OF EXTRACHROMOSOMAL CELL LINES WITH LOW CELL-TO-CELL EXPRESSION VARIABILITY [0157] Transient transfection with most expression plasmids is only suitable for experiments lasting up to a few days: expression levels and the proportion of expressing cells peak on day 2 or 3 post-transfection and are substantially reduced by days 5 and 6 [43]. However, some plasmids – called episomes – can replicate in mammalian cells. [0158] Episomes enable persistent gene expression and are compatible with many cell types [44].
  • Plasmid replication depends solely on two viral sequences: an origin of replication called oriP and the oriP-binding nuclear protein EBNA-1 [45,47].
  • oriP-bound EBNA-1 also tethers plasmids to chromosomes, both to prevent plasmid 311 loss during mitosis [48] and to promote replication [49].
  • An Equalizer episome was constructed by subcloning Equalizer-L and the gene-dosage reporter onto a plasmid with oriP and EBNA-1 (Fig.22). [0160] Next, the ability was evaluated of the Equalizer-L episome to maintain constant gene expression and low cell-to-cell expression variability for multiple weeks.
  • the inventors transfected episomes in HEK293 cells and grew the cells for two months.
  • the inventors quantified the fluorescence of individual cells every 1-2 weeks using flow cytometry.
  • the boundaries between expressing and nonexpressing cells were defined using untransfected cells and control cultures expressing a single fluorescent protein (Fig.23). Representative images were taken of the cells under fluorescence microscopy (Fig.24). In the absence of selection, plasmid loss is reported to be between 2 and 5% per generation [45].
  • the episome expresses a hygromycin B resistance gene, and the emergence of plasmid-free cells were prevented by using growth media with antibiotics starting 1 day after transfection.
  • GFP+ RFP- cells may result from imperfect detectability of the RFP (mCherry) at lower plasmid concentrations, from silencing of the EF1-promoter driving the gene-dosage reporter [50] or, less likely, from genomic instability [51].
  • the fraction of GFP+ RFP- cells was particularly high in the CMV episome culture, where they accounted for 32-71%of GFP+ cells. It was considered that the strong CMV promoter created stronger selective pressure for lower plasmid concentrations, reduced available cellular machinery available for expressing the gene-dosage reporter, or both.
  • Equalizers that buffer circuit output from variation in plasmid copy number.
  • Cell-to-cell expression variability with Equalizers is equivalent to that observed in cell lines that harbor chromosomally integrated reporters and are thus not subject to plasmid copy number variation (Figs.3c, 6a).
  • the HYB plasmid [15] did not reduce overall cell-to-cell variability (Fig.5f, Fig.20f & 20g).
  • a contributing factor may be the dependence of expression on a transactivator located on the same plasmid.
  • the resulting coherent feedforward loop is expected to amplify the existing dependence of gene expression on copy number.
  • the mean circuit output from cells with the OLP plasmid increased faster than the change in apparent gene dosage 383 (Fig.5h).
  • a second factor may be that HYB and OLP express their circuit components using two promoters, a configuration that is predicted to increase intrinsic noise [58,59].
  • a variant of the Equalizer-L where TetR, the miRNA, and the reporter gene are expressed from separate promoters produced higher cell-to-cell variation despite deterministic simulations predicting identical gene-dosage capacity (Fig.21).
  • additional cell-to-cell variation may have been caused by plasmid replication: the inventors found that the SV40 promoter encoded by HYB and OLP also includes the SV40 origin of replication. This origin is thought to be mediate plasmid replication uncoupled from the cell cycle [44] in HEK293T – the cell line used here [60] and in the original report of HYB [15].
  • the vectors used for transient transfection of Equalizer-L or CMV are not replication-competent, and those used for stable transfection can replicate synchronously with the cell cycle [45].
  • Further engineering of dosage-compensation systems may benefit from studying natural biological systems. In these systems, gene dosage or concentration changes can occur due to gene loss or duplication, chromosome replication during mitosis, and changes in volume during cell growth, etc. Compensation motifs have been postulated or demonstrated to buffer gene dosage [17–20,61,62]. These motifs could be repurposed into the next generation of gene dosage compensation circuits.
  • Plasmids used in this study are available from Addgene (169367, 169731-169735, 169737-169748, 170041) and their sequences are available from GenBank (MW962296-MW962297, MW987521-MW987537).
  • GenBank MW962296-MW962297, MW987521-MW987537.
  • pDN-D2ir mCherry P2A TetR:EGFP was obtained from D. Nevozhay & G. Bala ⁇ zsi and was used to amplify tetR and its cognate tetO2 binding site.
  • pTRE-Tight-BI-DsRed-miR-FF3/tgt-FF3-AmCyan-FF3 [14] were used to amplify miR-FF3 and their binding sites.
  • miR-FF4 was cloned with miR-FF3 as template and using two long primers 5’- ACATCTGTGGCTTCACTATTTAATTAAAGACTTCAAGCGGCGCTCACTGTCAACAGC AC-3’ (SEQ ID NO:5) and 5’- TGAAGCCACAGATGTATTTAATTAAAGACTTCAAGCGGTGCCTACTGCCTCGGAGAA TT-3’ (SEQ ID NO:6) that modified the core sense/antisense sequence from miR-FF3 to miR- FF4 [34].
  • pCEP4-CXCR4 was obtained from Addgene (Plasmid #98944) and was used to subclone the episome plasmids.
  • HYB (pGLM127) and OLP (pGLM130) plasmids were previously described [15] and were obtained from Dr. M. Khammash.
  • the miR-FF4 used in HYB and OLP circuits had several nucleotide differences to the miR-FF4 [34] which was used a as reference to build the circuit plasmids.
  • the mutations were c.1T>A;4A>T;5G>A;35C>T.
  • Some of the unregulated promoter constructs have different 5’ and 3’ UTRs. These differences have minimal impact on cell-to-cell variation.
  • Schematics of plasmid constructs are in Figs.11 & 22 and the entire list of plasmids used in this study is in Table 4.
  • HEK293 The Flp-InTM 293 (RRID:CVCL U421, Thermo Fisher Scientific) cell line was primarily used in the study. In the text, this cell line was called as HEK293 for simplicity.
  • Other mammalian cell lines used in this study were HEK293A (RRID:CVCL 6910, Thermo Fisher Scientific), CHO-K1 (CCL-61, ATCC), HeLa cells (CCL-2, ATCC), HEK293T (CRL-3216 ATCC), COS-7 (CRL-1651, ATCC), and N2A (CCL-131, ATCC). These cells lines were free of mycoplasma contamination.
  • CHO-K1 cells All the cell lines, except CHO-K1 cells, were maintained in high- glucose Dulbecco’s Modified Eagle Medium (DMEM, D1145, Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS, F2442, Sigma-Aldrich), 2 mM glutamine (G7513, Sigma- Aldrich), and 100 unit/mL penicillin-streptomycin (P4333, Sigma-Aldrich) at 37 °C in air with 5% CO 2 .
  • DMEM Modified Eagle Medium
  • FBS fetal bovine serum
  • 2 mM glutamine G7513, Sigma- Aldrich
  • P4333 penicillin-streptomycin
  • HEK293 cells were plated in a 6-well plate for a confluence of 70% one hour before transfection. Cells were then co-transfected (using 6:1 mass ratio, respectively) with pOG44 plasmid (V600520, Thermo Fisher Scientific) and an unregulated CMV expression plasmid encoding EGFP and having FRT sites flanking the expression cassette.
  • Opti-MEMTM (11058021, Thermo Fisher Scientific) and 13.5 ⁇ L of FuGene HD (E2311, Promega, Madison, WI).24 hours after transfection, fully supplemented medium in each well was replaced to reduce the possible cytotoxicity caused by the transfection reagents. 48 hours after after transfection, medium was removed from each well and replenished with fresh medium containing 100 ng/ ⁇ L hygromycin B (10687010, Thermo Fisher Scientific). Same medium was replaced every 2-3 days until attached colonies could be identified and grew to 70 to 80 % confluency.
  • the plates were coated with 60 ⁇ L per well of 0.1 mg/mL of poly-L-lysine and incubated for an hour. After removing the poly-L-lysine, the wells were washed with 1x Dulbecco’s Phosphate-Buffered Saline (DPBS) without calcium and magnesium (21-031-CV, Corning). 70 ⁇ L of cells in fully supplemented DMEM were then plated in each well to achieve ⁇ 60% confluency. The plates were incubated a 37°C with 5% CO2 air for one to two hours to promote cellular attachment prior to transfection.
  • DPBS Phosphate-Buffered Saline
  • mCherry encoded a fluorescent protein with minimal overlap
  • EGFP reporter fluorescent protein
  • This control plasmid does not contain any TetO binding sites or miRNA targets sites and thus expression of the mCherry is not under control of the Equalizer.
  • the mCherry expression cassette was cloned into the circuit plasmids.
  • circuit plasmid with the onboard mCherry expression cassette and 50 ng of empty vector plasmid (that did not encode any genes) were used per well.
  • the inventors used 1 to 200 ng of circuit plasmids. Appropriate amount of empty vector plasmid was added so that the total transfecting plasmid amount was 200 ng per well.
  • plasmid DNA was mixed with Fugene (with 100 ng to 0.3 ⁇ L ratio) in 12.5 ⁇ L Opti-MEM. After incubating the mixture at room temperature for 6 to 8 minutes, 27 ⁇ L per well of fully supplemented DMEM was added.
  • a transfection mixture was prepared by mixing 2000 ng of episomal plasmid (Equalizer or unregulated promoter) with 50 ⁇ L of Opti-MEM. Then, 6 ⁇ L of Fugene was added to the mixture and incubated at room temperature for 6 to 8 minutes. After the incubation, the transfection mixture was added to the plated cells. Twenty four hours after transfection, the transfected cells on the 6-well plate were detached and replated on two 96-well plates: one for imaging and another for flow cytometry. For the imaging plate, cells were plated at a confluency of 20 to 30% and for the flow cytometry plate, cells were plated at a confluency of 40 to 50%.
  • the cells in the 6-well plate were also passaged to another 6-well plate at a confluency of 30% to maintain the episome cell cultures.
  • the episome cells cultures were grown and maintained as described above for the entire duration of the experiment. Cells were passaged twice per week and during every passage fresh hygromycin B (50 ng/ ⁇ L) was replenished to select for and maintain the cells transfected with the episomal plasmids. Note that the epsisomal plasmids express a hygromcyin B resistance gene (hph).
  • hph hygromcyin B resistance gene
  • Detached cells were resuspended in 1x DPBS without calcium and magnesium and deposited into 96-well deep well plates. Attune NxT Acoustic Focusing Cytometer with the Autosampler (ThermoFisher Scientific) was used to measure the fluorescence of individual cells.
  • the following lasers and emission filters were used: for mCerulean, a 405-nm laser and a 440/50-nm emission filter; for EGFP 601 and mCitrine, a 488-nm laser and a 530/30-nm emission filter; for mCherry, DsRed-Express, and mScarlet-I a 561- 602 nm laser and a 620/15-nm emission filter. For each sample, 5000 to 10,000 cells were typically measured. Cells 603 expressing one type of FP (single-FP controls) were prepared to compensate for bleed-through between the color 604 channels.
  • Emission light was collected by a scientific CMOS camera (Flash4 v2+, Hamamatsu) after passing through a Multiband Filter (Spectra-X, 77074159).
  • Data processing [0187] Flow cytometry data was analyzed using FlowJo (version 10.6.1, BD). Forward and side scatters were used to gate singlet cells. Among the singlet cells, only the transfected cells were used for analysis unless mentioned otherwise. Circuit output levels of individual cells were evaluated using the fluorescence levels of reporter fluorescent proteins (EGFP or mScarlet- I) expressed by the circuit plasmid.
  • EGFP reporter fluorescent proteins
  • mCherry or mCitrine expressed from an independent expression cassette was used to determine transfected cells by gating for cells that show higher mCherry or mCitrine fluorescence than the baseline non-transfected cells.
  • mCherry and mCitrine fluorescence levels of individual cells were also used to estimate the active-plasmid copy number (i.e.gene dosage) inside the transfected cells.
  • the CV values of EGFP or mScarlet-I fluorescence distributions were used to measure the cell-to-cell variability in circuit output. CV values were calculated by dividing the SDs of fluorescence distribution of cell populations by mean fluorescence value of the population.
  • MATLAB version r2019b, MathWorks
  • Fig.12 quantitative and qualitative (e.g. Fig.6b)assessment of images.
  • image segmentation was conducted using ilastik [65] to distinguish the cells from the background. Smoothing and background subtraction were applied on the raw images. Segmentation masks were then used to evaluate the fluorescent protein intensity of individual cells. The mean, standard deviation, and CV values of fluorescent protein intensities of segmented cells were calculated. Each field-of- view had 200 - 1000 cells. Fields-of-view with saturated pixels were removed from analysis.
  • image segmentation was first conducted, as mentioned above, to obtain the means and standard deviations of fluorescence intensities of cells in the fields-of-view.
  • the inventors then systematically set the lookup table (LUT) boundary for each field-of-view so that the boundary was centered around the mean fluorescence intensity of the cells in the field-of-view. More specifically, for each field-of-view, the inventors set the lower bound to zero and the upper bound to mean fluorescence value plus three times the standard deviation value.
  • the masks shown in the figures were generated by thresholding. Note that these masks were not used for image segmentation, but simply to visualize regions of the images that corresponded to cells.
  • MATLAB and Prism were used to conduct basic calculations, generate plots, and conduct statistical analysis.
  • Stochkit2 [67] was used for stochastic simulation (see “Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L”). Model reactions and assumptions are listed in Example 8 Simulation parameters are included in Tables 1-3. Simulations in Fig.2 used 1ng/mL doxycycline for the ideal NF circuit, the leaky NF circuit, and Equalizer. Simulations in Fig.4d-g used 1ng/mL doxycycline for the 10 ng/mL for the NF circuit, and 1 ng/mL for Equalizer-L. Fig.2b & 2f used miRNA dissociation rate constant of 0.3 second-1 for the IFF circuit and Equalizer as an in silico proof of concept, before Equalizer was experimentally tested.
  • each Simbiology circuit model was ran to steady state at individual copy number, and the log sensitivity at each copy number was calculated using numerical differentiation with second-order schemes (keeping values of DNA copy number, C N , integer).
  • C N DNA copy number
  • a second-order forward finite difference was used to approximate the local log sensitivity ([POI] denotes steady-state protein concentration): [0195]
  • All computational and experimental data regarding NF topology shown in the study refer to the Equalizer (-L, -M, or -H) without the miRNA, its flanking splice sites and its target(s).
  • different inducer concentrations were supplied in the initial conditions to identify the optimal inducer concentration that produces the lowest log sensitivity.
  • leakiness of each inducible construct can be conceptualized as the ratio of the expression level when no inducer was added to the maximum expression achieved by adding a saturated amount of inducers, it depends on the number of plasmids in a cell, because cells with different plasmid copy number will have different TetR concentrations without inducers, leading to different basal transcription rate per plasmid.
  • Leakiness is considered in the modeling as the leakage parameter, as described in the end of “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8. See “Estimating the distribution of plasmid copy numbers in transfected cells” in Example 8 for the estimation of leakage value.
  • the mean expression level of ten thousand cells with a fitted plasmid copy number distribution was simulated with the Equalizer model (topology 4 in “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8) and the NF model (topology 2 in “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8) across doxycycline concentration of 0, 1, 5, 10, 50, 100 ng/mL.
  • MATLAB’s fmin search function was used to find the miRNA-target affinity that produces smallest mean squared error of the simulated mean expression ratio of the Equalizer model and the NF model compared with experimental data (see “Estimating miRNA affinity” in Example 8 for details).
  • Data Availability [0200] Annotated plasmid sequences are available from GenBank (Accession numbers: MW962296-MW962297, MW987521-MW987537) and Addgene (#169367, 169731-169735, 169737-169748, 170041).
  • Equalizer circuit that separately expresses the circuit components using multiple promoters (Fig.29e). Parts of the models were adapted from Nevozhay et al. [1] and Bleris et al. [2]. All reactions are listed below and follow mass action kinetics unless otherwise stated; the reactions that do not follow the mass action kinetics are underlined. The kinetics of these reactions are described by Equations 5-6 elsewhere herein. [0205] The parameters for mass action kinetics are listed in Table 1.
  • TetR In the presence of saturating amounts of the doxycycline inducer (dox), TetR is unable to bind to (tetO2) and gene expression can proceed. [0210]
  • dox doxycycline inducer
  • TetR-based NF model [1] by adding plasmid copy number (CN) as an additional parameter.
  • the inventors used the following set of differential equations based on mass action kinetics: [0212] where CN is the plasmid copy number, ”[ ]” notation denotes intracellular concentration, and ”[ ]ext” indicates extracellular concentration.
  • Dox is the inducer molecule (i.e. doxycycline).
  • a is the production rate per plasmid.
  • F([TetR]) is the inhibition function with intracellular TetR concentration as the independent variable. This inhibition function is shown at the end of “Model description of four key topologies utilized to predict gene dosage compensation” above.
  • b is the association rate of TetR and Dox
  • d is the protein decay rate
  • c is the diffusion constant of Dox
  • f is the inducer decay rate.
  • the decay terms d [TetR] and f ⁇ [Dox] are small compared with the association term b ⁇ [TetR] ⁇ [Dox] [1].
  • plasmid copy number distribution is specific to experimental conditions such as transfecting cell type, transfection reagent, amount of transfecting plasmid. Therefore, this parameter was estimated based on the experimental results.
  • the plasmid copy number distribution was estimated using the EGFP expression distribution of HEK293 cells expressing EGFP from the transfected unregulated promoter plasmid and the NF circuit plasmid (see Methods for details). For the unregulated promoter plasmids, expression levels were approximately linearly proportional to the plasmid copy number (Fig.3g).
  • the expression level distribution that can be empirically determined could be a proxy for the plasmid copy number distribution.
  • the inventors defined plasmid copy number as ”active” plasmid copy number that was actively transcribed.
  • CDF cumulative distribution function
  • Fig.31 Depicted in Fig.31 is the CDF histogram of EGFP fluorescence of transfected cells that were analyzed by flow cytometry.
  • the solid curvy line in Figure 31 is the fitted CDF curve of a gamma distribution, which has a shape parameter k of 0.57 and a scale parameter ⁇ of 1.7x10 5 . Therefore, because the EGFP expression distribution fitted a gamma distribution, one could conclude that the underlying plasmid copy number should also have a gamma distribution, assuming the EGFP expression random variable is the plasmid copy number random variable scaled by some positive constant factor.
  • the shape parameter k of the plasmid copy number distribution was determined. To this end, the flow-cytometry-acquired single-cell EGFP expression distributions were used of HEK293 cells transfected with the unregulated CMV plasmid. As a property of the gamma distribution, the shape parameter k remains constant if the random variable is scaled by some positive constant. Thus one can use any unregulated plasmid to estimate the shape parameter k of the plasmid copy number distribution.
  • the unregulated PGK circuit had a similar fit with an estimated k value of 0.56. These data further support the assumption that expression levels scale linearly with plasmid copy number and suggests only minimal non-linearity.
  • Fitting the scale parameter ⁇ of the plasmid copy number distribution and the leakage of the NF circuit [0223] Having determined the shape parameter k, one can now use the experimentally determined mean expression of the NF circuit at different doxycycline concentrations to estimate the scale parameter ⁇ of the plasmid copy number distribution and the leakage parameter. In contrast to determining k, the distribution of single-cell expression from unregulated circuits does not constrain ⁇ .
  • the inventors scanned across different leakage and scale parameter ⁇ combinations to minimize the mean squared error (MSE) of the simulated mean expression of the NF circuit at 8 different inducer concentrations (0, 1, 5, 10, 50, 100, 500, 1000 ng/mL) compared with experimental data. All experimental data were normalized to the lowest expression level so that the simulated expression vs. inducer curve demonstrated a similar trend to what is experimentally observed.
  • MSE mean squared error
  • the MSE is closest to zero between a scale parameter ⁇ of 100 to 120. ⁇ of 120 is used for the simulations in this work (Fig.31d). See “Predicting cell-to-cell variability” for how sensitive the simulation results are to the choice of ⁇ . [0225] See: Example 8 CopyNumberDistScaleParameter 1dScan.m. Using the defined shape and scale parameters, the plasmid copy number distribution can be used to predict the cell- to-cell variability caused by plasmid copy number variability for any given circuit model. [0226] Estimating miRNA affinity [0227] miRNA is an integral part of several of the circuits used in this study, thus to properly model and predict protein of interest cell-to-cell variation for these circuits, miRNA affinity was estimated.
  • miRNA affinity to its target depends on the specific sequences of the miRNA and target, the number of target sites on the transcript, and location of target sites on the transcript [6]. From the modeling standpoint, miRNA affinity can be described by the ratio of the dissociation rate constant and the association rate constant. Previous studies found that miRNA affinities are modulated by the dissociation rate constant [7, 8]. Thus, the inventors fixed the association rate constant (kf RISC complex formation in the model) and used the change in dissociation rate constant (kf RISC complex deformation in the model) to model the change in miRNA affinity to its target.
  • MATLAB’s fminsearch function was used to fit the values of dissociation rate constant by minimizing the mean squared difference between the simulated and experimental ratio of the mean expression of 10 thousand cells expressing the Equalizer circuit and the NF circuit at 6 different inducer concentrations (0, 1, 5, 10, 50, 100 ng/mL). Equalizer and the NF circuits were included in this analysis so that direct comparisons between a circuit with and without miRNA, respectively, can be done.
  • the plasmid copy number distribution was estimated from the distribution obtained from “Estimating the distribution of plasmid copy numbers in transfected cells”. To test the simulation accuracy, different initial guesses of the dissociation rate constant of miRNA to its target were used and converged to the same estimated dissociation rate constant of 0.303 second -1 .
  • the fully specified models for Equalizer and the IFF circuit can be used to predict cell-to-cell variability of cells expressing the circuits.
  • Predicting cell-to-cell variability [0229] The modeling can predict the cell-to-cell variability originating from plasmid copy number variability (extrinsic noise). However, the experimentally observed cell-to-cell variability is caused by intrinsic noises and extrinsic noise.
  • plasmid copy number variability is the major source of extrinsic noise, and extrinsic noise from other sources is negligible in comparison
  • intrinsic noise is constant across Equalizer, IFF circuit, NF circuit, and unregulated CMV circuit. Based on the second assumption, the intrinsic noise can then be approximated as the experimentally observed cell-to-cell variability of the CMV cell line, which integrated the unregulated CMV circuit to the genome and thus, the noise should be mostly intrinsic noise since it has minimal DNA copy number variability.
  • the total noise coefficient of variation
  • the initial condition for all topologies assumes 10 copies of the gene in the off-state, the inducer at a steady-state determined by the influx rate and the degradation rate shown in Table 1 with an extracellular inducer of 0.5 ng/mL, and RISC molecule count is set to the value mentioned above (1.7e5 molecules/cell); the initial molecule count for the rest of the species is zero.
  • the inventors used the kinetic parameters listed in Table 1 for the Equalizer circuit (Topology 4 in Fig.29). The kinetic parameters were modified for the multiple promoter Equalizer circuit (Topology 5 in Fig.29) so that the steady- state mRNA amount and steady-state POI amount remains the same as the Equalizer circuit.
  • Stochastic simulation set 1 original parameters Parameter Equalizer Multiple promoter Equalizer k_transcription (1/second) 4.67e-2 3.70e-2 (same for all three genes) k_translation (1/second) 3.33e-4 3.33e-4 (same for all three genes) k_on (1/second) 3.10e-4 3.10e-4 (same for all three genes) k_off (1/second) 4.18e-4 4.18e-4 (same for all three genes) [0242]
  • the second set of stochastic simulations test how each topology reacts to amplified translation bursting, for which the steady-state mRNA amount in each topology is reduced 50 fold compared with set 1 while maintaining steady-state POI and TetR amount with the following modified parameters: [0243] Stochastic simulation set 2: amplified translational bursting Parameter Equalizer Multiple promoter Equalizer k_transcription (1/second) 5.10e
  • CV is a dimensionless quantity that is invariant to proportional scaling.
  • ⁇ scaled is: [0256] [0257]
  • standard deviation of the scaled distribution ⁇ scaled is: [0258]
  • the CV of the scaled distribution, CVscaled is: [0260] [0261]
  • the Fano factor changes when the population distribution is scaled with a factor ⁇ : [0262] [0263]
  • the experiments conducted in this work produce quantities in arbitrary units where the signal is proportional to the molecule number with unknown proportionality.
  • the Fano factor has major disadvantages. Not only would the application of the Fano factor metric have unclear units, but its exact value would scale with the changes in the unknown proportionality coefficient as Eq. (26). As a result, the Fano factor does not stay invariant under proportional scaling, unlike CV. For example, the Fano factor cannot be compared between experiments that employ measurements of molecule count (e.g., flow cytometry, microscopy) with different devices, cameras, or amplification gain settings as these are expected to affect the proportionality coefficient. Additionally, the Fano factor compares the spread of probability distribution relative to a Poisson distribution with the same mean. Thus, the Fano factor metric struggles to deal with comparisons between conditions when the mean value of each condition is very different (Fig.20i).
  • molecule count e.g., flow cytometry, microscopy
  • improving mRNA stability increases Equalizer circuit expression level and gene dosage compensation ability.
  • the cell-to-cell variation (Fig.35a) and expression level (Fig.35b) change when doxycycline concentration varies.
  • the doxycycline concentration that gives the lowest cell-to-cell variation for each condition may be chosen for Fig.35c, Fig.35d, and Fig.35e.
  • Reducing the mRNA degradation rate (Fig.35c) decreases the cell-to-cell variation and (Fig.35d) increases the expression level.
  • a efficacious performance is achieved when the mRNA degradation rate reaches the minimum (Fig.35e).
  • the system utilizes components from the Lac operon (e.g., LacO operator sites) instead of other repressors, such as from the Tet operon (e.g., TetO operator sites).
  • Lac operon e.g., LacO operator sites
  • Tet operon e.g., TetO operator sites
  • lactose when lactose becomes available, it is converted into allolactose, which inhibits the DNA binding ability of LacI at the LacO2 site, thereby permitting gene expression.
  • Fig.34a shows the cell-to-cell variability as a function of IPTG concentration for both the NF-only circuit and the NF+IFF (Equalizer) design.
  • IPTG concentration is demonstrated for the LacI-based NF-only circuit in Fig.34b, and the expression (circuit output) vs. IPTG concentration is provided for the LacI-based Equalizer circuit in Fig.34c. See below for representative sequences of LacI-based Equalizers.
  • Table 3 Gamma-distributed plasmid copy number parameters [0300] Parameter Value Unit Reference shape parameter k 0.57 dimensionless estimated scale parameter 0 120 dimensionless estimated [0301] See “Estimating the distribution of plasmid copy numbers in transfected cells” for methods used for the estimation of k and ⁇ . [0302] Table 4: Examples of Plasmids used in this disclosure [0303] Name Description Usage Schematic Addgene; GenBamk CMV-tetO2 promoter- Equalizer-H bGlob intron-tetR-P2A- eGFP-MiR( 169367; plasmid FF3)-target- Fig. 3a-c Fig.
  • mScarlet a bright monomeric red fluorescent protein for cellular imaging. Nat Methods 14, 53–56 (2017). [1178] 39. Griesbeck, O., Baird, G. S., Campbell, R. E., Zacharias, D. A. & Tsien, R. Y. Reducing the environmental sensitivity of yellow fluorescent protein. mechanism and applications. J Biol Chem 276, 29188–29194 (2001). [1179] 40. Balleza, E., Kim, J. M. & Cluzel, P. Systematic characterization of maturation time of fluorescent proteins in living cells. Nat Methods 15, 47–51 (2016). [1180] 41. Cranfill, P. J. et al. Quantitative assessment of fluorescent proteins.
  • IRES- dependent second gene expression 837 is significantly lower than cap-dependent first gene expression in a bicistronic vector. Mol Ther 1, 376–382838 (2000). [1201] 62. Frei, T. et al. Characterization and mitigation of gene expression burden in mammalian cells. Nat Commun 11, 4641 (2020). [1202] 63. Man-Sai, A., Francisco, S.-C. & Mora-Rodriguez, R. A biocomputational platform for the automated construction 842 of large-scale mathematical models of mirna- transcription factor networks for studies on gene dosage compen- 843 sation. In 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), 1–7 (IEEE, 2016).

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Abstract

Embodiments of the disclosure include systems, compositions, and methods for regulating gene expression, including in a manner that allows for enhanced control of copy number variation at both the population and single-cell level compared to known systems. The disclosure concerns systems that utilize expression constructs having either or both a transcriptional negative feedback loop (NF) component and a post-transcriptional incoherent feedforward (IFF) control that in combination regulate expression of one or more genes of interest. In specific embodiments, both loops are present on the same polynucleotide and are regulated by the same regulatory sequence. In specific embodiments, the NF and the IFF are present on the same replicating plasmid to generate extrachromosomal cell lines having as low cell-to-cell variation in expression as cell lines where a gene of interest is chromosomally integrated.

Description

A SYNTHETIC CIRCUIT FOR BUFFERING GENE DOSAGE VARIATION BETWEEN INDIVIDUAL MAMMALIAN CELLS CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63/187704, filed May 12, 2021, which is incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT [0002] This invention was made with Government support under Grants EB027145, NS113294, and NS118288, awarded by the National Institutes of Health, and 1935265 and 1707359 awarded by the National Science Foundation. The Government has certain rights in this invention. TECHNICAL FIELD [0003] Embodiments of the disclosure include at least the fields of gene expression, recombinant technology, research, and medicine. BACKGROUND [0004] Expressing genes of interest from synthetic cassettes is critical for studying natural proteins, producing reagents of commercial interest, and constructing synthetic biological circuits. Uniform expression among individual cells is needed when expressing genes whose properties depend on their concentration [1]. For example, many natural and engineered proteins can be nonfunctional or undetectable at low concentrations, and aggregate, mislocalize, or display aberrant function at high expression levels [2–7]. Expression homogeneity would also facilitate the development of synthetic biological circuits with predictable behavior at the single- cell level [8,9]. [0005] An important challenge to achieving uniform expression levels is the large variability in copy numbers observed after transfection of plasmids in mammalian cells [10]. While expression from the chromosome is widely used to reduce cell-to-cell variation in gene expression, it has multiple disadvantages compared with expression from plasmids. First, because experiments using plasmids can be conducted as soon as 1 to 3 days after transfection, the functions of new genes or circuits can be rapidly evaluated. In contrast, the creation of new cell lines via chromosomal integration typically takes several weeks because of the need to select stable integrants with the desired expression level. Second, plasmids can be more easily and rapidly deployed across a wide array of cell types. Meanwhile, chromosomal expression requires genomic integration to be repeated and validated for each cell type. The limitations of classical expression methods have motivated the development of plasmid-based gene dosage compensation circuits – synthetic circuits that buffer plasmid copy number variation. In an ideal compensation circuit, the per-plasmid expression rate is inversely proportional to the copy number; the total protein expression thus remains constant (Fig.1). These circuits promise to combine the versatility and convenience of plasmids with the lower cell-to-cell variability of chromosomal expression. A variety of gene dosage compensation circuits have been theorized or tested in bacterial [11] and mammalian cells [12–16]. However, existing mammalian circuits buffer gene dosage variation across a limited range of plasmid copy numbers. Moreover, their ability to reduce cell-to-cell expression variability within a transfected population has not been demonstrated or has been incompletely quantified. [0006] The present disclosure provides solutions to long-felt need in the art of transgene expression. BRIEF SUMMARY [0007] Transgene expression is used by nearly every biotech and pharma company working on biologics. Uniform gene expression from cell to cell is critical because protein function is often dependent on concentration. However, transient plasmid transfections in mammalian cells produce a wide distribution of copy numbers per cell, and consequently, high expression heterogeneity. There is therefore high interest in expression systems that would produce the same level of expression in each cell regardless of the number of plasmids in each particular cell. [0008] The present disclosure concerns systems, methods, and compositions to address such needs in the art. The disclosure regards systems, methods, and compositions that allow controllable variability of copy numbers of vectors (including at least plasmids) and, therefore, enhanced expression homogeneity when compared to known systems. In specific embodiments, the disclosure provides means for buffering plasmid copy number variation to reduce cell to-cell expression variability within a transfected population. [0009] In particular embodiments, the disclosure concerns protein expression circuits that buffer copy number variation at the single-cell level. These circuits couple two different mechanisms that are each inhibitory loops with specific topologies: (1) a transcriptional negative feedback (NF) loop; and (2) post-transcriptional incoherent feedforward control (IFF). In specific embodiments, engineered circuits that utilize both of these mechanisms buffer expression heterogeneity caused by plasmid dosage variation between individual mammalian cells. The identification of such circuits were guided by computational models that provided circuit design and gave mechanistic insight into their underlying efficacy. The circuits can function in multiple cell types and outperform other compensation circuits at the single-cell level. In specific embodiments, when incorporated into replicating plasmids, the system enables long-term gene expression with cell-to-cell variation comparable to chromosomal expression. [0010] In certain embodiments, the system of the disclosure is utilized to generate extrachromosomal cell lines. In specific cases, certain plasmids, episomes, can replicate in mammalian cells and are utilized as part of the circuit system that includes low expression heterogeneity as normally seen using chromosomal expression. [0011] In particular embodiments, there is a gene expression system comprising a negative feedback loop and an incoherent feedforward control for expression of at least one gene of interest, wherein components of the negative feedback loop and incoherent feedforward control are optionally regulated by the same regulatory sequence. In specific cases, the negative feedback loop is a transcriptional negative feedback loop and/or wherein the incoherent feedforward control is a post-transcriptional incoherent feedforward control. The negative feedback loop component may lack regulation by an miRNA, in some cases, and/or the negative feedback loop may comprise a repressor that represses expression of its own gene. In specific embodiments, the system lacks an incoherent feedforward loop component. The system may be further defined as comprising an expression construct comprising at least two components to regulate production of a gene product from the 1, 2, 3, or more genes of interest, said components comprising: [0012] (a) a sequence encoding a repressor that represses expression of itself and the gene of interest, wherein the expression of the repressor sequence is regulated by a cognate operator site to which the repressor may bind, wherein the sequence encoding the repressor and the gene of interest are expressed from different promoters or are on a multicistronic vector or are separated by a ribosome-skipping sequence or an internal ribosome entry site, [0013] (b) one or more sequences encoding a miRNA located anywhere in the overall sequence of the system, optionally including a 5’ untranslated region, a 3’ untranslated region, or within any of the gene of the system; and [0014] (c) sequences including one or more miRNA target sites in the 5’ untranslated region, the 3’ untranslated region, or both. [0015] In specific embodiments of the system, the regulatory sequence that regulates expression of the transcriptional negative feedback loop and post-transcriptional incoherent feedforward control comprises a constitutive, inducible, or tissue-specific promoter. In specific embodiments, the repressor is a tetracycline repressor, a Lac repressor that binds to one or more lacO operator sites, a dCas9, TALEN, or Zinc finger. When the TetR repressor is used, the cognate operator site to which the repressor may bind may be one or more copies of tetO2. The miRNA may or may not be flanked by splicing sites, and examples of miRNA include miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a. [0016] In specific embodiments of the system, the expression construct comprises two non-identical genes of interest, wherein a first gene of interest and a second gene of interest are separated by a ribosome-skipping sequence or an internal ribosome entry site, and wherein said first gene of interest, ribosome-skipping sequence or an internal ribosome entry site, and second gene of interest are downstream from the sequence encoding the repressor and separated from the sequence encoding the repressor by a ribosome-skipping sequence or an internal ribosome entry site. In particular aspects, the (a) sequence encoding the miRNA is downstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites. The (a) sequence encoding the miRNA may be upstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites, or the miRNA target sites are elsewhere in the expression construct. In some cases, the (a) sequence encoding the miRNA is within the sequence encoding the repressor, the ribosome- skipping sequence/ internal ribosome entry site, or the gene of interest flanked by the miRNA target sites. The gene of interest for the system may be a reporter protein, a gene-editing reagent (CRISPR-Cas9 component), a therapeutic protein, an enzyme, an optogenetic reagent, a chemogenetic reagent, or a combination thereof. Examples of reporter proteins include a fluorescent protein (blue, cyan, green, yellow, red, far-red, or infrared fluorescent protein), a fluorescent indicator, a bioluminescent protein, or a bioluminescent indicator. When the system comprises a ribosome-skipping sequence, it may be one or more 2A self-cleaving peptides, such as T2A, P2A, E2A, F2A, or a combination thereof. The expression construct may be on a plasmid or episome, each that may be in a cell, such as a mammalian or yeast cell. In various embodiments, instead of the system being utilized on an extrachromosomal vector, such as a plasmid, it may be integrated into the chromosome and, in specific cases, such integration may yield gene expression levels that is more independent of the integration site chosen. [0017] Embodiments of the disclosure include polynucleotides comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0018] (a) one or more operator sites (e.g., tetO2 or lacO2) that regulate expression of promoter expression; [0019] (b) one or more miRNA target sites; [0020] (c) sequence encoding a repressor; [0021] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0022] (e) one or more genes of interest; [0023] (f) one or more miRNA target sites; and [0024] (g) one or more miRNA-encoding sequences. [0025] In some cases, the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction one or more additional genes of interest. In specific cases, the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction another ribosome-skipping sequence or an internal ribosome entry site and another gene of interest different from the gene in (e). [0026] In any case, the repressor may be a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger. The sequence encoding the miRNA may or may not be flanked by splicing sites. In some cases, the miRNA is located at the 5’ or 3’ end of the construct instead of being flanked by splicing sites. Specific examples of miRNAs include miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a. [0027] In specific embodiments of the polynucleotide, the expression construct comprises one or more regulatory sequences that regulate expression of the repressor, the gene of interest, and, optionally, also the miRNA. The repressor and miRNA may be expressed from different regulatory sequences than the gene of interest. [0028] Embodiments of the disclosure include episomes comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0029] (a) one or more operator sites that regulates expression of a repressor; [0030] (b) one or more miRNA target sites; [0031] (c) sequence encoding a repressor; [0032] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0033] (e) one or more genes of interest; [0034] (f) one or more miRNA target sites; and [0035] (g) one or more miRNA-encoding sequences. [0036] In specific cases, the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction multiple iterations of ribosome-skipping sequence or an internal ribosome entry site with another gene of interest different than the gene in (e). In specific cases, the repressor is a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN or Zinc finger For the episome the sequence encoding the miRNA may or may not be flanked by splicing sites, given that it may be located at the 5’ or 3’ end of the construct. Specific examples of miRNAs include miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR- 21, miR-30a, miR-141, or miR-146a. [0037] In specific embodiments of the polynucleotide, the expression construct comprises one or more regulatory sequences that regulate expression of the repressor, the gene of interest, and, optionally, also the miRNA. The repressor and miRNA may be expressed from different regulatory sequences than the gene of interest. In specific aspects, the ribosome- skipping sequence is at least one 2A self-cleaving peptide, including T2A, P2A, E2A, F2A, or a combination thereof. [0038] Embodiments of the disclosure include plasmids, comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: [0039] (a) one or more operator sites that regulates expression of a repressor; [0040] (b) one or more miRNA target sites; [0041] (c) sequence encoding a repressor; [0042] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0043] (e) one or more genes of interest; [0044] (f) one or more miRNA target sites; and [0045] (g) one or more miRNA-encoding sequences. [0046] In the plasmid, the expression construct between (e) and (f) may or may not further comprise in a 5’ to 3’ direction one or more other ribosome-skipping sequences or an internal ribosome entry sites associated with another gene of interest different from the gene in (e). In the plasmid, the repressor may be a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger. The sequence encoding the miRNA may be flanked by one or more splicing sites. In specific embodiments, the expression construct comprises a regulatory sequence that regulates expression of the repressor, the gene of interest, and the miRNA, including at least the ribosome-skipping sequence is a 2A self-cleaving peptide. [0047] Embodiments of the disclosure include cells, including cell lines, that comprise any system encompassed herein, any polynucleotide encompassed herein, any episome encompassed herein, or any plasmid encompassed herein. The cells may or may not be mammalian cells or yeast cells. In specific cases, the cells are comprised in a suitable cryopreservation medium. Pluralities of the cells are encompassed herein, including compositions that comprise a plurality. [0048] Embodiments of the disclosure encompass methods of regulating expression of at least one gene of interest, comprising the steps of: [0049] (1) providing a system, optionally in a cell or cell-free medium, wherein said system comprises a polynucleotide (plasmid or episome, in at least some cases) comprising an expression construct that comprises in a 5’ to 3’ direction: [0050] (a) one or more operator sites that regulates expression of a repressor; [0051] (b) one or more miRNA target site; [0052] (c) sequence encoding a repressor; [0053] (d) a ribosome-skipping sequence or an internal ribosome entry site; [0054] (e) one or more genes of interest; [0055] (f) one or more miRNA target sites; and [0056] (g) one or more miRNA-encoding sequences, wherein said miRNA-encoding sequence is flanked by splicing sites; [0057] (2) when desired, exposing the polynucleotides to an effective amount of a compound that induces expression from the expression construct by inhibiting the repressor, said expression from the expression construct producing a transcript that comprises sequence encoding the repressor, sequence encoding a gene product from the gene of interest, and the miRNA-encoding sequence, wherein the miRNA recognizes the miRNA target sites on the transcript, thereby resulting in inhibition of production of the gene product. In specific cases where TetR is the repressor, the compound that induces expression is doxycycline or a functionally similar compound (tetracycline and/or minocycline). When doxycycline is used, an effective amount of doxycycline may be about 0-1000 ng/mL. [0058] In specific embodiments, the method further comprises the step of transfecting or transducing the cells with an effective amount of the respective plasmid or episome, and an effective amount of the plasmid or episome for transfecting or transducing may be about 1-200 ng per approximately 24,000 cells. In specific embodiments, there are 1-30,000 total plasmids/nucleus and/or 1-1000 actively transcribing plasmids/nucleus. [0059] In one embodiment, there is a method of reducing gene expression variability between individual cells of a gene of interest, comprising the steps of transfecting or transducing a plurality of cells with a plasmid or episome comprising an expression construct that expresses a single transcript encoding one or more components of a transcriptional negative feedback loop and one or more components of a post-transcriptional incoherent feedforward control for expression of the gene of interest, wherein the components of the transcriptional negative feedback loop and the post-transcriptional incoherent feedforward control are regulated by the same regulatory sequence. In some embodiments, the expression construct is further defined as expressing a single transcript encoding a tetracycline repressor, a gene product from the gene of interest, and a miRNA flanked by splicing sites, wherein following exposure of an effective amount of an inducer to inhibit the repressor, the transcript is expressed and the miRNA is spliced out to inhibit production of the gene product of interest. [0060] The foregoing has outlined rather broadly the features and technical advantages of the present disclosure in order that the detailed description that follows may be better understood. Additional features and advantages will be described hereinafter which form the subject of the claims herein. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present designs. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope as set forth in the appended claims. The novel features which are believed to be characteristic of the designs disclosed herein, both as to the organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS [0061] For a more complete understanding of the present disclosure, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which: [0062] Figures 1a-1b. Gene expression from ideal dosage compensation circuits does not vary with plasmid copy number. (1a) Expression from promoters with no control circuitry (i.e. open-loop circuits) is proportional to the plasmid copy number. (1b) An ideal dosage compensation system uses control mechanisms to tune the per-copy expression rate, thereby maintaining constant expression regardless of copy number. [0063] Figures 2a-2f. Combining incoherent feedforward (IFF) and negative feedback (NF) loops is predicted to widen the copy number range with efficient dosage compensation. (2a) Schematic of the miRNA-based post-transcriptional IFF circuit. (2b) The depletion of free RISC (gray trace) at high plasmid copy numbers abolishes dosage compensation by the IFF circuit (purple trace). (2c) Schematic of the transcriptional NF circuit. (2d) Incomplete repression by TetR results in a minimal transcription rate per plasmid (gray trace) that narrows the compensation range (leaky vs. ideal NF). Simulations were run with a doxycycline concentration that resulted in a wide compensation range of the NF circuit (1 ng/mL for both the ideal and leaky NF loops). (Fig.8). (2e) Schematic of the Equalizer circuit, which combines transcriptional NF (green shaded area) with post-transcriptional IFF (purple shaded area). (2f) The Equalizer has the potential for compensating for a wider range of plasmid copy numbers than NF and IFF circuits alone. Simulated doxycycline concentration, 1 ng/mL. [0064] Figures 3a-3g. Equalizers demonstrate robust gene dosage compensation at the single-cell and population levels. (3a-3b) Circuit output variability (3a) and relative mean circuit output levels (3b) of HEK293 cells transfected with the Equalizer plasmids and cultured under different inducer concentrations. Output levels are relative to that of uninduced Equalizer-L. Mean values ±SEM are shown. n = 8 (Equalizer-L) or 3 (-M/-H) independent transfections. Here and for panels 3c) and (3d), 100 ng circuit plasmids were used per transfection. (3c) Equalizer plasmids produced lower cell-to-cell expression variability than plasmids with unregulated promoters. p < 0.01 for all pairs in Tukey’s tests. Cells transfected with Equalizer-L produced similar variability as cells with a chromosomally-integrated unregulated CMV cassette (CMV cell line); p > 0.99, Tukey’s test. Circuit output values were relative to that of Equalizer-L. Mean ± SEM are shown; some error bars are too small to be seen. n = 3 (unregulated circuits and Equalizer-M & -H) or 8 (Equalizer-L) independent transfections. n = 3 independent cell cultures (CMV cell line). Equalizer-L was induced with 1 ng/mL of doxycycline. (3d) Equalizer-L produced lower cell-to-cell variability than the CMV promoter in five cell lines. The black circles are independent transfections. Equalizer-L was induced with 1.0 ng/mL of doxycycline. Mean ± SEM are shown; some error bars are too small to be seen. n = 6 per circuit. ****, p < 0.0001; Sidak’s test. (3e) Representative output-level histograms. Each histogram was normalized to its peak. For (3e-3g), Equalizer-L was induced with 1 ng/mL of doxycycline. (3f) Equalizer-L produced lower cell-to-cell variability than unregulated promoters at different gene dosage levels. The circles represent independent transfections. n = 36 per circuit (3 per dose and 6 doses per circuit). The dashed lines indicate trend lines (linear for Equalizer-L and CMV; exponential for PGK). The gene-dosage reporter values were normalized to those obtained when transfecting 1 ng of plasmid. (3g) The mean Equalizer-L output is robust to increases in gene dosage. The gene-dosage and circuit output values were normalized to those obtained when transfecting 1 ng of plasmid. The circles and sample sizes are as in panel (3f). The dashed lines indicate trend lines (linear for Equalizer-L and PGK, hyperbolic for CMV). [0065] Figures 4a-4g. Equalizer-L combines NF and IFF circuitry to increase the range of gene dosage compensation (4a) Equalizer-L produced lower cell-to-cell variability than either NF or IFF circuits alone. In this experiment and in simulations (4d-4g), doxycycline was used at the concentration producing the lowest cell-to-cell variability for each circuit (Equalizer-L, 1 ng/mL; NF, 10 ng/mL). **, p < 0.01 (Tukey’s test). Square markers indicate independent transfections. (4b-4c) Simulation results closely matched the experimentally-determined cell-to- cell output variability of the NF (4b) and IFF (4c) circuits. For panel (4b), the filled markers indicate the mean of n = 3 independent transfections per construct. For panel (4c), the square markers indicate independent transfections. The error bars are the SEM. Each simulation datapoint (open markers) was computed from 10,000 cells whose plasmid copy number was sampled from the estimated plasmid copy number distribution. See Example 8 for simulation models and methods. (4d) Deterministic simulations predicted that Equalizer-L has a wider compensation range than standalone NF and IFF circuits. The dashed gray curve (right axis) illustrates the estimated plasmid copy number distribution. (4e-4g) Simulated overall expression rate (4e), number of proteins translated per mRNA (4f), and transcription rate per plasmid (4g) for each topology. The dotted lines indicate the slopes corresponding to perfect dosage compensation. [0066] Figures 5a-5h. Equalizer-L has superior gene dosage compensation than an alternative circuit that combines post-transcriptional NF and IFF motifs. (5a-5d) Circuit schematics. mScarlet-I (RFP) and mCitrine (YFP) are reporters of circuit output and gene dosage, respectively. CMV (c) and OLP (5d) do not have dosage compensation circuitry and are used as controls for Equalizer-L (5a) and HYB (5b), respectively. (5e) Representative circuit- output histograms. For all experiments (5e-5h), Equalizer-L was induced with 1 ng/mL of doxycycline. (5f) HYB produces high cell-to-cell circuit output variability. The gene-dosage values were normalized to those obtained when transfecting 1 ng of plasmid. The circles represent independent transfections. n = 36 per circuit (3 per dose and 6 doses per circuit). The dashed lines are trend lines (linear for Equalizer-L and CMV; exponential for HYB and OLP). The inset shows the trend lines for the entire range of gene-dosage reporter levels. (5g-5h) Equalizer-L (5g) showed superior gene dosage compensation than HYB (5h) at the population level. The gene-dosage and circuit output values were normalized to those obtained when transfecting 1 ng of plasmid. Dashed lines indicate trend lines (hyperbolic for CMV and linear otherwise). Sample sizes are as in (5f). p < 0.0001 for Welch’s t-test comparing the trendline slopes of Equalizer-L and HYB. [0067] Figures 6a-6b. The replicating variant of Equalizer-L enables the development of extra-chromosomal cell lines that have stable gene expression with low cell-to-cell variation (6a) Over the course of > 8 weeks, the Equalizer-L episome produced similar cell-to-cell variation as a chromosomally integrated CMV cassette (CMV cell line) and lower variation than episomes with unregulated promoters. HEK293 cells were used. In 6a, from left to right the bars are Equalizer-L episome, CMV cell line, CMV episome, and PGK episome. For all experiments (6a-6c), Equalizer-L was induced with 1 ng/mL doxycycyline. The error bars represent SEM. n = 4 independent trials. Tukey’s test was used to compare the Equalizer-L episome with the other conditions. ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. (6b) Representative images of HEK293 cells expressing EGFP from episomes or the chromosome at 23 days post-transfection. Each image is displayed with a linear lookup table with the minimum set to 0 and the maximum set to the sum of the mean intensity value and three standard deviations (see Methods). This approach enables a qualitative comparison of the cell-to-cell expression variability despite large differences in mean circuit output. Insets, binary masks to help identify regions of the images that correspond to cells (white region). Scale bar, 50 μm. See Fig.24 for images acquired on other days. (6c) The circuit output levels from the Equalizer-L episome were stable for > 50 days, whereas episomes with unregulated promoters displayed pronounced declines. Each circuit’s mean output levels were normalized to levels at 9 days post- transfection. Induction, sample sizes, error bars, and statistical tests are as in panel (a). [0068] Figures 7a-7b. Negative feedback and type I incoherent feedforward topologies of genetic circuits. (7a) In type I incoherent feedforward circuits, a common input (e.g., plasmid) drives the expression (black arrows) of an inhibitor (e.g., miRNA) and an output protein (e.g., EGFP). The inhibitor only represses (red blunted arrow) the output protein production and does not repress its own expression. (7b) In negative feedback circuits, a common input (e.g., plasmid) drives the expression of an inhibitor (e.g., Tet repressor protein) and an output protein (e.g., EGFP). Unlike incoherent feedforward circuits, the inhibitor represses both its own production and that of the output protein. [0069] Figures 8a-8b. The gene dosage compensation profiles of NF circuits can be tuned by changing the doxycycline concentration. Predicted gene dosage compensation at different doxycycline concentrations for (8a) an ideal NF circuit or (8b) an NF circuit that is ”leaky”. The leaky NF circuit has incomplete repression at saturating concentrations of TetR. The solid lines correspond to the doxycycline concentrations that predicted the lowest CV with the fitted plasmid copy number distribution described in “Estimating the distribution of plasmid copy numbers in transfected cells” of Example 8, 1ng/mL for the ideal NF and 10ng/mL for leaky NF. The calculation of the predicted gene dosage compensation score is described in the Examples. See “Model description of four key topologies utilized to predict gene dosage compensation” of Example 8 for the model description and Tables 1 and 2 for other parameters used in the simulations. [0070] Figures 9a-9c. A TetR-based negative feedback circuit shows incomplete repression when uninduced. (9a) Schematic of an EGFP expression cassette with negative feedback (NF) control. (9b-9c) Uninduced NF circuits produce substantial fluorescence. (9b) Representative images of HEK293A cells transfected with plasmids encoding the NF circuit without inducer (left) and under saturating amounts of inducer (1000 ng/mL doxycycline, right). Images are shown with the same lookup table. Scale bars, 50 m. (9c) The mean circuit output levels of the uninduced NF is 17±1.1 % of the circuit output levels obtained under fully induced NF. The mean output levels were relative to that of the fully induced NF circuit. Error bars indicate SEM. n = 3 independent transfections. [0071] Figures 10a-10e. Simulating the gene dosage compensation profiles of Equalizer and IFF circuits using different model parameters. (10a) The gene dosage compensation profile of the Equalizer can be tuned by changing the doxycycline induction level. The curves for doxycycline concentrations of 5, 10, and 50 ng/mL show substantial overlap. The mathematical definition of predicted gene dosage compensation is described in the Examples. (10b-10e) The Equalizer circuit is predicted to display improved gene dosage compensation compared with its incoherent feedforward (IFF) subcircuit over a wide range of RISC concentrations and miRNA- target binding affinities. (10b & 10c) Predicted dosage compensation as a function of plasmid copy number for (10b) the IFF subcircuit and (10c) the Equalizer. For both panels, RISC abundance was varied 9-fold (from 0.2 to 1.8-fold). The solid lines corresponds to 1.7e+05 RISC complexes/cell. The simulated doxycycline concentration was 1 ng/mL. (10d & 10e) Predicted dosage compensation as a function of plasmid copy number for (10d) the IFF subcircuit and (10e) the Equalizer. For both panels, the miRNA affinity was tuned in the model by changing the miRNA dissociation rate constant while keeping the miRNA association rate constant unchanged. For this in silico proof of concept, before any parameter fitting, the miRNA dissociation rate was first set to 0.3 second-1 (solid line) and was varied 9-fold (from 0.2 to 1.8- fold, dashed lines). The simulated doxycycline concentration was 1 ng/mL. See “Model description of four key topologies utilized to predict gene dosage compensation” of Example 8 for model description and Tables 1 and 2 for other parameters used in the simulations. [0072] Figures 11a-11k. Schematics of Equalizer and unregulated expression plasmids. (11a-11e) Schematics of Equalizer plasmids. bGlob intron is the rabbit ^-globin intron II. WPRE is the woodchuck hepatitis virus post-transcriptional regulatory element. bGlob intron and WPRE were placed at the 5’ and 3’ UTRs, respectively, to increase the overall gene expression levels. (11f-11k) Schematics of unregulated expression plasmids. For the CMV and PGK unregulated promoters, additional variants were made with tetO2, bGlob intron, and WPRE for closer similarity to the Equalizer circuits. These elements did not impact cell-to-cell variability (Fig.28). For simplicity, those variants are also referred to as CMV and PGK, as appropriate. Table 4 lists the experiments in which each plasmid was used. (11i) Schematic of unregulated mCherry expression plasmid. The CMV-mCherry plasmid was co-transfected with the circuit plasmids that do not have the onboard mCherry cassette. This plasmid was used to quantify plasmid uptake and distinguish transfected cells from the non-transfected cells. See Table 4 for the complete list of plasmids and their usage in this disclosure. [0073] Figure 12. Cell-to-cell variability in circuit output level is similar when quantified by flow cytometry or microscopy. HEK293 cells were transfected with Plasmid 1 (Equalizer-L episome) or Plasmid 2 (Unregulated CMV episome). Cells transfected with the Equalizer-L episome were induced with 1 ng/mL doxycycline. Two days after transfection, cells were analyzed with either flow cytometry or microscopy. [0074] Figures 13a-13b Mean gene-dosage reporter and mean circuit output levels of Equalizer-L and unregulated promoter circuits at different transfection plasmid doses. (13a & 13b) Data is from the same experiments as Figs.3e-3g. HEK293 cells were transfected with plasmids encoding Equalizer-L, unregulated CMV, or unregulated PGK circuits (see Figs.11d, 11h & 11j) and analyzed using flow cytometry. Each plasmid also expressed mCherry RFP from the constitutive EF1 promoter. Gene-dosage reporter levels of individual cells were quantified as red fluorescence. (13a) Mean gene-dosage reporter levels at different doses of transfected plasmids. At each dose, the mean gene-dosage reporter levels of the three circuits were similar. Mean values ± SEM are shown. n = 6 independent transfections. (13b) Mean circuit output levels at different doses of transfected plasmids. Mean values ± SEM are shown. n = 6 independent transfections per circuit. [0075] Figures.14a-14b Equalizer-L buffers circuit output changes resulting from different doses of transfected plasmid. (14a & 14b) Data is from the same experiments as Figs. 3e-3g. HEK293 cells were transfected with plasmids encoding Equalizer-L, unregulated CMV, or unregulated PGK circuits (Fig.11d, 11h & 11j) and analyzed using flow cytometry. Each plasmid also expressed mCherry RFP from the constitutive EF1 promoter. Gene-dosage reporter levels of individual cells were quantified as red fluorescence. (14a) Representative histograms of gene-dosage reporter levels. At each plasmid dose, the gene-dosage reporter distributions of the three circuits were similar. (14b) For each circuit, single-cell flow cytometry data used to generate Fig.3e was pooled and divided into bins with an equal number of data points per bin. For each bin, the mean fluorescence was computed, and each mean value was normalized to that of the first bin. This approach was used for both axes. The top panel shows the entire range of gene-dosage reporter levels. The bottom panel shows a narrower range of gene-dosage reporter levels. n = 3 independent transfections. Error bars indicate SEM. [0076] Figures 15a-15c. Schematics of Equalizer-L and the standalone NF and IFF circuits (15a) Equalizer-L (15b) the negative feedback (NF) circuit (15c) the incoherent feedforward (IFF) circuit. bGlob intron is the rabbit ^–globin intron II. WPRE is the woodchuck hepatitis virus post-transcriptional regulatory element. bGlob intron and WPRE were placed at the 5’ and 3’ UTRs, respectively, to increase the overall gene expression levels. [0077] Figure 16. Equalizer-L produces a narrower circuit output distribution than the standalone NF or IFF circuits in transfected cells. Representative contour plots of flow cytometry data used in Fig.4b & 4c are shown. The inducer was used at the concentration providing the lowest cell-to-cell output variability: 0.5 ng/mL for the Equalizer-L and 10 ng/mL for the NF circuit. The plasmid expressing mCherry (i.e., an RFP) from the constitutive promoter CMV (Figure 11k) was co-transfected with the circuit plasmids. Gene-dosage reporter levels were estimated as red fluorescence. Histograms of gene-dosage reporter (top edge) and circuit output (right edge) are shown. The dots indicate data points that were outside the lowest contour lines (i.e., 10% line) [0078] Figure 17. Equalizer-L is predicted to show superior dosage compensation compared with IFF and NF circuits. Equalizer-L and NF circuit were simulated with doxycycline concentrations that were predicted to give the best gene dosage compensation (1 ng/mL for Equalizer-L and 10 ng/mL for NF; see Fig.4b). The predicted gene dosage compensation is defined in the Examples. See “Model description of four key topologies utilized to predict gene dosage compensation” of Example 8 for the model description and Tables 1 and 2 for other parameters used in the simulations. [0079] Figure 18. Equalizer-L shifts the saturation of RISC to higher plasmid copy numbers. The solid lines show the predicted output protein levels. Dashed lines show the abundance of free RNA-induced silencing complex (RISC). Because of the shift in RISC saturation, Equalizers can more effectively buffer gene dosage at higher plasmid copy numbers. See “Model description of four key topologies utilized to predict gene dosage compensation” of Example 8 for model description and Tables 1 and 2 for other parameters used in the simulations. [0080] Figures 19a-19b. Determination of the optimal inducer concentrations for the Equalizer-L variant and an alternative NF-IFF hybrid circuit (HYB) to achieve the lowest cell-to- cell output variability. Cell-to-cell output variability (left) and mean circuit output levels (right) of HEK293T cells harboring (19a) Equalizer-L or (19b) HYB and cultured with different inducer concentrations. For the schematics of the gene circuits, see Fig.5a & 5b. This experiment is similar to that of Figures 3a-3b but uses the Equalizer-L circuit variant with mScarlet-I (i.e., an RFP) as a reporter of circuit output and mCitrine (i.e., YFP) as a marker of gene dosage. Mean values ± SEM are shown. The mean expression values are relative to that of uninduced Equalizer-L. n = 6 independent transfections per circuit. [0081] Figures 20a-20j Equalizer-L has superior gene dosage compensation than HYB, an alternative circuit that combines NF and IFF regulations. (20a) Representative gene-dosage reporter histograms at the different plasmid doses.( 20b) HYB-transfected cells (left) produce a bimodal and wide circuit output distribution while Equalizer-L-transfected cells (right) produce a narrow unimodal distribution. Representative contour plots of flow cytometry data. The plasmid dose was 50 ng. Histograms of gene-dosage reporter (top edge) and circuit output (right edge) are shown. The non-transfected cell population is included as a reference to show baseline fluorescence levels. Biexponential axes are used. The dots indicate data points outside the lowest contour lines (i.e., 10% line). (20c) For each circuit, all single-cell flow cytometry data used to generate Figs.5e-5h was divided into 20 bins with an equal number of data points per bin. For each bin, the mean fluorescence was computed and each mean value was normalized to that of the first bin. This approach was used for both axes. The top panels show the entire range of gene- dosage reporter values. The bottom panels show a narrower range of gene-dosage reporter (inside red dashed boxes). n = 3 independent transfections. Error bars indicate the SEM. (20d- 20f) Data reanalysis after excluding transfected cells (mCitrine+ cells) with no detectable circuit output levels (see panel b). (20d) Reanalysis of panel c. (20e) Reanalysis of Fig.5h. (20f) Reanalysis of Fig.5f. (20g) Cell-to-cell variability in circuit output levels of the original HYB and OLP plasmids in two different cell types. These cell types and plasmids were used in the original study that reported the two circuits [18]. The two plasmids have DsRed-Express rather than mScarlet-I as circuit output reporter. p > 0.99 for HEK239T and p = 0.08 for CHO-K1 (Sidak’s test). n = 7 independent transfections per circuit for HEK293T and n = 3 independent transfections per circuit for CHO-K1. Error bars indicate the SEM.50 ng of circuit plasmid was used per transfection reaction. (20h) The Fano factor of circuit output levels. ***, p < 0.001; ****, p < 0.0001 (Dunnett’s test). n = 6 independent transfections. Error bars indicate the SEM. 50 ng circuit plasmid was used per transfection reaction. This panel was created to reproduce data from a previous study [18]. However, note that the Fano factor is not an appropriate metric to quantify cell-to-cell variability in this experiment given the large differences in mean circuit output levels (see “Comparing the coefficient of variation and the Fano factor as measures for quantifying cell-to-cell variability in the experiments” of Example 8). (20i) The Fano factor of circuit output levels of circuit plasmids used in Fig.3c (50 ng transfection dose). n = 6 independent transfections. Error bars indicate SEM. Note that the Fano factor is not an appropriate metric to quantify cell-to-cell variability in this experiment given the large differences in mean circuit output levels (see “Comparing the coefficient of variation and the Fano factor as measures for quantifying cell-to-cell variability in the experiments” of Example 8). (20j) When plotting unnormalized circuit output values on a graph with a linear y-axis, flat curves may be due to gene dosage compensation, low circuit output, or both. Data from Fig.5 (right) and Fig.3 (left) are shown for a side-by-side comparison. The unregulated PGK promoter, which has weak expression but does not compensate for gene dosage, produces a flat curve similar to that of Equalizer-L (right). Error bars indicate SEM. [0082] Figures 21a-21d. The multi-promoter variant of the Equalizer-L showed impaired dosage compensation versus the original Equalizer-L. (21a) Simplified schematics of the original Equalizer-L (top) and a multiple promoter implementation (bottom). More detailed schematics are shown in Fig.11d-11e. (21b) The multi-promoter Equalizer-L produces greater cell-to-cell variability in circuit output levels at most inducer concentrations compared than single-promoter Equalizer-L. Mean values ± SEM are shown. n = 6 transfections/condition. p = 0.038 (t-test comparing the areas under the curve). (21c) Deterministic simulations predicted that the two Equalizer-L variants would produce similar cell-to-cell variability across the tested range of inducer concentrations. (21d) The multi-promoter Equalizer-L is predicted to produce ~ 2-fold greater intrinsic noise than the original Equalizer-L. Three different sets of translational and transcriptional parameters produce similar results. This greater intrinsic noise may explain why the multi-promoter Equalizer-L produced higher cell-to-cell variability experimentally (panel b) but not in deterministic simulations that do not account for intrinsic noise (panel c). The simulated doxycycline concentration was 0.5 ng/mL. Addition information is provided in “Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L” of Example 8. [0083] Figures 22a-22c. Schematics of episomal Equalizer-L and unregulated plasmids used to characterize gene dosage compensation of Equalizer-L over a two-month period. (22a-c) bGlob intron is the rabbit ^-globin intron II. bGlob intron was placed at the 5’ UTR to increase the overall gene expression levels. Hph gene encodes the hygromycin-B-phosphotransferase protein, which confers resistance to Hygromycin-B. EBNA-1 is the Epstein-Barr nuclear antigen-1. OriP is the origin of replication of the Epstein-Barr virus. EBNA-1 and OriP are necessary to maintain episomal plasmids in transfected cells. [0084] Figures 23a-23c. Determination of fluorescence thresholds between expressing and non-expressing cells in flow cytometry experiments with episomes. The boundaries between the quadrants (Q1-4) were set to minimize false positives using cellular fluorescence distributions from (23a) untransfected (GFP- and RFP-) cells, (23b) cells stably expressing EGFP from the genome, and (23c) episomes encoding an EF1 -mCherry expression cassette. The number inside each quadrant (Q1-4) represents the number of cells in the quadrant as a percentage of total cells. [0085] Figure 24. Representative fluorescence images of episome-carrying cells over a two-month period. The same cell populations analyzed by flow cytometry (Fig.24) were also imaged by fluorescence microscopy. To help distinguish dim cells from the background, binary masks were also shown beside each fluorescent image. Magenta denotes cells, and blue denotes background. Two-photon microscopy was used to produce optical sectioning, thereby reducing variations in brightness due to differences in cell thickness. Scale bars, 50 μm. [0086] Figure 25. Circuit expression distributions from populations of episome-carrying cells over a two-month period. The fluorescence of individual HEK293 cells was determined by flow cytometry. As a control, cells were grown and analyzed from a line containing a chromosomally-integrated CMV-EGFP expression cassette (column 2). This line was not transfected with episomes, but passaged and analyzed on the same days as other episome- transfected cells. Schematics of episome plasmids are shown in Fig.22. Each episome plasmid expressed mCherry (i.e., an RFP) from a constitutive EF1 promoter. Gene-dosage reporter level of individual cells was quantified as red fluorescence of the cells. Circuit output level was defined as EGFP fluorescence. The thresholds between GFP+ and GFP- cells and between RFP+ and RFP- cells were defined using control cell populations (Fig.23). Flow cytometry data are presented on biexponential axes. The number inside each quadrant (Q1-4) represents the number of cells in the quadrant as a percentage of total cells. The axes numbers shown in the bottom left plot apply to the rest of the plots. [0087] Figures 26a-26e. Circuit output and gene-dosage marker levels from Day 9 to Day 60 after transfection with episomal vectors. (26a) Circuit output. (26b) Gene dosage marker, defined as the red fluorescence from the EF1 -mCherry cassette encoded on each circuit plasmid (see Fig.22 for plasmid schematics). (26c) Mean gene-dosage marker values from panel b were normalized to the mean at 9 days after transfection. (26d-26e) Reanalysis of (26d) cell-to-cell output variability (bars from left to right correspond to legend from top to bottom) and (26e) circuit output levels shown in Fig.6a & 6c, respectively. Reanalysis was conducted after including GFP+ and RFP- cells (see Fig.23). For all panels, mean values ± SEM are shown. n = 4 independent trials. The data shown here is from the same experiments as illustrated in Fig.6. [0088] Figures 27a-27e. Equalizer components rewired to match a natural yeast dosage compensation topology is predicted to have less efficient gene dosage compensation than the original Equalizer topology. (27a) A schematic of the yeast galactose (GAL) pathway, a natural gene dosage. GAL1, 3, 4, and 80 are GAL pathway proteins. POI stands for protein of interest. Diagram adapted from Peng et al. [19]. (27b) The original Equalizer’s topology drawn to mimic the layout in (27a). (27c) A schematic of Equalizer components rewired to match the yeast GAL pathway topology in (27a). Compared with (7b), the miRNA does not inhibit the POI expression. (d) Alternative rewiring of Equalizer components. Here, the miRNA in (27c) is replaced with a hypothetical TetR inhibitor that binds to TetR with 1:1 stoichiometry. The miRNA was replaced with a TetR-inhibitor because a study has shown that a 1:1 stoichiometry of activator (e.g., TetR- inhibitor) and inhibitor (e.g., TetR) was important for dosage compensation [20]. (27e) Simulation results that compare the predicted gene dosage compensation of the circuits illustrated in (27b-27d). For each topology, the doxycycline concentration predicted to produce the lowest cell-to-cell variability was determined in simulations with 10,000 cells and the plasmid copy number distribution fitted in “Estimating the distribution of plasmid copy numbers in transfected cells” of Example 8. Doxycycline concentrations of 0, 0.5, 1, 5, 10, 50, and 100 ng/mL were tested. The curves plotted here are at the optimal doxycycline concentration for each topology (1 ng/mL for the Equalizer-L, and 1 ng/mL for (27c) & (27d)). The circuit was simulated in (27d) with three association rates between TetR and TetR inhibitor to determine how this parameter would impact the predicted gene dosage compensation. [0089] Figures 28a-28b. Addition of the tetO2 site in the promoter region, the rabbit ^- globin intron II sequence in the 5’UTR, and WPRE sequence in the 3’ UTR has minimal effect on cell-to-cell output variability. (28a-28b) Unregulated PGK plasmid or Unregulated PGK+ plasmid, a variant with additional sequences, were transfected in HEK293 cells. The fluorescence of single cells was quantified by flow cytometry. Schematics of PGK and PGK+ plasmids can be found in Fig.11i and 11j, respectively. (28a) Cell-to-cell output variability. p = 0.54 (unpaired t-test). (28b) Circuit output levels. p = 0.043 (unpaired t-test). For both panels, mean values ± SEM are shown. n = 6 independent transfections per circuit. [0090] Figures 29a-29e. Description of topologies utilized to predict gene dosage compensation. [0091] Figure 30. Modeling of a transcriptional negative feedback circuit. [0092] Figures 31a-31d. Estimating the distribution of plasmid copy numbers in transfected cells. (31a) Distribution of single-cell expression of the unregulated CMV circuit. (31b) Distribution of single-cell expression of the unregulated PGK circuit. (31c) Dependence of the Mean Square Error (MSE) on promoter leakage and the scale parameter (Θ) of the gamma distribution used to fit the plasmid copy number distribution. The MSE corresponds to the Mean Square Error of the simulated mean expression of the NF circuit at 8 different inducer concentrations (0, 1, 5, 10, 50, 100, 500, 1000 ng/mL) compared with experimental data. The promoter leakage is defined as the following: leakage/(1+leakage) is the ratio of the expression under saturating TetR concentration and the expression under saturating inducer (doxycycline) concentration. (31d) . Dependence of the MSE on the scale parameter (Θ) at a leakage level of 0.25. This leakage level produced the smallest deviation between the simulated and experimental mean expression of the NF circuit at the inducer concentrations specified in (31c). [0093] Figures 32a-32b. Predicting cell-to-cell variability. (32a) Comparison of experimental results and simulations when using a scale parameter (Θ) of 120 and miRNA dissociation rate constant of 0.303 second-1. (32b) Comparison of experimental results and simulations when using a scale parameter (Θ) of 100 and miRNA dissociation rate constant of 0.289 second-1. [0094] Figure 33. Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L. [0095] Figures 34a-34c. Equalizer circuits can be developed using alternative NF inhibitors, as shown here using LacI rather than TetR. (34a) Cell-to-cell variability as a function of IPTG concentration for both the NF-only circuit and the NF+IFF (Equalizer) design. The Equalizer circuit outperforms the NF-only circuit; (34b) Expression (circuit output) vs. IPTG concentration for a representative LacI-based NF-only circuit; (34c) Expression (circuit output) vs. IPTG concentration for a representative LacI-based Equalizer circuit. [0095.5] Figures 35a-35e. Improving mRNA stability is predicted to increase Equalizer circuit expression level and gene dosage compensation ability. (35a) cell-to-cell variation upon variation of doxycycline level; (35b) expression level of protein of interest (POI) as a function of doxycycline concentration; (35c) cell-to-cell variation as a function of the relative target mRNA degradation rate (35d) POI expression level as a function of the relative target mRNA degradation rate; and (35e) POI expression level and cell-to-cell variation for various mRNA degradation rates DETAILED DESCRIPTION [0096] Precise control of gene expression is critical for biological research and biotechnology. However, transient plasmid transfections in mammalian cells produce a wide distribution of copy numbers per cell, and consequently, high expression heterogeneity. Embodiments of the disclosure encompass plasmid-based synthetic circuits, referred to herein as Equalizers, that buffer copy number variation at the single-cell level. Equalizers couple a transcriptional negative feedback loop with post-transcriptional incoherent feedforward control. The disclosed circuits produce as low cell-to-cell variation as chromosomally integrated genes. In some embodiments, episome-encoded Equalizers enable the rapid generation of extrachromosomal cell lines with stable and uniform expression. The disclosure provides these circuits as simple and versatile devices for homogeneous gene expression and that can facilitate the engineering of synthetic circuits that function reliably in every cell. I. [0097] Systems and Compositions [0098] Embodiments of the disclosure include systems that comprise synthetic circuits for buffering gene dosage variation between individual mammalian cells. In particular embodiments, the systems incorporate control at both the transcriptional level and the pos- transcriptional level. In specific embodiments, the systems of the disclosure utilize a bipartite approach to plasmid copy regulation that also impacts expression control by incorporating two different inhibitory loops: (1) a transcriptional negative feedback (NF) loop; and (2) post- transcriptional incoherent feedforward control (IFF). [0099] In particular embodiments, the systems utilize component(s) for the NF loop and component(s) for the IFF control on the same polynucleotide molecule and, in at least some cases, expression of the respective component(s) for the NF loop and component(s) for the IFF control is regulated by the same regulatory sequence, such as a promoter of any kind. For the NF loop, the components may utilize any proteins that bind a regulatory sequence to inhibit transcription. In specific cases, elements from the tetracycline operon system or lac operon may be utilized. Other examples for the NF loop include a dCas9 with or without a coupled transcriptional repressor (coupled with a co-expressed guide RNA), a Zinc finger, or repressors based on transcription activator-like effectors (TALE) scaffolds. In specific cases, the system includes an expression construct that comprises operably linked sequences including (1) a regulatory sequence to which a repressor can bind, (2) sequence that encodes a repressor, (3) gene of interest sequence that encodes a gene product of interest, wherein the repressor sequence and gene of interest sequence are separated by a ribosome-skipping sequence or an internal ribosome entry site (such as a 2A peptide cleavage sequence), (4) as part of the IFF component, miRNA target sequences that flank the repressor-2A-gene of interest sequences; and (5) sequence that encodes an miRNA that can bind the miRNA target sequences, wherein in at least some cases the miRNA is flanked by splicing sites. In particular embodiments, all of the the operably linked sequences are regulated by the same promoter, although in other cases multiple promoters are utilized. [0100] In particular embodiments, a transcriptional repressor, such as tetR, is linked by a 2A sequence to a gene of interest from which a gene product of interest may be expressed, and the order of tetR and the gene of interest in a 5’ to 3’ direction may be of any order. The gene product of interest may be of any kind and may be a protein or an RNA. As part of the NF loop component of the system, the repressor represses its own transcription by binding to a site upstream of the repressor, which may be referred to as a cognate operator site. As being part of the same expression construct, the repressor also represses transcription of the gene of interest and the miRNA from the IFF component of the system. [0101] In particular embodiments, the sequence encoding the repressor, a 2A sequence, and the gene of interest are flanked by miRNA target sites, although in alternative cases the miRNA can be positioned so that it is expressed at the 5’ end or the 3’ end of the expressed transcript. Expression from the expression construct, and subsequent splicing at splicing sites that flank an miRNA sequence, produces the miRNA IFF component of the system that is then able to inhibit production of the gene product of interest. [0102] A regulatory sequence, such as a promoter, regulates expression from the expression construct that produces a transcript that comprises sequence that encodes the repressor, the gene of interest, and the miRNA (that is subsequently spliced out) on the same transcript molecule. The promoter that regulates the expression construct may be of any kind, including constitutive, inducible, tissue-specific, and so on. In specific cases, the promoter is the CMV promoter, PGK promoter, the UBC promoter, the EF1a promoter, the CAG promoter, the hSyn1 promoter, the CaMKII promoter, the GFAP promoter, the TRE promoter, SV40, synthetic promoters of the COMET family, synthetic promoters with cell-state specificity, and so on. [0103] In specific embodiments, the expression construct employs a ribosome-skipping sequence or an internal ribosome entry site. In specific cases, one or more ribosomal skipping sequences, such as one or more 2A peptide cleavage sequences, are utilized. Examples of 2A sequences are below, where the GSG sequence is optional. [0104] T2A (GSG) EGRGSLL TCGDVEENPGP (SEQ ID NO:1) [0105] P2A (GSG) ATNFSLLKQAGDVEENPGP (SEQ ID NO:2) [0106] E2A (GSG) QCTNYALLKLAGDVESNPGP (SEQ ID NO:3) [0107] F2A (GSG) VKQTLNFDLLKLAGDVESNPGP (SEQ ID NO:4) [0108] Any gene of interest may be utilized in the system of the disclosure. The gene product produced by the gene of interest may be of any kind and may be utilized for research or therapeutic purposes. In some embodiments, multiple gene products are produced in the system because multiple, nonidentical genes of interest are utilized in the same expression construct. In such cases, the multiple genes of interest are separated by a ribosome-skipping sequence or an internal ribosome entry site such that the multiple gene products are ultimately produced separately. In alternative cases, they could be linked covalently if separate expression is not required. In specific cases, when multiple gene products are produced from the system, they may or may not be needed for a joint purpose, such as dimerization, for example. [0109] The IFF miRNA component may be an miRNA of any sequence as long as it includes complementarity sequence with its target sites.In specific embodiments it is miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a, for example. The miRNA has known target sequences that are utilized in the expression construct by flanking the repressor-2A-gene of interest combined sequence. The miRNA is flanked by splicing sites such that upon production of a pre-transcript that includes the repressor and gene of interest sequences, the miRNA sequence is spliced out of the pre-transcript so that it can act on its target sites. The IFF miRNA component of the expression construct may be 5’ or 3’ to the NF sequences in a 5’ to 3’ direction of the polynucleotide. In other embodiments, other than using an miRNA the IFF is implemented using transcriptional activators and repressors. For example, other ways to generate an IFF may be utilized, including at least: (1) other kinds of RNA that cause RNA interference, such as small interfering RNA (siRNA) and short hairpin RNA (shRNA); (2) endoribonucleases that degrade mRNAs, such as CasE; (3) a transcriptional IFF, in such cases where a promoter expresses an activator that would then turn on the gene of interest expressed from a different promoter that is upregulated by the activator; the activator would also upregulate a third promoter that produces a repressor that would then repress expression of the gene of interest. [0110] Compositions of the disclosure include any entity that comprises the system, including polynucleotides that comprise the expression construct with the system components, cells that comprise the polynucleotides that comprise the expression construct with the system components, vectors that comprise the expression construct with the system components (including plasmids or episomes), and cells that comprise the vectors with the expression construct with the system components. The cells may be of any kind, including prokaryotic or eukaryotic, including mammalian. Although the system may be utilized extrachromosomally, the system components (which includes equalizer circuits) may be integrated into the genome, e.g., to reduce the gene expression variability caused by genetic and epigenetic differences between integration positions. [0111] In embodiments alternative to systems having combinations of NF and IFF, there may be use of NF or IFF where they are not combined, in particular for producing dosage compensation at the single cell level. In some cases, the NF mechanism of the system is utilized in the absence of IFF (e.g., see Figs.4a-4b), whereas in other cases the IFF mechanism of the system is utilized in the absence of NF. In cases wherein IFF is utilized in the absence of NF, such a system is not utilized for population-based dosage compensation. II. [0112] Methods of Use of the System [0113] Embodiments of the disclosure include methods of regulating expression of at least one gene of interest. The methods allow for controllable, substantially uniform production of amounts of gene products of interest, including at least natural or synthetic proteins, such as for their study or for producing substances of commercial interest. In particular embodiments, the methods of the disclosure allow for reducing variability in copy numbers following transfection of plasmids in mammalian cells. The methods provide for achievement of uniform expression levels in desired cells that comprise the system. [0114] Embodiments of the disclosure include methods for regulating gene expression and includes methods of reducing gene expression variability between individual cells. In one embodiment, there is a method of reducing gene expression variability between individual cells of a gene of interest, comprising the steps of transfecting or transducing a plurality of cells with a plasmid or episome comprising an expression construct that expresses a single transcript encoding one or more components of a transcriptional negative feedback loop and one or more components of a post-transcriptional incoherent feedforward control for expression of the gene of interest, wherein the components of the transcriptional negative feedback loop and the post- transcriptional incoherent feedforward control are regulated by the same regulatory sequence. [0115] In certain embodiments, the expression construct is further defined as expressing a single transcript encoding a tetracycline repressor, a gene product from the gene of interest, and a miRNA flanked by splicing sites, wherein following exposure of an effective amount of an inducer to inhibit the repressor, the transcript is expressed and the miRNA is spliced out to inhibit production of the gene product of interest. In specific aspects, the method includes providing a system in a cell, where the cell comprises a polynucleotide such as a plasmid or episome that includes a particular expression construct comprising one or more components of a transcriptional negative feedback loop and of a post-transcriptional incoherent feedforward control. In specific cases, the expression construct comprises in a 5’ to 3’ direction: (a) an operator site that regulates expression of a repressor; (b) an miRNA target site; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) a gene of interest; (f) an miRNA target site; and (g) an miRNA-encoding sequence, wherein said miRNA-encoding sequence is flanked by splicing sites. [0116] When desired by the user of the system, the cells are exposed to an effective amount of an inducer compound that induces expression from the expression construct by inhibiting the repressor. The expression from the expression construct produces a transcript that comprises sequence encoding the repressor, sequence encoding a gene product from the gene of interest, and the miRNA-encoding sequence. Following production of the transcript and splicing of the miRNA out of the transcript by splicing sites that flank the miRNA-encoding sequence, the miRNA recognizes the miRNA target sites on the transcript that results in inhibition of production of the gene product. [0117] In particular embodiments, the concentration of inducer that is utilized in the method facilitates substantially uniform production of the gene product of interest. In specific cases when the inducer is doxycycline or a functional derivative thereof, an effective amount of doxycycline may be about 0-1000 ng/mL. Ranges for an effective amount of doxycycline include 0.5-1000, 0.5-750, 0.5-500, 0.5-250, 0.5-100, 0.5-50, 0.5-5, 5-1000, 5-750, 5-500, 5-250, 5-100, 5-50, 50-1000, 50-750, 50-500, 50-250, 50-100, 100-1000, 100-750, 100-500, 100-250, 250-1000, 250-750, 250-500, 500-1000, 500-750, or 750-1000 ng/mL. [0118] In specific embodiments, the method further comprises the step of transfecting or transducing desired cells with an effective amount of the plasmid or episome. In such cases, one may transfect or transduce a particular amount of plasmid into the cells, such as an effective amount being about 1-200 ng plasmid for approximately 24,000 cells. In specific embodiments, there are 1-30,000 total plasmids/nucleus and/or 1-1000 actively transcribing plasmids/nucleus. [0119] As noted above, in particular embodiments the methods employ two different inhibitory loops to control expression and production of a gene of interest. In some cases, the two inhibitory loops are a tetracycline-based transcriptional negative feedback loop and an miRNA-based post-transcriptional incoherent feedforward control. Although the gene expression is regulated by multiple inhibitory mechanisms, the gene product is nevertheless produced at least as a function of the plasmid concentration. For example, in specific cases, at low plasmid concentration the concentration of miRNA is sufficiently low so that the inhibition loop may be weak enough to allow production of the gene product of interest. In other cases, a higher plasmid concentration produces stronger inhibition and in at least certain embodiments this is how the system compensates for plasmid dosage. [0120] In other embodiments, although the gene expression is regulated by multiple inhibitory mechanisms, the gene product is nevertheless produced at least as a function of incomplete inhibition of transcription of the gene of interest. This is a function of the TetR repressing itself, in specific cases of the system. For example, in situations where the concentration of TetR is low, then TetR levels will rise (along with levels of the gene product of interest) until it reaches a concentration where it starts to repress itself. If because of chance events there is excess transcription of TetR, then no new TetR (and gene product of interest) will be expressed until the level of TetR reduces because of dilution during cell growth. In this way, the overall expression level remains relatively constant, and in at least specific embodiments the mechanism helps to compensate for plasmid copy number variation. III. [0121] Kits of the Disclosure [0122] Any of the system components or compositions described herein may be comprised in a kit. In specific embodiments, the kit may comprise polynucleotides, cells, primers, buffers, salts, instructions, and so forth for generation or use of the system of the disclosure. In specific embodiments, an expression construct, vector including the expression construct, or vector for insertion of the expression construct may be included, including reagents to generate them. Certain cells for use of the system may be included, including mammalian cells, for example. In some cases, compounds that control at least part of the system may be included in the kit, including, for example, an inducer that induces transcription of the expression construct upon repression of the repressor. [0123] The components of the kits may be packaged either in aqueous media or in lyophilized form. The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted. When the components of the kit are provided in one and/or more liquid solutions, the liquid solution is an aqueous solution, with a sterile aqueous solution being particularly preferred. The components of the kit may be provided as dried powder(s). When reagents and/or components are provided as a dry powder, the powder can be reconstituted by the addition of a suitable solvent. It is envisioned that the solvent may also be provided in another container means. The kits may comprise a second container means for containing a sterile, pharmaceutically acceptable buffer and/or other diluent. [0124] Where there are more than one component in the kit, the kit also will generally contain a second, third or other additional container into which the additional components may be separately placed. However, various combinations of components may be comprised in a vial. Such containers may include injection or blow molded plastic containers into which the desired vials are retained. [0125] In some embodiments, the kit comprises a plasmid or episome that includes one or more of the expression construct components but lacks the gene of interest such that a user may with standard recombinant technology incorporate their gene of interest into the construct for desired purposes. In cases wherein the repressor is tetR, the kit may also include doxycycline or a functional derivative thereof. In cases wherein the kit comprises cells either with or without vectors that comprise the expression construct, the cells may or may not be cryopreserved, for example. EXAMPLES [0126] The following examples are included to demonstrate embodiments of the disclosure. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the methods and compositions of the disclosure. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the disclosure. EXAMPLE 1 MODELING SUGGESTS THAT NEGATIVE FEEDBACK AND INCOHERENT FEEDFORWARD CIRCUITS CAN FUNCTION SYNERGISTICALLY FOR DOSAGE COMPENSATION [0127] To guide the development of a more effective gene dosage compensation system, different control topologies were modeled and it was quantified how their circuit output varied as a function of plasmid copy number. An important performance metric that was considered was the range of plasmid copy numbers with effective gene dosage compensation, herein termed the compensation range. Type I incoherent feedforward (IFF) circuits (Fig.7a) were first evaluated because they have been shown to buffer gene dosage variation in both natural [17–20] and synthetic circuits [12, 14–16]. Inspired by previous studies, IFF circuits were focused on where inhibition is mediated by microRNA (miRNA)-based RNA interference [12, 14, 15]. A representative implementation was modeled of a circuit containing miR-FF4 (a synthetic miRNA with a strong affinity to its target sites [21–23]), miR-FF4 target sites, and the gene of interest (GOI) on the same transcript (Fig.2a). Following transcription, miRNAs are spliced out of a newly made precursor messenger RNA (pre-mRNA) and incorporated into RNA-Induced Silencing Complexes (RISC). The miRNA acts as a template for RISC to recognize and cleave mRNA molecules with miRNA target sites [24]. [0128] In agreement with previous studies, the deterministic simulations predicted that this IFF topology could compensate for gene dosage (Fig.2b, purple vs. black curves; see “Model description of four key topologies utilized to predict gene dosage compensation for model description” in Example 8). However, the model also predicted that RISC availability decreased sharply at high plasmid concentrations (Fig.2b, gray curve), consistent with previous reports of RISC saturation in the presence of high miRNA levels [25]. The absence of free RISC renders the IFF circuit inoperative, thereby limiting gene dosage compensation at high plasmid copy numbers (Fig.2b, purple curve at copy numbers >~ 102). [0129] To identify a topology that would compensate gene dosage across a broader range of plasmid copy numbers than miRNA-based IFF circuits, it was next considered that negative feedback (NF, Fig.7b) was also predicted to enable gene dosage compensation [12]. In one such NF circuit, the tetracycline repressor protein (TetR) is co-expressed from the same promoter as the gene of interest using a 2A ribosome-skipping sequence from porcine teschovirus-1 (Fig.2c, [26, 27]). By binding onto its cognate operator sites (tetO2) on the promoter, TetR represses both its own transcription and that of the gene of interest [28–30]. [0130] The model predicted that the NF circuit could effectively buffer gene dosage variation (Fig.2d, Ideal NF curves; see “Mathematical modeling of a transcriptional negative feedback circuit” in Example 8). Simulations further suggested that varying inducer (doxycycline) concentration regulates the dependence of dosage compensation on plasmid copy number (Fig.8a). For example, increasing doxycycline from 1 to 5 ng/mL improved dosage compensation at copy numbers >~ 30 at the expense of reduced performance at lower plasmid concentrations (Fig.8a). Overall, several inducer concentrations produced effective dosage compensation over a wide range of plasmid copy numbers. However, this circuit has been reported to deviate from ideal behavior due to incomplete repression at high TetR concentration when no inducer is present, leading to ”leaky” expression [29,31–33] (Fig.9). Simulations predicted that this incomplete repression narrows the compensation range (Fig.2d, Leaky NF curves; Fig.8b). [0131] It was considered that combining miRNA-based IFF and leaky TetR-based NF would widen the compensation range and simulated this combined architecture at different doxycycline concentrations (See “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8 for model description). The model predicted that this combination circuit could provide dosage compensation over 2-3 logs of plasmid copy numbers, outperforming both standalone IFF and NF topologies (Fig.2e & f, Fig.10a). Simulations further suggested that this combination circuit would provide improved dosage compensation compared with the IFF subcircuit over a wide range of RISC concentrations (Fig.10b-10c) and binding affinity between miRNAs and their targets (Fig.10d-10e). This circuit “Equalizer” was so named given its intended function to reduce gene expression variability between individual cells. EXAMPLE 2 EQUALIZER-L ACHIEVES AS LOW CELL-TO-CELL EXPRESSION VARIABILITY AS STABLE CELL LINES [0132] A series of experiments were conducted to demonstrate that the Equalizer topology can effectively compensate for variability in gene expression caused by differences in plasmid copy number among transfected cells. Modeling results predicted that tuning the binding affinity of the miRNA to its target sites could change the Equalizer’s compensation performance (Fig.10b). Therefore, two Equalizer variants were constructed with different miRNA/target pairs: Equalizer-M uses miR-FF4 while Equalizer-H uses miR-FF3, a miRNA with lower affinity to its target than miR-FF4 [34] (Fig.11a & 11b). Equalizer-L was also constructed, which encodes miR-FF4 like Equalizer-M but incorporates a second miRNA target site upstream of the start codon (Fig.11c, Fig.2e), an arrangement that can increase miRNA-based inhibition [23]. The Equalizer variants and control plasmids were constructed with the enhanced green fluorescent protein (EGFP) as circuit output reporter. [0133] Cell-to-cell variability in plasmid copy number naturally arises during transient transfection as plasmid uptake is stochastic [35]. Cells were identified that were successfully transfected with a spectrally compatible fluorescent reporter (i.e., mCherry RFP) expressed from a co-transfected plasmid (Fig.11e) or a separate cassette on the same plasmid as the circuit (e.g., Fig.11d). When present on the same plasmid, mCherry also served as a gene-dosage reporter: the mCherry fluorescence values were used to approximate the relative number of actively- expressing plasmids. [0134] HEK293 cells were transfected with Equalizer plasmids and measured single-cell fluorescence using flow cytometry. The coefficient of variation (CV) was utilized of EGFP fluorescence in mCherry+ cells to measure cell-to-cell variability in circuit output levels. Flow cytometry and microscopy produced similar CVs of circuit output, demonstrating that the CV is robust to differences in the method used to quantify single-cell fluorescence (Fig.12). Flow cytometry was utilized in ensuing experiments, given the high throughput of this technique. [0135] The model predicted that doxycycline could be used to tune Equalizers’ gene dosage compensation range and profile (Fig.10a), as previously shown with NF circuits (Fig.8). To identify the optimal inducer concentration for each Equalizer variant, the cell-to-cell output variability was quantified at several doxycycline concentrations from 0 to 30 ng/mL (Fig.3a). The shape of the CV dependence on inducer concentration was non-monotonic, with intermediate concentrations producing the lowest expression variability. At their respective inducer concentration, Equalizer-L produced the lowest cell-to-cell variation (CV = 71), followed by Equalizer-M (CV = 88) and Equalizer-H (CV = 110). The relative expression levels were quantified over the same range of inducer 132 concentrations (Fig.3b). Induction increased expression of all three Equalizers by a maximum of 4-8 fold. Equalizer-L produced lower expression variability but also lower expression. For example, Equalizer-H produced 5.2 and 22 times higher fluorescence than Equalizer-L at 0 and 30 ng/mL, respectively. [0136] The expression variability obtained above with Equalizers was compared to the variability produced by commonly used promoters without control circuitry (i.e., ”open-loop”), hereafter referred to as unregulated promoters. As expected, higher variation was observed with all unregulated promoters tested: the phosphoglycerate kinase (PGK, CV = 221), the ubiquitin C (UBC, CV = 220), and the cytomegalovirus (CMV, CV = 137) promoters. The greater variability produced by the PGK and UBC promoters than the CMV promoter may be due to increased burstiness of the weaker PGK and UBC promoters [36], saturation of gene expression due to limited cellular resources when using the strong CMV promoter, or both. [0137] It was determined to what extent the cell-to-cell variability observed with Equalizer circuits was due to residual dependence on plasmid copy number rather than other sources of variation such as intrinsic noise [36], differences in expression capacity [37], or measurement noise. A condition was generated without copy number variation by chromosomally integrating an EGFP expression cassette with the same promoter (CMV) as the Equalizer circuit. [0138] The variation produced by Equalizer-L and the CMV cell line were both similar (CV ~71), demonstrating the potency of Equalizer-L at buffering plasmid copy number variability (Fig.3c). Subsequent experiments were conducted solely with Equalizer-L because it was the most effective of the three circuit variants at buffering gene dosage. Henceforth, all experiments with Equalizer-L were performed at the doxycycline concentration producing the lowest cell-to-cell variation (1 ng/ml), unless otherwise noted. [0139] To evaluate whether Equalizer-L’s gene dosage compensation circuitry is functional in multiple cell types, Equalizer-L were tested in multiple commonly used mammalian cell lines derived from different species. Compared with the unregulated CMV promoter, Equalizer-L achieved lower cell-to-cell variability in all the cell types tested, including Neuro2A, a line of mouse neuroblasts; CHO-K1, a line of Chinese hamster ovarian cells; COS-7, a line of African green monkey kidney cells; and HeLa, a line of human cervical adenocarcinoma cells (Fig.3d). [0140] To confirm that the results were robust to the dose of transfected plasmids, expression heterogeneity was quantified following transfection with 1 to 200 ng of plasmids. The lower cell-to-cell variability of Equalizer-L was maintained across the entire range of plasmid doses (Fig.3e & 3f). The mean gene-dosage reporter values did not increase linearly with the plasmid dose, leading to a smaller range of gene-dosage reporter values (Fig.13a). [0141] The mean circuit output level of Equalizer-L was intermediate between those of the PGK and CMV promoters for 5 of the 6 plasmid doses (Fig.13b). Therefore, the lower output variability produced by Equalizer-L is not simply due to its weaker expression compared with the CMV promoter. For each plasmid dose, the mean values and the overall distribution of gene-dosage reporter levels were similar between all three circuits, demonstrating that the results were not due to differences in transfection efficiency or expression capacity (Fig.14a). [0142] The mean circuit output and the mean gene-dosage reporter values were computed for each plasmid dose. As expected, the resulting transfer curves showed that Equalizer-L compensates for increases in plasmid copy number at the population level. For example, over a 20-fold change in mean gene dosage, the mean circuit output levels of the Equalizer-L only increased 2.7-fold, compared with 15.7 and 10.4 for the PGK and CMV promoters, respectively (Fig.3g). [0143] Because the data was acquired by measuring individual cells, one could quantify circuit output over a wider range of gene dosages than when only considering population means. The single-cell data was pooled from experiments with each plasmid dose and quantified the mean circuit output of each 5-percentile bin of gene-dosage reporter values. In response to a 200- fold change in gene dosage, Equalizer-L circuit output increased ~ 4-fold compared with ~ 90- fold for PGK and ~ 50-fold for CMV (Fig.14b). Taken together, the studies demonstrate that the Equalizer-L robustly buffers plasmid copy number variation at both population and single-cell levels and produces output variation similar to chromosomal expression. EXAMPLE 3 NEGATIVE FEEDBACK AND INCOHERENT FEEDFORWARD LOOPS ACT SYNERGISTICALLY TO WIDEN THE GENE DOSAGE RANGE OF EFFECTIVE COMPENSATION [0144] Having established Equalizer-L as an effective gene dosage compensation circuit, it was experimentally confirmed that it outperforms the standalone NF and IFF subcircuits (Fig. 4a, Figs.15-16), as originally predicted (Fig.2, Figs.8 & 10). In this study, both the Equalizer-L and the NF circuit were induced with doxycycline at the concentration producing the lowest cell- to-cell variation (1 and 10 ng/ml for Equalizer-L and NF, respectively; Fig.4b). The same inducer concentrations were also used in the ensuing simulations. [0145] Computational modeling was utilized to understand how the NF and IFF subcircuits interplay within the Equalizer architecture. To this end, the initial models were refined by using the experimental results. Using the distribution of expression for unregulated circuits and mean expression levels of NF circuit, the distribution was estimated of plasmid copy number following transient transfection and the leakage parameter of TetR repression. Comparing simulated and measured mean expression for the cells expressing the Equalizer and the NF circuits, the miRNA affinity was estimated to its target sites (see “Estimating the distribution of plasmid copy numbers in transfected cells” and “Estimating miRNA affinity” in Example 8). To account for the contribution of intrinsic noise [36] to cell-to-cell variation, the inventors assumed that the expression variability of the CMV cell line (Figure 3d) was due solely to intrinsic noise. Intrinsic noise was assumed for the different circuits across all doxycycline concentrations (see “Predicting cell-to-cell variability” in Example 8). With these constraints in place, one could predict the cell-to-cell output variability with no free parameters. [0146] The simulation results closely matched the trends in cell-to-cell variability determined experimentally for the NF and Equalizer-L circuits in response to doxycycline (Fig. 4b). Simulations also accurately approximated the circuit output heterogeneity observed with the IFF circuit (Fig.4c). This close agreement between simulated and experimental values was consistent with the consideration that the Equalizer-L reduces cell-to-cell output variability primarily by buffering gene dosage variation rather than by reducing intrinsic noise. More generally, the close agreement between experimental results and simulations suggested that the model was suitable to study the gene dosage compensation properties of Equalizer-L. [0147] The model was used to predict the range of plasmid copy numbers over which Equalizer-L outperforms the NF and IFF subcircuits. The predicted plasmid copy number distribution was overlaid with the predicted circuit output as a function of the plasmid copy number for the IFF, NF, and Equalizer-L circuits (Fig.4d). The model predicted that the variability in plasmid copy number 204 between cells was wide, with 99% of transfected cells harboring between 1 and 432 plasmids (Fig.4d, gray curve). Equalizer-L was effective at buffering copy number variation across the entire range, although with reduced potency at very low (<~5) and very high (>~500) plasmid copy numbers (Fig.4d, Fig.17). In contrast, the NF circuit was limited by poor dosage compensation at both low and high plasmid copy numbers, while the IFF’s gene dosage compensation was predominantly impaired at high plasmid copy numbers. The shape of the predicted NF circuit output is different from that shown above (Fig. 2d, Leaky NF) due to a difference in inducer concentration. Fig.4b was simulated with the inducer concentration producing the lowest cell-to-cell output variability of the (leaky) NF circuit (10 ng/mL), while Fig.2d was generated using a doxycycline concentration optimized for the ideal NF circuit (1 ng/mL). [0148] In an ideal plasmid dosage compensation circuit, protein expression per plasmid is inversely proportional to the plasmid copy number. In log-log plots, this ideal scaling corresponds to a straight line with a slope parallel to the dotted lines depicted in Fig.4e-4g. Equalizer-L compensated for gene dosage at or near this theoretical ideal across a wider range of plasmid copy numbers than the NF and IFF circuits (Fig.4e). To determine which Equalizer-L subcircuit was responsible for gene dosage compensation in different plasmid copy number regimes, it was plotted how the predicted post-transcriptional and transcriptional rates varied across the estimated range of plasmid copy numbers. At low copy numbers (<10), dosage compensation was primarily provided by the IFF subcircuit of Equalizer-L (Figure 4f), with negligible contributions from the NF subcircuit (Figure 4g). At both intermediate (101- 102) and high (> 102) plasmid copy numbers, the IFF and NF loops acted synergistically to provide overall dosage compensation close to the theoretical ideal. [0149] The improved dosage compensation of the Equalizer-L at high plasmid copy numbers was due to stretching of both transcriptional and post-transcriptional dosage compensation curves compared with those of the NF and IFF circuits, respectively (Figure 4f & 4g). The predicted change in the post-transcriptional curve (Figure 4f) is consistent with the NF subcircuit reducing transcription of miRNAs and their targets, thereby delaying saturation of RISC until higher copy numbers (Fig.18). The shallower but wider transcriptional dosage compensation curve of Equalizer-L compared with the standalone NF circuit (Figure 4g) is consistent with the IFF subcircuit reducing TetR concentrations: TetR levels at which leakiness dominates are, therefore, only reached at higher copy numbers. EXAMPLE 4 GENE DOSAGE COMPENSATION OF EQUALIZER-L IS SUPERIOR TO AN ALTERNATIVE CIRCUIT THAT COMBINES MIRNA-BASED NF AND IFF TOPOLOGIES [0150] While the Equalizer circuits were developed and characterized, another NF-IFF hybrid circuit was reported [15]. This system, called HYB, was also proposed to compensate for plasmid copy number variation. Although both HYB and Equalizer-L combine NF and IFF topologies, there are important differences in their implementation (Figures 5a & 5b). First, while Equalizer-L expresses all the circuit components using a single promoter, HYB utilizes two promoters. Second, while Equalizer-L uses miRNAs solely in its IFF subcircuit, HYB utilizes miRNAs to regulate both its NF and IFF subcircuits. Third, the implementation of NF differed between the two circuits. In Equalizer-L’s NF loop, TetR directly represses the expression of its own gene and the circuit output. In contrast, HYB’s NF loop is mediated 239 by miRNA-based repression of a transactivator that increases the expression of the output protein and the miRNA itself. Finally, the HYB circuit also includes a coherent feedforward loop since both the circuit output and its transactivator are encoded on the same plasmid. The Equalizer-L plasmid neither encodes a transactivator nor incorporates a coherent feedforward loop. [0151] The differences between these two circuits presented a unique opportunity to evaluate how gene dosage performance could be affected by circuit design choices. It was noticed that the two systems used different fluorescent protein reporters, thereby complicating their comparison. Therefore, the Equalizer-L and HYB plasmids were modified to express the same reporter: the red fluorescent protein mScarlet-I [38] as the reporter of circuit output, and the yellow fluorescent protein mCitrine [39] as the reporter of plasmid dosage. These fluorescent proteins were chosed because of their monomericity, high brightness, and fast maturation time [38–42]. The same modifications were applied to the unregulated controls: CMV for Equalizer-L and OLP for HYB (Fig.5c & 5d). The following experiments were conducted in HEK293T cells because this cell line was used in the original study that reported HYB [15]. [0152] It was first confirmed that the above modifications did not impact the gene dosage capacity of Equalizer-L and that the optimal gene dosage compensation was still achieved with 1 ng/mL of doxycycline (Fig.19a). It was evaluated how the cell-to-cell variability and circuit output produced by the HYB plasmid varied with doxycycline concentration. HYB produced the lowest cell-to-cell variability in the absence of inducer (Fig.19b). Therefore, doxycycline was not used in subsequent experiments with HYB and OLP. HYB also produced the highest circuit expression when no inducer was added, as expected for a system that is repressed by doxycycline (Fig.5b). [0153] In contrast with expression from Equalizer-L and CMV, expression from HYB and OLP was largely bimodal and more steeply dependent on the plasmid dosage (Fig.5e). Increasing the plasmid dose increased the proportion of cells in the high-expression peak. The distributions of gene-dosage reporters were not bimodal and, therefore, could not explain the bimodality of the HYB and OLP circuit output distributions (Fig.20a-20b). Instead, the observed bimodality may have occurred because the tetracycline transactivator (tTA) is encoded on the same plasmid as its cognate promoter (TRE). Since the TRE promoter used in both OLP and HYB is highly sensitive to transactivator level (Hill coefficient ~ 3.2 [32]), a modest change in plasmid concentration could enable cells to cross the threshold necessary for TRE activation. [0154] Different plasmid doses were transfected and expression heterogeneity was quantified. Equalizer-L reduced cell-to-cell variability to similar levels as previously observed when using EGFP as the circuit output reporter (Figs.5f & 3). However, HYB produced similar expression variability as OLP. The resulting mean circuit output levels were quantified of populations of cells as a function of mean gene-dosage reporter levels. Equalizer-L showed excellent gene compensation, with only ~1.2-fold increase in the mean circuit output in response to a ~60-fold increase in apparent gene dosage (Fig.5g). In contrast, while the mean output of HYB had a weaker dependence on gene dosage than OLP, HYB’s mean output levels remained nearly proportional to the change in gene dosage (Fig.5h). Gene dosage compensation was quantified at the single-cell level. HYB produced a ~180-fold change in expression in response to a ~100-fold increase in plasmid dosage, lower than the 320-fold change observed with OLP (Fig.20c). In comparison, over the same range, Equalizer-L increased only by ~3-fold. CMV increased by ~14-fold, producing a non-linear response to gene-dosage reporter values. [0155] Several control experiments and analyses were conducted to further characterize that Equalizer-L provides superior dosage compensation than HYB. To determine whether the poor dosage compensation of HYB is due to the lower-expressing subpopulation in its bimodal distribution, the inventors reanalyzed results considering only higher-expressing cells from experiments with HYB or OLP. Similar results were obtained as above for both population and single-cell assays (Fig.20d-20f). Similar cell-to-cell variability was also obtained with the original (unmodified) HYB and OLP plasmids, demonstrating that the results are not due to changing the output protein from DsRed-Express to mScarlet-I (Fig.20g). HYB and OLP had comparable expression heterogeneity in CHO-K1 cells, showing that the results extend to other cell types than HEK293T (Fig.20g). [0156] The previously-reported finding was replicated that HYB has a lower Fano factor than its corresponding unregulated promoter, OLP (Fig.20h) [15]. However, Fano factors are not easily interpretable when comparing distributions with different means (see “Comparing the coefficient of variation and the Fano factor as measures for quantifying cell-to-cell variability in the experiments” in Example 8). For example, the PGK promoter produced a lower Fano Factor than the CMV promoter (Fig.20i) despite producing larger CV values in the evaluations of cell- to-cell variability (Fig.3f) and a nearly linear dependence on gene dosage (Figure 3g). The inventors also replicated the finding that HYB produces a flatter curve than OLP when these circuits are evaluated by plotting unnormalized circuit output values on a linear axis [15] (Fig. 20j, left). However, evaluation of gene dosage compensation using unnormalized values can be misleading, as weaker promoters will also produce flatter curves when plotted in this manner. For example, the dependence of circuit output on gene dosage appeared similar between Equalizer-L and the (weak) PGK promoter, despite PGK not compensating for gene dosage (Fig. 20j, right). Taken together, the results show that Equalizer-L has superior gene dosage compensation capacity at the population and single-cell levels compared with HYB. EXAMPLE 5 A REPLICATING VARIANT OF EQUALIZER-L ENABLES SIMPLE, RAPID, AND VERSATILE DEVELOPMENT OF EXTRACHROMOSOMAL CELL LINES WITH LOW CELL-TO-CELL EXPRESSION VARIABILITY [0157] Transient transfection with most expression plasmids is only suitable for experiments lasting up to a few days: expression levels and the proportion of expressing cells peak on day 2 or 3 post-transfection and are substantially reduced by days 5 and 6 [43]. However, some plasmids – called episomes – can replicate in mammalian cells. [0158] Episomes enable persistent gene expression and are compatible with many cell types [44]. However, episomes are expected to suffer from high cell-to-cell variability in circuit output as they undergo the same transfection process as non-replicating plasmids. It was reasoned that incorporating Equalizer-L in an episome would combine the simplicity and versatility of plasmid expression with the potential for long-term experiments with low expression heterogeneity that normally requires chromosomal expression. [0159] To develop an episomal version of Equalizer, plasmids were repurposed that are derived from the Epstein-Barr virus and that replicate synchronously with the cell cycle [45] at a copy number between 1 and 100, depending on the cell type [45,46]. Plasmid replication depends solely on two viral sequences: an origin of replication called oriP and the oriP-binding nuclear protein EBNA-1 [45,47]. oriP-bound EBNA-1 also tethers plasmids to chromosomes, both to prevent plasmid 311 loss during mitosis [48] and to promote replication [49]. An Equalizer episome was constructed by subcloning Equalizer-L and the gene-dosage reporter onto a plasmid with oriP and EBNA-1 (Fig.22). [0160] Next, the ability was evaluated of the Equalizer-L episome to maintain constant gene expression and low cell-to-cell expression variability for multiple weeks. The inventors transfected episomes in HEK293 cells and grew the cells for two months. The inventors quantified the fluorescence of individual cells every 1-2 weeks using flow cytometry. The boundaries between expressing and nonexpressing cells were defined using untransfected cells and control cultures expressing a single fluorescent protein (Fig.23). Representative images were taken of the cells under fluorescence microscopy (Fig.24). In the absence of selection, plasmid loss is reported to be between 2 and 5% per generation [45]. The episome expresses a hygromycin B resistance gene, and the emergence of plasmid-free cells were prevented by using growth media with antibiotics starting 1 day after transfection. [0161] For the entire duration of the two-month experiment, cells expressing the Equalizer-L episome had indistinguishable cell-to-cell variability from a cell line expressing a chromosomally-integrated CMV expression cassette (Fig.6a & b, Fig.24). The average circuit output also remained relatively constant, similar to what was observed with the (chromosomal) CMV cell line (Fig.6c, Fig.26a). The cell-to-cell variability observed with episomes expressing the CMV or PGK promoters (Fig.22b & 22c) was higher throughout the experiment. From day 9 to day 60 post-transfection, the CMV and PGK episomes also produced 70% and 82% decreases in gene expression, respectively (Fig.6c). Expression from the gene-dosage reporter also decreased by 53-81% between day 9 and day 60 for all plasmids (Fig.26b & 26c). These changes likely reflect a decrease in copy number due to imperfect plasmid replication: while antibiotic selection prevents the growth of plasmid-free cells, a reduction in the number of plasmids per cell can occur. These presumed changes in the copy number distribution may explain why the cell-to-cell output variability of the CMV and PGK episomes decreased over the two months of the experiment (Fig.6a). However, because gene-dosage reporter values were low on several days, one could not accurately quantify changes in plasmid copy number distributions. As predicted, the Equalizer-L episome buffered these fluctuations, producing circuit output that remained largely invariant over the same timescale (Fig.6c). [0162] Despite the presence of an RFP gene-dosage reporter on all episomes, all cultures showed a significant fraction of cells with detectable circuit output (GFP+) but undetectable gene-dosage reporter values (RFP-) (Fig.25). These apparent GFP+ RFP- cells may result from imperfect detectability of the RFP (mCherry) at lower plasmid concentrations, from silencing of the EF1-promoter driving the gene-dosage reporter [50] or, less likely, from genomic instability [51]. The fraction of GFP+ RFP- cells was particularly high in the CMV episome culture, where they accounted for 32-71%of GFP+ cells. It was considered that the strong CMV promoter created stronger selective pressure for lower plasmid concentrations, reduced available cellular machinery available for expressing the gene-dosage reporter, or both. Consistent with these explanations, the mean RFP expression of RFP+ cells was ~ 1.7-4.4-fold lower than that observed with the weaker-expressing PGK and Equalizer-L episomes (Fig.26b). While only RFP+ cells were analyzed above (Fig.6a & c, Fig.26a-26c), including GFP+ RFP- cells in the analyses resulted in similar trends in cell-to-cell expression variability and mean circuit output (Fig.26d & 26e). Regardless of the emergence of these subpopulations, most cells robustly expressed Equalizer-L for 60 days. EXAMPLE 6 SIGNIFICANCE OF CERTAIN EMBODIMENTS [0163] A critical goal of synthetic biology is to design systems with predictable functions [8]. However, uniform expression of even single genes across a population of mammalian cells remains challenging. As variations in plasmid copy number is a key factor driving expression heterogeneity following transient transfection, approaches to reduce the dependence of expression on the abundance of encoding genetic material are needed [8,9]. To this end, this study reports new mammalian genetic circuits, called Equalizers, that buffer circuit output from variation in plasmid copy number. Cell-to-cell expression variability with Equalizers is equivalent to that observed in cell lines that harbor chromosomally integrated reporters and are thus not subject to plasmid copy number variation (Figs.3c, 6a). Robust gene dosage compensation was displayed at both the population and single-cell levels, in multiple cell lines (Fig.3d), and across a wide range of transfected plasmid doses (Figs.3e-3g, 5). [0164] When encoded on an episome, Equalizer-L enables stable expression over multiple weeks of growth with as low cell-to-cell variability as chromosomal cell lines (Fig.6). Episomal cell lines can be generated by simple transfections, followed by a short period of antibiotic selection, and are compatible with a wide array of cell types [52–55]. Therefore, this method is rapid, versatile, and accessible to all labs without specialized skills in chromosomal integration techniques. After forty (PGK) or sixty (Equalizer-L) days post-transfection, the emergence was observed of cells that were no longer expressing the reporter of circuit output (EGFP) despite continued expression of mCherry, the reporter of plasmid dosage (Fig.25). This decrease in circuit output may be due to silencing of the CMV promoter used in Equalizer-L, as previously reported [56]. Alternative promoters or CMV variants [57] that are less prone to silencing should be evaluated for experiments lasting longer than 1.5-2 months. [0165] The results highlight how the non-ideal behavior of circuit components must be considered when designing gene circuits. For example, incomplete repression of gene expression at high TetR concentration was predicted to strongly impair gene dosage compensation of the NF-only circuit at high plasmid copy numbers (Fig.2d, Fig.8). Limitations in cellular resources are another design consideration; for example, simulations suggest that miRNA-based IFF circuits are limited at high plasmid copy numbers due to low availability of free RISC (Fig.2b, Fig.18). The Equalizer circuits achieve robust performance by combining two imperfect subcircuits, each with distinct limitations and complementary gene dosage compensation ranges (Fig.4d-4g). [0166] The results also illustrate that simply incorporating NF and/or IFF loops is insufficient to reduce expression heterogeneity following transient transfection. For example, despite encoding both an NF and an IFF subcircuit, the HYB plasmid [15] did not reduce overall cell-to-cell variability (Fig.5f, Fig.20f & 20g). A contributing factor may be the dependence of expression on a transactivator located on the same plasmid. The resulting coherent feedforward loop is expected to amplify the existing dependence of gene expression on copy number. Consistent with this hypothesis, the mean circuit output from cells with the OLP plasmid increased faster than the change in apparent gene dosage 383 (Fig.5h). A second factor may be that HYB and OLP express their circuit components using two promoters, a configuration that is predicted to increase intrinsic noise [58,59]. Consistent with this explanation, a variant of the Equalizer-L where TetR, the miRNA, and the reporter gene are expressed from separate promoters produced higher cell-to-cell variation despite deterministic simulations predicting identical gene-dosage capacity (Fig.21). Finally, additional cell-to-cell variation may have been caused by plasmid replication: the inventors found that the SV40 promoter encoded by HYB and OLP also includes the SV40 origin of replication. This origin is thought to be mediate plasmid replication uncoupled from the cell cycle [44] in HEK293T – the cell line used here [60] and in the original report of HYB [15]. The vectors used for transient transfection of Equalizer-L or CMV are not replication-competent, and those used for stable transfection can replicate synchronously with the cell cycle [45]. [0167] Further engineering of dosage-compensation systems may benefit from studying natural biological systems. In these systems, gene dosage or concentration changes can occur due to gene loss or duplication, chromosome replication during mitosis, and changes in volume during cell growth, etc. Compensation motifs have been postulated or demonstrated to buffer gene dosage [17–20,61,62]. These motifs could be repurposed into the next generation of gene dosage compensation circuits. However, simulations where Equalizer-L components were rewired to mirror the topology of one of these natural circuits, the yeast galactose pathway [17– 19], predicted a reduction in dosage compensation (Fig.27). Nevertheless, further exploration of natural gene dosage motifs could help develop new and improved Equalizers. [0168] In summary, synthetic circuits are provided that near-perfectly buffer variation in plasmid copy number between individual mammalian cells. The system is a simple-to-use, robust, and versatile solution to achieving uniform gene expression at the single-cell level. EXAMPLE 7 EXAMPLES OF METHODS [0169] Plasmid construction [0170] All new plasmids were generated using standard molecular biology methods and were verified by sequencing. Plasmids used in this study are available from Addgene (169367, 169731-169735, 169737-169748, 170041) and their sequences are available from GenBank (MW962296-MW962297, MW987521-MW987537). pDN-D2ir mCherry P2A TetR:EGFP was obtained from D. Nevozhay & G. Bala´zsi and was used to amplify tetR and its cognate tetO2 binding site. pTRE-Tight-BI-DsRed-miR-FF3/tgt-FF3-AmCyan-FF3 [14] were used to amplify miR-FF3 and their binding sites. miR-FF4 was cloned with miR-FF3 as template and using two long primers 5’- ACATCTGTGGCTTCACTATTTAATTAAAGACTTCAAGCGGCGCTCACTGTCAACAGC AC-3’ (SEQ ID NO:5) and 5’- TGAAGCCACAGATGTATTTAATTAAAGACTTCAAGCGGTGCCTACTGCCTCGGAGAA TT-3’ (SEQ ID NO:6) that modified the core sense/antisense sequence from miR-FF3 to miR- FF4 [34]. pCEP4-CXCR4 was obtained from Addgene (Plasmid #98944) and was used to subclone the episome plasmids. HYB (pGLM127) and OLP (pGLM130) plasmids were previously described [15] and were obtained from Dr. M. Khammash. The miR-FF4 used in HYB and OLP circuits had several nucleotide differences to the miR-FF4 [34] which was used a as reference to build the circuit plasmids. The mutations were c.1T>A;4A>T;5G>A;35C>T. Some of the unregulated promoter constructs have different 5’ and 3’ UTRs. These differences have minimal impact on cell-to-cell variation. Schematics of plasmid constructs are in Figs.11 & 22 and the entire list of plasmids used in this study is in Table 4. [0171] Cell lines [0172] The Flp-InTM 293 (RRID:CVCL U421, Thermo Fisher Scientific) cell line was primarily used in the study. In the text, this cell line was called as HEK293 for simplicity. Other mammalian cell lines used in this study were HEK293A (RRID:CVCL 6910, Thermo Fisher Scientific), CHO-K1 (CCL-61, ATCC), HeLa cells (CCL-2, ATCC), HEK293T (CRL-3216 ATCC), COS-7 (CRL-1651, ATCC), and N2A (CCL-131, ATCC). These cells lines were free of mycoplasma contamination. All the cell lines, except CHO-K1 cells, were maintained in high- glucose Dulbecco’s Modified Eagle Medium (DMEM, D1145, Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS, F2442, Sigma-Aldrich), 2 mM glutamine (G7513, Sigma- Aldrich), and 100 unit/mL penicillin-streptomycin (P4333, Sigma-Aldrich) at 37 °C in air with 5% CO2. The growth media described above was considered as fully supplemented DMEM hereafter. It was confirmed with the manufacturer that the FBS did not contain any residual doxycycline. For the culture media for HEK293 cells, ZeocinTM (100 µg/mL, R25005, Thermo Fisher Scientific) was added to the fully supplemented media. CHO-K1 cells were cultured using DMEM/Nutrient Mixture F-12 (11320033, Thermo Fisher Scientific) supplemented with 10% FBS, 2 mM glutamine, and 100 unit/mL penicillin-streptomycin. The creation and maintenance of episomal cell lines are described in a separate section below. [0173] To generate the cell line that expressed the reporter of circuit output (i.e. EGFP) from the chromosome, the Flp-InTM system was utilized. Flp-InTM 293 cells (i.e. HEK293 cells) were plated in a 6-well plate for a confluence of 70% one hour before transfection. Cells were then co-transfected (using 6:1 mass ratio, respectively) with pOG44 plasmid (V600520, Thermo Fisher Scientific) and an unregulated CMV expression plasmid encoding EGFP and having FRT sites flanking the expression cassette. A total 4.5 µg of plasmid DNA was added to each well with 155 µL of Opti-MEMTM (11058021, Thermo Fisher Scientific) and 13.5 µL of FuGene HD (E2311, Promega, Madison, WI).24 hours after transfection, fully supplemented medium in each well was replaced to reduce the possible cytotoxicity caused by the transfection reagents. 48 hours after after transfection, medium was removed from each well and replenished with fresh medium containing 100 ng/µL hygromycin B (10687010, Thermo Fisher Scientific). Same medium was replaced every 2-3 days until attached colonies could be identified and grew to 70 to 80 % confluency. Cells were then passaged to a 10-cm culture dish or stored in liquid nitrogen for future use. The EGFP expression plasmid used for the genome integration had the same promoter and 5’ UTR as those of the Equalizer, the IFF, and the NF plasmids. [0174] Transient transfection [0175] Transfections were carried out using FuGene HD according to the manufacturer’s instructions (0.3 µL reagent:100 ng DNA per well for 96-well plates). [0176] For flow cytometry experiments, cells were transfected in glass-bottom 96-well plates (P96-1.5H-N, Cellvis). 3 hours before transfection, the plates were coated with 60 µL per well of 0.1 mg/mL of poly-L-lysine and incubated for an hour. After removing the poly-L-lysine, the wells were washed with 1x Dulbecco’s Phosphate-Buffered Saline (DPBS) without calcium and magnesium (21-031-CV, Corning). 70 µL of cells in fully supplemented DMEM were then plated in each well to achieve ˜60% confluency. The plates were incubated a 37°C with 5% CO2 air for one to two hours to promote cellular attachment prior to transfection. Among the 100 ng of plasmid DNA transfected per well, 50 ng were circuit plasmids, and the other 50 ng were transfection dosage control plasmid that encoded a fluorescent protein with minimal overlap (i.e. mCherry) with the reporter fluorescent protein (EGFP) encoded on the circuit plasmids. This control plasmid does not contain any TetO binding sites or miRNA targets sites and thus expression of the mCherry is not under control of the Equalizer. For most experiments, the mCherry expression cassette was cloned into the circuit plasmids. In this case, 50 ng of circuit plasmid with the onboard mCherry expression cassette and 50 ng of empty vector plasmid (that did not encode any genes) were used per well. For studies that the transfecting plasmid doses were varied, the inventors used 1 to 200 ng of circuit plasmids. Appropriate amount of empty vector plasmid was added so that the total transfecting plasmid amount was 200 ng per well. [0177] For each well, plasmid DNA was mixed with Fugene (with 100 ng to 0.3 µL ratio) in 12.5 µL Opti-MEM. After incubating the mixture at room temperature for 6 to 8 minutes, 27 µL per well of fully supplemented DMEM was added. 30 µL of the resulting mixture was added to the wells that had 70 µL of cell suspension. The plate was gently shaken to ensure the reagents were well mixed. For inducible constructs (Equalizers or NF circuit), two to four hours after transfection, 50 µL of doxycycline diluted in fully supplemented DMEM was added to achieve the desired inducer concentrations.50 µL of fully supplemented media without doxycycline was added to wells that did not require induction. [0178] Transfection of episomal plasmids and cell culture of episomal cell lines [0179] HEK293 cells were plated in a 6-well plate at a confluency of 70% an hour before transfection. For each well, a transfection mixture was prepared by mixing 2000 ng of episomal plasmid (Equalizer or unregulated promoter) with 50 µL of Opti-MEM. Then, 6 µL of Fugene was added to the mixture and incubated at room temperature for 6 to 8 minutes. After the incubation, the transfection mixture was added to the plated cells. Twenty four hours after transfection, the transfected cells on the 6-well plate were detached and replated on two 96-well plates: one for imaging and another for flow cytometry. For the imaging plate, cells were plated at a confluency of 20 to 30% and for the flow cytometry plate, cells were plated at a confluency of 40 to 50%. The cells in the 6-well plate were also passaged to another 6-well plate at a confluency of 30% to maintain the episome cell cultures. The episome cells cultures were grown and maintained as described above for the entire duration of the experiment. Cells were passaged twice per week and during every passage fresh hygromycin B (50 ng/µL) was replenished to select for and maintain the cells transfected with the episomal plasmids. Note that the epsisomal plasmids express a hygromcyin B resistance gene (hph). Among the cells that were plated in 96- well plates, cells that were transfected with the Equalizer-L episome were induced with 1 ng/mL doxycyline an 2 to 4 hours after plating. 48 hrs after induction, culture medium in the imaging plate was replaced with Hanks’ Balanced Salt Solution (HBSS, H8264, Sigma-Aldrich) and cells were imaged using two-photon microscopy setup described in the Fluorescence microscopy section below. The cells in the flow cytometry plate were prepared and analyzed as described in the Flow cytometry section below. Every week or two weeks for two months, the episome harboring cells were plated on the 96 well plates for imaging or flow cytometry. [0180] Flow cytometry [0181] Thirty six to forty eight hours after transfection, cells were detached using trypsin (T3924, Sigma-Aldrich) and washed twice with 1x DPBS without calcium and magnesium. Detached cells were resuspended in 1x DPBS without calcium and magnesium and deposited into 96-well deep well plates. Attune NxT Acoustic Focusing Cytometer with the Autosampler (ThermoFisher Scientific) was used to measure the fluorescence of individual cells. The following lasers and emission filters were used: for mCerulean, a 405-nm laser and a 440/50-nm emission filter; for EGFP 601 and mCitrine, a 488-nm laser and a 530/30-nm emission filter; for mCherry, DsRed-Express, and mScarlet-I a 561- 602 nm laser and a 620/15-nm emission filter. For each sample, 5000 to 10,000 cells were typically measured. Cells 603 expressing one type of FP (single-FP controls) were prepared to compensate for bleed-through between the color 604 channels. For the episomal Equalizer-L experiment (Fig.6) that involved sampling of cells on multiple days for a 605 two-month period, stable fluorescent beads (RFP-38-5, Spherotech) were measured to ensure that the optical setup 606 of the flow cytometer was the same throughout the entire duration of the experiment. [0182] Microscopy [0183] Thirty six to forty eight hours after transfection, cells were washed once with 1x DPBS without calcium and magnesium. The media was then switched to 100 µL/well of Hanks’ Balanced Salt solution supplemented with 10 mM HEPES. Cells were then imaged with an A1R MP+ microscope (Nikon Instruments) fitted with a 20x 0.75-NA dry objective and driven by the software NIS-Elements version 4.6 (Nikon Instruments). Two-photon microscopy (2PM) was used to image cells with a shallower depth of focus, to reduce apparent variation in fluorescence due to height differences between cells.2PM experiments used a galvanometric mirrors to steer a titanium:sapphire Chameleon Ultra II laser (Coherent). GFP was excited with 920-nm light. The emission light was filtered by a 525/50 nm filter and collected using a gallium arsenide phosphide (GaAsP) detector. For two-photon laser-scanning experiments, laser power and gain were adjusted for different constructs so that the brightest pixels were below pixel saturation. [0184] For Fig.24, the replicating Equalizer-L image was acquired with 5% Gain, 30% Power, 6.2 µs dwell time, and 2x averaging; the open-loop images were taken with 1% Gain, 5% Power, 6.2 µs dwell time, and 2x averaging. [0185] For wide-field one-photon microscopy experiments, GFP was excited with 470/20 nm light (SpectraX, Lumencore). Emission light was collected by a scientific CMOS camera (Flash4 v2+, Hamamatsu) after passing through a Multiband Filter (Spectra-X, 77074159). [0186] Data processing [0187] Flow cytometry data was analyzed using FlowJo (version 10.6.1, BD). Forward and side scatters were used to gate singlet cells. Among the singlet cells, only the transfected cells were used for analysis unless mentioned otherwise. Circuit output levels of individual cells were evaluated using the fluorescence levels of reporter fluorescent proteins (EGFP or mScarlet- I) expressed by the circuit plasmid. mCherry or mCitrine expressed from an independent expression cassette was used to determine transfected cells by gating for cells that show higher mCherry or mCitrine fluorescence than the baseline non-transfected cells. mCherry and mCitrine fluorescence levels of individual cells were also used to estimate the active-plasmid copy number (i.e.gene dosage) inside the transfected cells. The CV values of EGFP or mScarlet-I fluorescence distributions were used to measure the cell-to-cell variability in circuit output. CV values were calculated by dividing the SDs of fluorescence distribution of cell populations by mean fluorescence value of the population. To evaluate gene dosage compensation at a population-level, cells were transfected with different plasmid doses to vary the average plasmid copy number inside the transfected cells. It was then determined how the mean circuit output levels varied as a function of mean active-plasmid copy number. The population-level gene dosage compensation was evaluated by pooling the single-cell data points of cells transfected with different plasmid doses (e.g. Fig.20c). Pooled data was divided into bins with equal data points. Mean circuit output and plasmid dosage values of the bins were obtained and plotted. For all experiments, normalization of mean values were conducted when appropriate and the details of normalization are noted in the figure captions. [0188] MATLAB (version r2019b, MathWorks) was used for quantitative (e.g. Fig.12) and qualitative (e.g. Fig.6b)assessment of images. For quantitative analysis, image segmentation was conducted using ilastik [65] to distinguish the cells from the background. Smoothing and background subtraction were applied on the raw images. Segmentation masks were then used to evaluate the fluorescent protein intensity of individual cells. The mean, standard deviation, and CV values of fluorescent protein intensities of segmented cells were calculated. Each field-of- view had 200 - 1000 cells. Fields-of-view with saturated pixels were removed from analysis. For qualitative analysis of fluorescence images, image segmentation was first conducted, as mentioned above, to obtain the means and standard deviations of fluorescence intensities of cells in the fields-of-view. The inventors then systematically set the lookup table (LUT) boundary for each field-of-view so that the boundary was centered around the mean fluorescence intensity of the cells in the field-of-view. More specifically, for each field-of-view, the inventors set the lower bound to zero and the upper bound to mean fluorescence value plus three times the standard deviation value. The masks shown in the figures were generated by thresholding. Note that these masks were not used for image segmentation, but simply to visualize regions of the images that corresponded to cells. [0189] MATLAB and Prism (version 9.0.1, GraphPad) were used to conduct basic calculations, generate plots, and conduct statistical analysis. [0190] Statistical analysis [0191] Statistical analysis was conducted to compare the mean cell-to-cell variability or circuit output values of n = 3 to 9 independent transfections. When comparing the means of two groups, the inventors performed the unpaired two-sided t-test. For experiments that compared the means of more than two groups, the inventors used the ANOVA. Prior to the t-test, one-way ANOVA and two-way ANOVA, the inventors conducted the F-test, Brown-Forsythe test, and Spearman’s test, respectively, to compare the variances of the groups. When the variances were statistically different, the Welch’s correction was applied when appropriate. Because normality tests have low power when the sample size (n) is small [66], the inventors did not conduct normality tests and assumed normality. For one-way and two-way ANOVAs, the inventors conducted post-hoc multiple comparison tests (Tukey, Sidak or Dunnett). In the figures, p-values are annotated as: *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. [0192] Computational modeling [0193] MATLAB and MATLAB Simbiology were used for modeling and deterministic simulations of the study (see Example 8). Stochkit2 [67] was used for stochastic simulation (see “Comparing intrinsic noise between Equalizer-L and multi-promoter Equalizer-L”). Model reactions and assumptions are listed in Example 8 Simulation parameters are included in Tables 1-3. Simulations in Fig.2 used 1ng/mL doxycycline for the ideal NF circuit, the leaky NF circuit, and Equalizer. Simulations in Fig.4d-g used 1ng/mL doxycycline for the 10 ng/mL for the NF circuit, and 1 ng/mL for Equalizer-L. Fig.2b & 2f used miRNA dissociation rate constant of 0.3 second-1 for the IFF circuit and Equalizer as an in silico proof of concept, before Equalizer was experimentally tested. Unless specified otherwise, parameter values listed in Tables 1-3 were used in the simulations. [0194] Gene dosage compensation was predicted using the inverse of log sensitivity of steady-state protein concentration to DNA copy number by varying the copy number by ±1 plasmid. In other words, this measure is the ratio of relative changes in gene dosage and relative change in gene expression. The higher this number, the better the circuit can maintain the same protein expression with changes in gene dosage. For instance, dosage compensation of 4 implies that a 100% (i.e.2x) increase in gene dosage will lead to approximately 100%/4 = 25% increase in expression. To calculate the log sensitivity, each Simbiology circuit model was ran to steady state at individual copy number, and the log sensitivity at each copy number was calculated using numerical differentiation with second-order schemes (keeping values of DNA copy number, CN, integer). For the copy number 1, a second-order forward finite difference was used to approximate the local log sensitivity ([POI] denotes steady-state protein concentration): [0195]
Figure imgf000054_0001
Figure imgf000054_0002
Figure imgf000054_0003
[0196] All computational and experimental data regarding NF topology shown in the study refer to the Equalizer (-L, -M, or -H) without the miRNA, its flanking splice sites and its target(s). For modeling the Equalizer and NF circuits, different inducer concentrations were supplied in the initial conditions to identify the optimal inducer concentration that produces the lowest log sensitivity. [0197] While leakiness of each inducible construct can be conceptualized as the ratio of the expression level when no inducer was added to the maximum expression achieved by adding a saturated amount of inducers, it depends on the number of plasmids in a cell, because cells with different plasmid copy number will have different TetR concentrations without inducers, leading to different basal transcription rate per plasmid. Leakiness is considered in the modeling as the leakage parameter, as described in the end of “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8. See “Estimating the distribution of plasmid copy numbers in transfected cells” in Example 8 for the estimation of leakage value. [0198] To approximate the miRNA-target affinity used in the models, the mean expression level of ten thousand cells with a fitted plasmid copy number distribution (see “Estimating the distribution of plasmid copy numbers in transfected cells” in Example 8) was simulated with the Equalizer model (topology 4 in “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8) and the NF model (topology 2 in “Model description of four key topologies utilized to predict gene dosage compensation” in Example 8) across doxycycline concentration of 0, 1, 5, 10, 50, 100 ng/mL. MATLAB’s fmin search function was used to find the miRNA-target affinity that produces smallest mean squared error of the simulated mean expression ratio of the Equalizer model and the NF model compared with experimental data (see “Estimating miRNA affinity” in Example 8 for details). [0199] Data Availability [0200] Annotated plasmid sequences are available from GenBank (Accession numbers: MW962296-MW962297, MW987521-MW987537) and Addgene (#169367, 169731-169735, 169737-169748, 170041). [0201] Code Availability [0202] MATLAB SimBiology models of gene dosage compensation topologies are provided on Github: https://github.com/stpierrelab/Equalizer. EXAMPLE 8 SUPPORTING EMBODIMENTS FOR THE SYNTHETIC CIRCUIT FOR BUFFERING GENE DOSAGE VARIABILITY BETWEEN INDIVIDUAL MAMMALIAN CELLS [0203] Model description of four key topologies utilized to predict gene dosage compensation [0204] The inventors used deterministic simulations to predict the gene dosage compensation abilities of the five circuit topologies: (1) unregulated (i.e., open-loop constitutive expression) topology (Fig.29a), (2) TetR-based negative feedback topology (Fig.29b), (3) miRNA-based incoherent feedforward topology (Fig.29c), and (4) Equalizer circuit which is a hybrid of negative feedback and incoherent feedforward topologies (Fig.29d). (5) Equalizer circuit that separately expresses the circuit components using multiple promoters (Fig.29e). Parts of the models were adapted from Nevozhay et al. [1] and Bleris et al. [2]. All reactions are listed below and follow mass action kinetics unless otherwise stated; the reactions that do not follow the mass action kinetics are underlined. The kinetics of these reactions are described by Equations 5-6 elsewhere herein. [0205] The parameters for mass action kinetics are listed in Table 1. The transcription reaction of topologies 2, 4, and 5 (as seen in Fig.29) did not follow mass action kinetics, but instead followed the reactions listed below: [0206]
Figure imgf000056_0001
[0207] As shown in Equation 2, the inventors used a similar mathematical formulation as Siciliano et al.[3] to describe leakiness of the Tet system. Note that the leakage parameter cannot be directly calculated by the fold change between mean expression with and without induction, as cells with different plasmid copy number will have different TetR concentrations without induction, and the assumption that [TetR] is much larger than the half-saturation constant K50 may not always hold (so
Figure imgf000056_0002
may not be zero when there is no induction for cells with low plasmid copy number). See “Estimating the distribution of plasmid copy numbers in transfected cells” below for the estimation of the leakage parameter. [0208] Mathematical modeling of a transcriptional negative feedback circuit [0209] A mathematical model was built of an negative feedback (NF) circuit based on repression by a transcription factor called the Tetracycline Repressor (TetR). The two primary states of this circuit are illustrated in Fig.30. TetR can bind to its cognate operator sites (tetO), thereby inhibiting transcription of its own gene and that of the gene of interest. The two genes are separated by a ribosome-skipping sequence (P2A) that enables multi-cistronic gene expression in eukaryotic systems [4]. In the presence of saturating amounts of the doxycycline inducer (dox), TetR is unable to bind to (tetO2) and gene expression can proceed. [0210] To model this negative feedback circuit, the inventors modified an existing TetR- based NF model [1] by adding plasmid copy number (CN) as an additional parameter. The inventors used the following set of differential equations based on mass action kinetics:
Figure imgf000057_0001
[0212] where CN is the plasmid copy number, ”[ ]” notation denotes intracellular concentration, and ”[ ]ext” indicates extracellular concentration. Dox is the inducer molecule (i.e. doxycycline). a is the production rate per plasmid. F([TetR]) is the inhibition function with intracellular TetR concentration as the independent variable. This inhibition function is shown at the end of “Model description of four key topologies utilized to predict gene dosage compensation” above. b is the association rate of TetR and Dox, and d is the protein decay rate, c is the diffusion constant of Dox, and f is the inducer decay rate. The decay terms d [TetR] and f ^ [Dox] are small compared with the association term b ^ [TetR] ^ [Dox] [1]. If d ^ [TetR] and f ^ [Dox] are approximated as zero, the steady-state concentration of POI, [POI]ss, can be written as:
Figure imgf000057_0002
[0214] This shows that at steady-state, the concentration of POI is independent of the copy number, CN. If the approximation above does not hold, [POI]ss becomes modestly dependent on plasmid copy number, as shown in the main text (Fig.2d, Ideal Negative Feedback curve). [0215] Estimating the distribution of plasmid copy numbers in transfected cells [0216] Plasmid copy number distribution in transiently transfected cells was an important parameter used in the model to predict the cell-to-cell variability in gene expression caused by the variability in plasmid copy number. While most of the other parameter values used in the model were determined based on the prior work (see Table 1 and 2), plasmid copy number distribution is specific to experimental conditions such as transfecting cell type, transfection reagent, amount of transfecting plasmid. Therefore, this parameter was estimated based on the experimental results. The plasmid copy number distribution was estimated using the EGFP expression distribution of HEK293 cells expressing EGFP from the transfected unregulated promoter plasmid and the NF circuit plasmid (see Methods for details). For the unregulated promoter plasmids, expression levels were approximately linearly proportional to the plasmid copy number (Fig.3g). Therefore, the expression level distribution that can be empirically determined could be a proxy for the plasmid copy number distribution. Of note, here, the inventors defined plasmid copy number as ”active” plasmid copy number that was actively transcribed. In agreement with previous studies [5], the EGFP expression of cells transfected with the unregulated plasmid roughly followed the gamma distribution whose cumulative distribution function (CDF), F(x), followed:
Figure imgf000058_0001
[0218] where x is the effective plasmid copy number, k is the shape parameter, Ɵ is the scale parameter, Γ(k) is the gamma function evaluated at k, and is the lower incomplete gamma function. Depicted in Fig.31 is the CDF histogram of EGFP fluorescence of transfected cells that were analyzed by flow cytometry. The solid curvy line in Figure 31 is the fitted CDF curve of a gamma distribution, which has a shape parameter k of 0.57 and a scale parameter Ɵ of 1.7x105. Therefore, because the EGFP expression distribution fitted a gamma distribution, one could conclude that the underlying plasmid copy number should also have a gamma distribution, assuming the EGFP expression random variable is the plasmid copy number random variable scaled by some positive constant factor. [0219] Fitting the shape parameter k of the plasmid copy number distribution [0220] After establishing that the plasmid copy number distribution fits a gamma distribution, the shape parameter k of the plasmid copy number distribution was determined. To this end, the flow-cytometry-acquired single-cell EGFP expression distributions were used of HEK293 cells transfected with the unregulated CMV plasmid. As a property of the gamma distribution, the shape parameter k remains constant if the random variable is scaled by some positive constant. Thus one can use any unregulated plasmid to estimate the shape parameter k of the plasmid copy number distribution. Single-cell unregulated CMV plasmid data (depicted in the first figure) were used to estimate the shape parameter k of the plasmid copy number distribution to be 0.57 (see MATLAB script in the Equalizer GitHub repository: Example 8 CopyNumberDistShapeParameterEstimate.m). [0221] Further validation confirmed that expression levels scale linearly with plasmid copy numbers. This was accomplished by fitting the shape parameter k using the single-cell unregulated PGK plasmid data. Due to the lower expression of EGFP from the PGK promoter than CMV promoter, this promoter was chosen to demonstrate that absolute expression levels would not impact fitting of k. Using the same shape fitting technique as was done for the unregulated CMV circuit, the unregulated PGK circuit had a similar fit with an estimated k value of 0.56. These data further support the assumption that expression levels scale linearly with plasmid copy number and suggests only minimal non-linearity. [0222] Fitting the scale parameter Ɵ of the plasmid copy number distribution and the leakage of the NF circuit [0223] Having determined the shape parameter k, one can now use the experimentally determined mean expression of the NF circuit at different doxycycline concentrations to estimate the scale parameter Ɵ of the plasmid copy number distribution and the leakage parameter. In contrast to determining k, the distribution of single-cell expression from unregulated circuits does not constrain Ɵ. The additional constraints imposed by the mean expression of the NF circuit at different doxycycline concentrations allowed for estimation of Ɵ and leakage at the same time, since the plasmid copy number distribution and the leakage parameter both affect the expression induction curve of the NF circuit. The inventors scanned across different leakage and scale parameter Ɵ combinations to minimize the mean squared error (MSE) of the simulated mean expression of the NF circuit at 8 different inducer concentrations (0, 1, 5, 10, 50, 100, 500, 1000 ng/mL) compared with experimental data. All experimental data were normalized to the lowest expression level so that the simulated expression vs. inducer curve demonstrated a similar trend to what is experimentally observed. As shown in the plot below, where the z-axis shows the negative MSE, there is a sharp gradient along the leakage axis. The smallest deviation between the simulated and experimental data (i.e., MSE closest to zero) is achieved at the leakage value of 0.25. See: SuppNote3 CopyNumberDistScaleParameter Leakage Estimate.m. [0224] Given the small gradient along the scale parameter Ɵ axis, a finer scan was performed of the scale parameter with leakage fixed at 0.25 and with a larger range of Ɵ from 60 to 200. This is equivalent to taking a slice along leakage = 0.25 and looking at MSE versus Ɵ, as indicated by the pink bounding box in Fig.31c. The MSE is closest to zero between a scale parameter Ɵ of 100 to 120. Ɵ of 120 is used for the simulations in this work (Fig.31d). See “Predicting cell-to-cell variability” for how sensitive the simulation results are to the choice of Ɵ. [0225] See: Example 8 CopyNumberDistScaleParameter 1dScan.m. Using the defined shape and scale parameters, the plasmid copy number distribution can be used to predict the cell- to-cell variability caused by plasmid copy number variability for any given circuit model. [0226] Estimating miRNA affinity [0227] miRNA is an integral part of several of the circuits used in this study, thus to properly model and predict protein of interest cell-to-cell variation for these circuits, miRNA affinity was estimated. miRNA affinity to its target depends on the specific sequences of the miRNA and target, the number of target sites on the transcript, and location of target sites on the transcript [6]. From the modeling standpoint, miRNA affinity can be described by the ratio of the dissociation rate constant and the association rate constant. Previous studies found that miRNA affinities are modulated by the dissociation rate constant [7, 8]. Thus, the inventors fixed the association rate constant (kf RISC complex formation in the model) and used the change in dissociation rate constant (kf RISC complex deformation in the model) to model the change in miRNA affinity to its target. MATLAB’s fminsearch function was used to fit the values of dissociation rate constant by minimizing the mean squared difference between the simulated and experimental ratio of the mean expression of 10 thousand cells expressing the Equalizer circuit and the NF circuit at 6 different inducer concentrations (0, 1, 5, 10, 50, 100 ng/mL). Equalizer and the NF circuits were included in this analysis so that direct comparisons between a circuit with and without miRNA, respectively, can be done. The plasmid copy number distribution was estimated from the distribution obtained from “Estimating the distribution of plasmid copy numbers in transfected cells”. To test the simulation accuracy, different initial guesses of the dissociation rate constant of miRNA to its target were used and converged to the same estimated dissociation rate constant of 0.303 second-1. Having now defined the parameters for miRNA affinity and plasmid copy number distribution, the fully specified models for Equalizer and the IFF circuit can be used to predict cell-to-cell variability of cells expressing the circuits. [0228] Predicting cell-to-cell variability [0229] The modeling can predict the cell-to-cell variability originating from plasmid copy number variability (extrinsic noise). However, the experimentally observed cell-to-cell variability is caused by intrinsic noises and extrinsic noise. To compare the prediction with experimental data, in certain embodiments the following considerations were made: (1) plasmid copy number variability is the major source of extrinsic noise, and extrinsic noise from other sources is negligible in comparison; (2) intrinsic noise is constant across Equalizer, IFF circuit, NF circuit, and unregulated CMV circuit. Based on the second assumption, the intrinsic noise can then be approximated as the experimentally observed cell-to-cell variability of the CMV cell line, which integrated the unregulated CMV circuit to the genome and thus, the noise should be mostly intrinsic noise since it has minimal DNA copy number variability. To predict the total noise (coefficient of variation), the relationship was utilized that the square of total noise equals the sum of squares of extrinsic noise and intrinsic noise [9], i.e.
Figure imgf000061_0001
[0230] Given the relatively large range of the scale parameter (Ɵ) to produce a low MSE (Ɵ = 100 to 120, Example 8) and the effect Ɵ has on estimated miRNA affinity, it was tested how different Ɵ values would affect the predicted cell-to-cell variability. Shown in Fig.32 is the predicted cell-to-cell variability by simulating 10 thousand cells expressing the Equalizer-L and NF circuits versus the experimentally observed cell-to-cell variability induced with different concentrations of doxycycline (also shown in Fig.3d). Comparing the simulated to the experimental values demonstrates that the simulations could predict the relative shapes for each circuit. Additionally, the predicted CV is largely unaffected by the change of Ɵ from 120 to 100, as seen by only small changes in the simulated curves between the two different Ɵ values. These data suggest that using a Ɵ of 120 and a miRNA dissociation rate constant of 0.303 second-1 in the simulations produces accurate predictions and thus, the inventors used these values in all other modeling done in this work unless otherwise specified. [0231] Comparing intrinsic noise between Equalizer-L andmulti-promoter Equalizer-L [0232] To evaluate the impact of having multiple promoters on the intrinsic noise of expression from Equalizer, stochastic simulations were performed to compute the intrinsic noise across different transcriptional bursting parameter space. To model transcriptional bursting, an off-state was added to the gene of interest to the deterministic models, which is a standard assumption in the field that has been supported by in vivo observation[10]. To minimize redundancy, here the additional reactions are shown on top of the reactions listed in “Model description of four key topologies utilized to predict gene dosage compensation”:
Figure imgf000062_0001
[0233] [0234] The switching between on-state and off-state follows mass action kinetics parameters k on and k off (as shown in Table 1). [0235] Here the inventors focus on the comparison between Equalizer and the multi- promoter version of Equalizer (Fig.21a), which has the same regulatory elements as the Equalizer circuit but expresses each regulatory element from separate promoters as opposed to a single promoter in the Equalizer circuit: [0236] Stochastic simulation setup [0237] Stochastic simulations were performed using Stochkit2 [11], stand-alone stochastic simulation software that allows defining custom propensity functions. For each topology, a first simulation is run for 106 seconds to reach a steady-state. [0238] Then, auto-correlation is computed for the Equalizer circuit (topology 4) to determine the time τ required for the system to ”forget” its initial state at time 0 as shown in FIG. 33. τ of 5x 106 seconds was chosen since auto-correlation is well within the approximate upper and lower auto-correlation confidence bounds shown as horizontal lines. [0239] Then, using the end states of the first 106-second simulation for each topology as the initial states, 1000 simulations of length τ were run. The mean and standard deviation of the POI molecule numbers of the 1000 simulations are used to calculate the simulated cell-to-cell variability. The initial condition for all topologies assumes 10 copies of the gene in the off-state, the inducer at a steady-state determined by the influx rate and the degradation rate shown in Table 1 with an extracellular inducer of 0.5 ng/mL, and RISC molecule count is set to the value mentioned above (1.7e5 molecules/cell); the initial molecule count for the rest of the species is zero. [0240] In the first set of stochastic simulations, the inventors used the kinetic parameters listed in Table 1 for the Equalizer circuit (Topology 4 in Fig.29). The kinetic parameters were modified for the multiple promoter Equalizer circuit (Topology 5 in Fig.29) so that the steady- state mRNA amount and steady-state POI amount remains the same as the Equalizer circuit. The following table shows the modified parameters to achieve the same steady-state amount of mRNA and POI: [0241] Stochastic simulation set 1: original parameters Parameter Equalizer Multiple promoter Equalizer k_transcription (1/second) 4.67e-2 3.70e-2 (same for all three genes) k_translation (1/second) 3.33e-4 3.33e-4 (same for all three genes) k_on (1/second) 3.10e-4 3.10e-4 (same for all three genes) k_off (1/second) 4.18e-4 4.18e-4 (same for all three genes) [0242] The second set of stochastic simulations test how each topology reacts to amplified translation bursting, for which the steady-state mRNA amount in each topology is reduced 50 fold compared with set 1 while maintaining steady-state POI and TetR amount with the following modified parameters: [0243] Stochastic simulation set 2: amplified translational bursting Parameter Equalizer Multiple promoter Equalizer k_transcription (1/second) 5.10e-4 4.40e-4 (same for all three genes) k_translation (1/second) 1.66e-2 1.66e-2 (same for all three genes) k_on (1/second) 3.10e-4 3.10e-4 (same for all three genes) k_off (1/second) 4.18e-4 4.18e-4 (same for all three genes) [0244] The third set of stochastic simulations test how each topology reacts to amplified transcriptional bursting, for which the transcription bursting parameters k on and k off are decreased by 10 fold compared with set 1: [0245] Stochastic simulation set 3: amplified transcriptional bursting Parameter Equalizer Multiple promoter Equalizer
Figure imgf000064_0002
_ [0246] Stochastic simulations results are shown in Figure 21. [0247] Comparing the coefficient of variation and the Fano factor as measures for quantifying cell-to-cell variability in the studies [0248] Heterogeneity of a population not only arises between cells of different genetic backgrounds but also occurs in populations that arise from genetically identical cells through differences in intrinsic and extrinsic noise [12, 13, 14, 15]. There are two main measures to describe population variation, coefficient of variation (CV) [16] and Fano factor [17]. Consider a population distribution with a mean ( ^) and a standard deviation (σ) defined as:
Figure imgf000064_0001
[0249] [0250] where N is the size of the population and xi is each value of the population. [0251] Then CV is defined as: [0252] [0253] and Fano factor is defined as:
Figure imgf000065_0001
[0255] CV is a dimensionless quantity that is invariant to proportional scaling. When the population distribution x is scaled with a factor ^, then the mean of the scaled distribution, ^scaled is:
Figure imgf000065_0002
[0256] [0257] and the standard deviation of the scaled distribution ^scaled, is:
Figure imgf000065_0003
[0258] [0259] The CV of the scaled distribution, CVscaled is:
Figure imgf000065_0004
[0260] [0261] Unlike CV, the Fano factor changes when the population distribution is scaled with a factor ^: [0262]
Figure imgf000066_0001
[0263] The experiments conducted in this work produce quantities in arbitrary units where the signal is proportional to the molecule number with unknown proportionality. In such cases, the Fano factor has major disadvantages. Not only would the application of the Fano factor metric have unclear units, but its exact value would scale with the changes in the unknown proportionality coefficient as Eq. (26). As a result, the Fano factor does not stay invariant under proportional scaling, unlike CV. For example, the Fano factor cannot be compared between experiments that employ measurements of molecule count (e.g., flow cytometry, microscopy) with different devices, cameras, or amplification gain settings as these are expected to affect the proportionality coefficient. Additionally, the Fano factor compares the spread of probability distribution relative to a Poisson distribution with the same mean. Thus, the Fano factor metric struggles to deal with comparisons between conditions when the mean value of each condition is very different (Fig.20i). For example, if two conditions have the same mean, but one has a greater Fano factor value, then there is a clear difference in dispersion. However, if the conditions have different means, it is difficult to interpret what the difference in their Fano Factor values means in terms of fold change since there is no proportional scaling. [0264] The proportional scaling of CV allows for comparing the variability between systems with different sized means and units. Additionally, differences in data acquisition can be tolerated as long as the measurement values differ by a scaling factor. CV can also be expressed in percentage (as the inventors chose to do here) by multiplying the value given by Eq. (10) by 100. For these reasons, CV was chosen to quantify cell-to-cell variability. In a particular embodiment, improving mRNA stability increases Equalizer circuit expression level and gene dosage compensation ability. The cell-to-cell variation (Fig.35a) and expression level (Fig.35b) change when doxycycline concentration varies. The doxycycline concentration that gives the lowest cell-to-cell variation for each condition may be chosen for Fig.35c, Fig.35d, and Fig.35e. Reducing the mRNA degradation rate (Fig.35c) decreases the cell-to-cell variation and (Fig.35d) increases the expression level. A efficacious performance is achieved when the mRNA degradation rate reaches the minimum (Fig.35e). [0265] References for the present example: [0266] 1. Nevozhay, D., Adams, R. M., Murphy, K. F., Josi´c, K. & Bal´ azsi, G. Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression. Proceedings of the National Academy of Sciences 106, 5123–5128 (2009). [0267] 2. Bleris, L. et al. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Molecular systems biology 7, 519–519 (2011). 21811230[pmid] PMC3202791[pmcid] msb201149[PII]. [0268] 3. Siciliano, V. et al. Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line. PLoS Computational Biology 7, e1002074 (2011). [0269] 4. Kim, J. H. et al. High cleavage efficiency of a 2A peptide derived from porcine teschovirus-1 in human cell lines, zebrafish and mice. Plos One 6, e18556 (2011). [0270] 5. Friedman, N., Cai, L. & Xie, X. S. Linking stochastic dynamics to population distribution: An analytical framework of gene expression. Phys. Rev. Lett.97, 168302 (2006). [0271] 6. Schreiber, J., Arter, M., Lapique, N., Haefliger, B. & Benenson, Y. Model- guided combinatorial optimization of complex synthetic gene networks. Molecular systems biology 12, 899 (2016). [0272] 7. Salomon, W., Jolly, S., Moore, M., Zamore, P. & Serebrov, V. Single-molecule imaging reveals that argonaute reshapes the binding properties of its nucleic acid guides. Cell 162, 84–95 (2015). [0273] 8. Denzler, R. et al. Impact of microrna levels, target-site complementarity, and cooperativity on competing endogenous rna-regulated gene expression. Molecular Cell 64, 565– 579 (2016). [0274] 9. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proceedings of the National Academy of Sciences of the United States of America 99, 12795–12800 (2002). [0275] 10. Sepulveda, L. A., Xu, H., Zhang, J., Wang, M. & Golding, I. Measurement of gene regulation in individual cells reveals rapid switching between promoter states. Science 351, 1218–1222 (2016). https://science.sciencemag.org/content/351/6278/1218.full.pdf. [0276] 11. Sanft, K. R. et al. Stochkit2: software for discrete stochastic simulation of biochemical systems with events. Bioinformatics 27, 2457–2458 (2011). [0277] 12. Sanchez, A., Choubey, S. & Kondev, J. Regulation of noise in gene expression. Annual review of biophysics 42, 469–491 (2013). [0278] 13. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004). [0279] 14. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proceedings of the National Academy of Sciences of the United States of America 99, 12795– 12800 (2002). [0280] 15. Fu, A. Q. & Pachter, L. Estimating intrinsic and extrinsic noise from single- cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology 15, 447–471 (2016). [0281] 16. Barker, P. The logic of scientific design. Encyclopedia of Research Design (SAGE Publications, Inc., 2010). [0282] 17. Hortsch, S. K. & Kremling, A. Stochastic models for studying the role of cellular noise and heterogeneity, 34–44 (Elsevier, 2021). [0283] 18. Lillacci, G., Benenson, Y. & Khammash, M. Synthetic control systems for high performance gene expression in mammalian cells. Nucleic acids research 46, 9855–9863 (2018). [0284] 19. Peng, W., Song, R. & Acar, M. Noise reduction facilitated by dosage compensation in gene networks. Nat. Commun.7, 12959 (2016). [0285] 20. Acar, M., Pando, B. F., Arnold, F. H., Elowitz, M. B. & van Oudenaarden, A. A general mechanism for network-dosage compensation in gene circuits. Science 329, 1656– 1660 (2010). [0286] 21. Tigges, M., Marquez-Lago, T. T., Stelling, J. & Fussenegger, M. A tunable synthetic mammalian oscillator. Nature 457, 309 (2009). Citation for protein requiring narrow concentration range. [0287] 22. Tigges, M., D´enervaud, N., Greber, D., Stelling, J. & Fussenegger, M. A synthetic low-frequency mammalian oscillator. Nucleic acids research 38, 2702–2711 (2010). [0288] 23. To, T.-L. & Maheshri, N. Noise can induce bimodality in positive transcriptional feedback loops without bistability. Science 327, 1142–1145 (2010). [0289] 24. Monier, K., Armas, J. C. G., Etteldorf, S., Ghazal, P. & Sullivan, K. F. Annexation of the interchromosomal space during viral infection. Nature cell biology 2, 661 (2000). [0290] 25. Molina, N. et al. Stimulus-induced modulation of transcriptional bursting in a single mammalian gene. Proceedings of the National Academy of Sciences 110, 20563–20568 (2013). [0291] 26. Wang, D. et al. Quantitative functions of argonaute proteins in mammalian development. Genes & development 26, 693–704 (2012). EXAMPLE 9 LAC-BASED SYSTEMS [0292] In specific embodiments, the system utilizes components from the Lac operon (e.g., LacO operator sites) instead of other repressors, such as from the Tet operon (e.g., TetO operator sites). In these embodiments for the system, when lactose becomes available, it is converted into allolactose, which inhibits the DNA binding ability of LacI at the LacO2 site, thereby permitting gene expression. Fig.34a shows the cell-to-cell variability as a function of IPTG concentration for both the NF-only circuit and the NF+IFF (Equalizer) design. The expression (circuit output) vs. IPTG concentration is demonstrated for the LacI-based NF-only circuit in Fig.34b, and the expression (circuit output) vs. IPTG concentration is provided for the LacI-based Equalizer circuit in Fig.34c. See below for representative sequences of LacI-based Equalizers. TABLES [0293] Table 1: Mass action kinetic parameters [0294] Parameter Value Unit Reference k_transcription 4.67e-2 1/second [21] k_dicer 1e-3 1/second [22] k_drosha 1e-2 1/second [2] k_Inducer_bind_TetR 1.0e-5 1/(molecule*second) [23] k Inducer dissociates TetR 2.0e-8 1/second [23]
Figure imgf000070_0001
3.33e-4 1/second [1] k_influx^ 0.156 molecule/second [1] K_microRNA_bind_RISC 1.0e-5 1/(molecule*second) [22] k_microRNA_deg 2.88e-4 1/second [21] K_microRNA_RISC_dissociation 2.16e-5 1/second [22] k_mRNA_deg 2.88e-4 1/second [21] k_mRNAmicroRNA_deg 2.88e-4 1/second [21] k_POI_deg 9.67e-5 1/second [21] k_RISC_complex_deformatoin .303 1/second estimated k_RISC_complex_formation 1.84e-6 1/(molecule*second) [22] k_slicer 7.0e-3 1/second [22] k_splicing 2.0e-3 1/second [2] k_TetR_deg 9.67e-5 1/second [21] k Inducer-TetR deg 9.67e-5 1/second [21]
Figure imgf000071_0001
k_off 4.18e-4 1/second [25] RISC_initial 1.7e+5 molecules [26] [0295] a0.156 molecule/second corresponds to a extracellular doxycycline concentration of 1 ng/ml, assuming a nucleus volume of 690 ^m3 [24]; this influx rate linearly scales with extracellular doxycycline concentration. See “Estimating miRNA affinity” for methods used for estimation of the k RISC complex formation parameter. [0296] Table 2: Non-mass action kinetic parameters [0297] Parameter Value Unit Reference k_transcription_max 4.67e-2 1/second [21] K₅₀ 183 molecule [1] leakage .25 dimensionless estimated [0298] See “Estimating the distribution of plasmid copy numbers in transfected cells” for methods used for estimation of the leakage parameter. [0299] Table 3: Gamma-distributed plasmid copy number parameters [0300] Parameter Value Unit Reference shape parameter k 0.57 dimensionless estimated scale parameter 0 120 dimensionless estimated [0301] See “Estimating the distribution of plasmid copy numbers in transfected cells” for methods used for the estimation of k and θ. [0302] Table 4: Examples of Plasmids used in this disclosure [0303] Name Description Usage Schematic Addgene; GenBamk CMV-tetO2 promoter- Equalizer-H bGlob intron-tetR-P2A- eGFP-MiR( 169367; plasmid FF3)-target- Fig. 3a-c Fig. 11a mir(FF3)-WPRE- MW962297 bGH-terminator CMV-tetO2 promoter- Equalizer-M bGlob intron-tetR-P2A- eGFP-Mi 169731; plasmid R(FF4)-target- Fig. 3a-c Fig. 11b mir(FF4)-WPRE- MW987521 bGH terminator CMV-tetO2-promoter- bGlob intron- Equalizer-L MiR(FF4) target- Fig.3a-c; tetR-P2A-eGFP- Fig.4a-b 169732; plasmid ; Fig. 11c MiR(FF4) target- Fig.15 MW987522 mir(FF4)-WPRE- bGH-terminator CMV-tetO2-promoter- bGlob intron- Equalizer-L MiR(FF4) target- plasmid with tetR-P2A-eGFP- Fig.3d-g; onboard MiR(FF4) target- Fig.13-14; 169735; mCherry mir(FF4)-WPRE- Fig.20j; Fig. 11d MW987525 cassette bGH-terminator- Fig.21
Figure imgf000072_0001
rbGlob terminator CMV-tetO2-promoter- bGlob intron- MiR(FF4) target- eGFP MiR(FF4)-target- Multi- WPRE-bGH terminator- promoter CMV-tetO2 promoter- Equalizer-L bGlob intron- plasmid with MiR(FF4) target- Fig. 21 N/A; onboard tetR-MiR(FF4) target- Fig. 11e MZ099631 mCherry WPRE-bGH-terminator- cassette CMV-tetO2 promoter- bGlob intron-mir(FF4)- WPRE-bGH-terminator-
Figure imgf000072_0002
rhGlob terminator Equilizer-L CMV-tetO2-promoter- Fig. 5; Fig. 169737; (mScarlet-l) bGlob intron- 5a MW987526 plasmid with MiR(FF4) target- Fig.19; Fig. onboard tetR-P2A-mScarletl- 20- mCitrine MiR(FF4) target- c,f,h cassette mir(FF4)-WPRE- bGH-terminator- FF1a-promoter- mCitrine- SV40 terminator- bGH-terminator [0304] CMV-tetO2-promoter- bGlob intron- MiR(FF4) target- tetR-P2A-eGFP- MiR(FF4) target- mir(FF4)- Equalizer-L SV40 terminator- Fig.6; r- Fig. 169738; episome FF1a-promote 12; Fig. 22a Fig. MW987527 mCherry- 24-26 rbGlob terminator- HSV-TK promoter-hph- HSV-TK-terminator- EBNA promoter-EBNA1- EBNA terminator-OriP
Figure imgf000073_0001
bGlob intron-eGFP 169740; plasmid - Fig. 3c Fig. 11f WPRE-bGh-terminator MW987529 CMV+ CMV tetO2 promoter- plasmid with bGlob intron-eGFP- WPRE-bGH t Fig.3d-g; onboard erminator- Fig.1 169741; mCherry FF1a-promoter- 3-14; Fig. 11g Fi MW987530 mCherry- g.20i-j cassette rbGlob terminator
Figure imgf000073_0002
bGlob intron-eGFP- WPRE-bGH-terminator- FF1a-promoter- mChe Fig.6; CMV episome rry- Fig.12; F 169742; rbGlob terminator- ig. 22b MW987 V-TK promoter-hph- Fig. 531 HS 24-26 HSV-TK-terminator- EBNA promoter-EBNA1- EBNA terminator-OriP CMV+ CMV-tetO2 promoter- (mScarlet-l) bGlob intron-mScarletl- plasmid with WPRE-bGH-terminator- Fig.5; Fig. 5 169743; onboard EF1a-promoter- Fig. 20a-c,f,h c MW987532 mCitrine mCitrine- cassette rbGlob terminator PGK plasmid PGK-promoter eGFP- Fig.3c; 169744; bGH-terminator Fig. 28 Fig. 11h MW987534 PGK+ PGK-tetO2 promoter- plasmid with bGlob intron-eGFP- Fig.3e-g; onboard WPRE-bGH-terminator- Fig.13-14; Fig 169745; EF1a-promoter- . 11i mCherry Fig.20i-j; MW987534 cassette mCherry- Fig.28 rbGlob terminator [0305] PGK tetO2 promoter- bGlob intron-eGFP- WPRE-bGH terminator- PGK episome FF1a promoter- with onboard mCherry- Fig.6; F 170041; mCherry rbGlob terminator- Fig. 24-26 ig. 22c MW962296 cassette HSV-TK promoter-hph- HSV-TK-terminator- EBNA promoter-EBNA1- EBNA terminator-OriP UBC plasmid
Figure imgf000074_0001
- 169746; bGH-terminator Fig. 3c Fig. 11j MW987535 Fig.3a-c; CMV CMV-promoter- Fig.4a-c; mCherry mCherry- Fig.9; Fig.11k N/A plasmid bGH-terminator Fig.16; Fig.28 Negative CMV-tetO2-promoter- Fig.4a-b; feedback (NF) bGlob intron-tetR- 169747; plasmid P2A-eGFP-WPRE- Fig.9; Fig. 15b MW987536 bGH-terminator Fig.16
Figure imgf000074_0002
moter- Incoherent bGlob intron- feedforward MiR(FF4)-target- Fig.4c; 169748; eGF Fig. 15c (IFF) plasmid P-MiR(FF4) target- Fig. 16 MW987537 mir(FF4)-WPRE- bGH-terminator EF1a-promoter- mCitrine- SV40 terminator- bGH-terminator- SV40 promoter- tTA::Cerulean- Fig.5; HYB plasmid MiR(FF4) targetx3- Fig.19b; Fig.5b N/A SV40 terminator- Fig.20a-f, h, j bGH-terminator- TRE promoter- mScarletl(mir FF4)- MiR(FF4) targetx3- SV40 terminator EF1a-promoter- mCitrine- SV40 terminator- bGH-terminator- SV40 promoter- HYB tTA::Cerulean- (original) MiR(FF4) targetx3- Fig.20g ref. [18] N/A plasmid SV40 terminator- bGH-terminator- TRE promoter- DsRed(mir FF4)- MiR(FF4) targetx3- SV40 terminator [0306] EF1a-promoter- mCitrine- SV40 terminator- bGH-terminator- SV40 promoter- tTA::Cerulean- OLP plasmid MiR(FF5) targetx3- Fig.5; Fig. 20a-f, h, Fig. 5d N/A SV40 terminator- j bGH-terminator- TRE promoter- mScarletl(mir FF4)- MiR(FF5) targetx3- SV40 terminator OLP EF1a-promoter- (original) mCitrine- Fig.20g ref. [18] N/A plasmid SV40 terminator- bGH-terminator- SV40 promoter- tTA::Cerulean- MiR(FF5) targetx3- SV40 terminator- bGH-terminator- TRE promoter- DsRed(mir FF4)- MiR(FF5) targetx3- SV40 terminator PARTICULAR SEQUENCES [0307] Equalizer-H plasmid [0308] source 1..7978 [0309] /organism="pcDNA5- tetR-P2A-eGFP-miR(FF3) target-miR(FF3), Equalizer-H plasmid, complete sequence" [0310] /mol_type="other DNA" [0311] /note="other sequences; artificial sequences; vectors." [0312] regulatory 236..858 [0313] /regulatory_class="promoter" [0314] /note="CMV-tetO2 promoter" [0315] regulatory 1401..1613 [0316] /note="bGlob_int" [0317] regulatory 1631..1987 [0318] /note="htetR" [0319] regulatory 3092..3113 [0320] /note="FF3" [0321] regulatory 3120..3495 [0322] /note="miRNA FF3" [0323] gene 2372..3091 [0324] /gene="EGFP" [0325] CDS 2372..3091 [0326] /gene="EGFP" [0327] /codon_start=1 [0328] /product="EGFP" [0329] /translation="MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTL KFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDD GNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIK VNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLL EFVTAAGITLGMDELYK" (SEQ ID NO:7) [0330] BASE COUNT 1880 a 2061 c 2012 g 2025 t [0331] ORIGIN [0332] 1 gacggatcgg gagatctccc gatcccctat ggtgcactct cagtacaatc tgctctgatg [0333] 61 ccgcatagtt aagccagtat ctgctccctg cttgtgtgtt ggaggtcgct gagtagtgcg [0334] 121 cgagcaaaat ttaagctaca acaaggcaag gcttgaccga caattgcatg aagaatctgc [0335] 181 ttagggttag gcgttttgcg ctgcttcgcg atgtacgggc cagatatacg cgttgacatt [0336] 241 gattattgac tagttattaa tagtaatcaa ttacggggtc attagttcat agcccatata [0337] 301 tggagttccg cgttacataa cttacggtaa atggcccgcc tggctgaccg cccaacgacc [0338] 361 cccgcccatt gacgtcaata atgacgtatg ttcccatagt aacgccaata gggactttcc [0339] 421 attgacgtca atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt [0340] 481 atcatatgcc aagtacgccc cctattgacg tcaatgacgg taaatggccc gcctggcatt [0341] 541 atgcccagta catgacctta tgggactttc ctacttggca gtacatctac gtattagtca [0342] 601 tcgctattac catggtgatg cggttttggc agtacatcaa tgggcgtgga tagcggtttg [0343] 661 actcacgggg atttccaagt ctccacccca ttgacgtcaa tgggagtttg ttttggcacc [0344] 721 aaaatcaacg ggactttcca aaatgtcgta acaactccgc cccattgacg caaatgggcg [0345] 781 gtaggcgtgt acggtgggag gtctatataa gcatccctat cagtgataga gatcagatct [0346] 841 ccctatcagt gatagagagc tgttttgacc tccatagaag acaccgggac cgatccagcc [0347] 901 tccggactct agcgtttaaa cttaagctgg gtacccgggg atcctctagg gcctctgagc [0348] 961 tattccagaa gtagtgaaga ggcttttttg gaggcctagg cttttgcaaa aagctccgga [0349] 1021 tcgatcctga gaacttcagg gtgagtttgg ggacccttga ttgttctttc tttttcgcta [0350] 1081 ttgtaaaatt catgttatat ggagggggca aagttttcag ggtgttgttt agaatgggaa [0351] 1141 gatgtccctt gtatcaccat ggaccctcat gataattttg tttctttcac tttctactct [0352] 1201 gttgacaacc attgtctcct cttattttct tttcattttc tgtaactttt tcgttaaact [0353] 1261 ttagcttgca tttgtaacga atttttaaat tcacttttgt ttatttgtca gattgtaagt [0354] 1321 actttctcta atcacttttt tttcaaggca atcagggtat attatattgt acttcagcac [0355] 1381 agttttagag aacaattgtt ataattaaat gataaggtag aatatttctg catataaatt [0356] 1441 ctggctggcg tggaaatatt cttattggta gaaacaacta catcctggtc atcatcctgc [0357] 1501 ctttctcttt atggttacaa tgatatacac tgtttgagat gaggataaaa tactctgagt [0358] 1561 ccaaaccggg cccctctgct aaccatgttc atgccttctt ctttttccta caggtcctgc [0359] 1621 aggcgccacc atggagtcta gactggacaa gagcaaagtc ataaactctg ctctggaatt [0360] 1681 actcaatgaa gtcggtatcg aaggcctgac gacaaggaaa ctcgctcaaa agctgggagt [0361] 1741 tgagcagcct accctgtact ggcacgtgaa gaacaagcgg gccctgctcg atgccctggc [0362] 1801 aatcgagatg ctggacaggc atcataccca cttctgcccc ctggaaggcg agtcatggca [0363] 1861 agactttctg cggaacaacg ccaagtcatt ccgctgtgct ctcctctcac atcgcgacgg [0364] 1921 ggctaaagtg catctcggca cccgcccaac agagaaacag tacgaaaccc tggaaaatca [0365] 1981 gctcgcgttc ctgtgtcagc aaggcttctc cctggagaac gcactgtacg ctctgtccgc [0366] 2041 cgtgggccac tttacactgg gctgcgtatt ggaggatcag gagcatcaag tagcaaaaga [0367] 2101 ggaaagagag acacctacca ccgattctat gcccccactt ctgagacaag caattgagct [0368] 2161 gttcgaccat cagggagccg aacctgcctt ccttttcggc ctggaactaa tcatatgtgg [0369] 2221 cctggagaaa cagctaaagt gcgaaagcgg cgggccaaaa aagaagagaa agggtgacgg [0370] 2281 tgctggttta attaacatgg gaagcggagc tactaacttc agcctgctga agcaggctgg [0371] 2341 agacgtggag gagaaccctg gacctgctag catggtgagc aagggcgagg agctgttcac [0372] 2401 cggggtggtg cccatcctgg tcgagctgga cggcgacgta aacggccaca agttcagcgt [0373] 2461 gtccggcgag ggcgagggcg atgccaccta cggcaagctg accctgaagt tcatctgcac [0374] 2521 caccggcaag ctgcccgtgc cctggcccac cctcgtgacc accctgacct acggcgtgca [0375] 2581 gtgcttcagc cgctaccccg accacatgaa gcagcacgac ttcttcaagt ccgccatgcc [0376] 2641 cgaaggctac gtccaggagc gcaccatctt cttcaaggac gacggcaact acaagacccg [0377] 2701 cgccgaggtg aagttcgagg gcgacaccct ggtgaaccgc atcgagctga agggcatcga [0378] 2761 cttcaaggag gacggcaaca tcctggggca caagctggag tacaactaca acagccacaa [0379] 2821 cgtctatatc atggccgaca agcagaagaa cggcatcaag gtgaacttca agatccgcca [0380] 2881 caacatcgag gacggcagcg tgcagctcgc cgaccactac cagcagaaca cccccatcgg [0381] 2941 cgacggcccc gtgctgctgc ccgacaacca ctacctgagc acccagtccg ccctgagcaa [0382] 3001 agaccccaac gagaagcgcg atcacatggt cctgctggag ttcgtgaccg ccgccgggat [0383] 3061 cactctcggc atggacgagc tgtacaagta gaacgatatg ggctgaatac aaaaagcttg [0384] 3121 tgagtatgtg ctcgcttcgg cagcacatat actatgtcga atgaggcttc agtactttac [0385] 3181 agaatcgttg cctgcacatc ttggaaacac ttgctgggat tacttcttca ggttaaccca [0386] 3241 acagaaggct cgagtgctgt tgacagtgag cgcacgatat gggctgaata caaatagtga [0387] 3301 agccacagat gtatttgtat tcagcccata tcgtttgcct actgcctcgg agaattcaag [0388] 3361 gggctacttt aggagcaatt atcttgttta ctaaaactga ataccttgct atctctttga [0389] 3421 tacattttta caaagctgaa ttaaaatggt ataaattaaa tcactttttt caattgtttc [0390] 3481 cttttttttc ctcaggcggc cgcaatcaac ctctggatta caaaatttgt gaaagattga [0391] 3541 ctggtattct taactatgtt gctcctttta cgctatgtgg atacgctgct ttaatgcctt [0392] 3601 tgtatcatgc tattgcttcc cgtatggctt tcattttctc ctccttgtat aaatcctggt [0393] 3661 tgctgtctct ttatgaggag ttgtggcccg ttgtcaggca acgtggcgtg gtgtgcactg [0394] 3721 tgtttgctga cgcaaccccc actggttggg gcattgccac cacctgtcag ctcctttccg [0395] 3781 ggactttcgc tttccccctc cctattgcca cggcggaact catcgccgcc tgccttgccc [0396] 3841 gctgctggac aggggctcgg ctgttgggca ctgacaattc cgtggtgttg tcctcgagtc [0397] 3901 tagagggccc gtttaaaccc gctgatcagc ctcgactgtg ccttctagtt gccagccatc [0398] 3961 tgttgtttgc ccctcccccg tgccttcctt gaccctggaa ggtgccactc ccactgtcct [0399] 4021 ttcctaataa aatgaggaaa ttgcatcgca ttgtctgagt aggtgtcatt ctattctggg [0400] 4081 gggtggggtg gggcaggaca gcaaggggga ggattgggaa gacaatagca ggcatgctgg [0401] 4141 ggatgcggtg ggctctatgg cttctgaggc ggaaagaacc agctggggct ctagggggta [0402] 4201 tccccacgcg ccctgtagcg gcgcattaag cgcggcgggt gtggtggtta cgcgcagcgt [0403] 4261 gaccgctaca cttgccagcg ccctagcgcc cgctcctttc gctttcttcc cttcctttct [0404] 4321 cgccacgttc gccggctttc cccgtcaagc tctaaatcgg gggctccctt tagggttccg [0405] 4381 atttagtgct ttacggcacc tcgaccccaa aaaacttgat tagggtgatg gttcacgtac [0406] 4441 ctagaagttc ctattccgaa gttcctattc tctagaaagt ataggaactt ccttggccaa [0407] 4501 aaagcctgaa ctcaccgcga cgtctgtcga gaagtttctg atcgaaaagt tcgacagcgt [0408] 4561 ctccgacctg atgcagctct cggagggcga agaatctcgt gctttcagct tcgatgtagg [0409] 4621 agggcgtgga tatgtcctgc gggtaaatag ctgcgccgat ggtttctaca aagatcgtta [0410] 4681 tgtttatcgg cactttgcat cggccgcgct cccgattccg gaagtgcttg acattgggga [0411] 4741 attcagcgag agcctgacct attgcatctc ccgccgtgca cagggtgtca cgttgcaaga [0412] 4801 cctgcctgaa accgaactgc ccgctgttct gcagccggtc gcggaggcca tggatgcgat [0413] 4861 cgctgcggcc gatcttagcc agacgagcgg gttcggccca ttcggaccgc aaggaatcgg [0414] 4921 tcaatacact acatggcgtg atttcatatg cgcgattgct gatccccatg tgtatcactg [0415] 4981 gcaaactgtg atggacgaca ccgtcagtgc gtccgtcgcg caggctctcg atgagctgat [0416] 5041 gctttgggcc gaggactgcc ccgaagtccg gcacctcgtg cacgcggatt tcggctccaa [0417] 5101 caatgtcctg acggacaatg gccgcataac agcggtcatt gactggagcg aggcgatgtt [0418] 5161 cggggattcc caatacgagg tcgccaacat cttcttctgg aggccgtggt tggcttgtat [0419] 5221 ggagcagcag acgcgctact tcgagcggag gcatccggag cttgcaggat cgccgcggct [0420] 5281 ccgggcgtat atgctccgca ttggtcttga ccaactctat cagagcttgg ttgacggcaa [0421] 5341 tttcgatgat gcagcttggg cgcagggtcg atgcgacgca atcgtccgat ccggagccgg [0422] 5401 gactgtcggg cgtacacaaa tcgcccgcag aagcgcggcc gtctggaccg atggctgtgt [0423] 5461 agaagtactc gccgatagtg gaaaccgacg ccccagcact cgtccgaggg caaaggaata [0424] 5521 gcacgtacta cgagatttcg attccaccgc cgccttctat gaaaggttgg gcttcggaat [0425] 5581 cgttttccgg gacgccggct ggatgatcct ccagcgcggg gatctcatgc tggagttctt [0426] 5641 cgcccacccc aacttgttta ttgcagctta taatggttac aaataaagca atagcatcac [0427] 5701 aaatttcaca aataaagcat ttttttcact gcattctagt tgtggtttgt ccaaactcat [0428] 5761 caatgtatct tatcatgtct gtataccgtc gacctctagc tagagcttgg cgtaatcatg [0429] 5821 gtcatagctg tttcctgtgt gaaattgtta tccgctcaca attccacaca acatacgagc [0430] 5881 cggaagcata aagtgtaaag cctggggtgc ctaatgagtg agctaactca cattaattgc [0431] 5941 gttgcgctca ctgcccgctt tccagtcggg aaacctgtcg tgccagctgc attaatgaat [0432] 6001 cggccaacgc gcggggagag gcggtttgcg tattgggcgc tcttccgctt cctcgctcac [0433] 6061 tgactcgctg cgctcggtcg ttcggctgcg gcgagcggta tcagctcact caaaggcggt [0434] 6121 aatacggtta tccacagaat caggggataa cgcaggaaag aacatgtgag caaaaggcca [0435] 6181 gcaaaaggcc aggaaccgta aaaaggccgc gttgctggcg tttttccata ggctccgccc [0436] 6241 ccctgacgag catcacaaaa atcgacgctc aagtcagagg tggcgaaacc cgacaggact [0437] 6301 ataaagatac caggcgtttc cccctggaag ctccctcgtg cgctctcctg ttccgaccct [0438] 6361 gccgcttacc ggatacctgt ccgcctttct cccttcggga agcgtggcgc tttctcatag [0439] 6421 ctcacgctgt aggtatctca gttcggtgta ggtcgttcgc tccaagctgg gctgtgtgca [0440] 6481 cgaacccccc gttcagcccg accgctgcgc cttatccggt aactatcgtc ttgagtccaa [0441] 6541 cccggtaaga cacgacttat cgccactggc agcagccact ggtaacagga ttagcagagc [0442] 6601 gaggtatgta ggcggtgcta cagagttctt gaagtggtgg cctaactacg gctacactag [0443] 6661 aaggacagta tttggtatct gcgctctgct gaagccagtt accttcggaa aaagagttgg [0444] 6721 tagctcttga tccggcaaac aaaccaccgc tggtagcggt ggtttttttg tttgcaagca [0445] 6781 gcagattacg cgcagaaaaa aaggatctca agaagatcct ttgatctttt ctacggggtc [0446] 6841 tgacgctcag tggaacgaaa actcacgtta agggattttg gtcatgagat tatcaaaaag [0447] 6901 gatcttcacc tagatccttt taaattaaaa atgaagtttt aaatcaatct aaagtatata [0448] 6961 tgagtaaact tggtctgaca gttaccaatg cttaatcagt gaggcaccta tctcagcgat [0449] 7021 ctgtctattt cgttcatcca tagttgcctg actccccgtc gtgtagataa ctacgatacg [0450] 7081 ggagggctta ccatctggcc ccagtgctgc aatgataccg cgagacccac gctcaccggc [0451] 7141 tccagattta tcagcaataa accagccagc cggaagggcc gagcgcagaa gtggtcctgc [0452] 7201 aactttatcc gcctccatcc agtctattaa ttgttgccgg gaagctagag taagtagttc [0453] 7261 gccagttaat agtttgcgca acgttgttgc cattgctaca ggcatcgtgg tgtcacgctc [0454] 7321 gtcgtttggt atggcttcat tcagctccgg ttcccaacga tcaaggcgag ttacatgatc [0455] 7381 ccccatgttg tgcaaaaaag cggttagctc cttcggtcct ccgatcgttg tcagaagtaa [0456] 7441 gttggccgca gtgttatcac tcatggttat ggcagcactg cataattctc ttactgtcat [0457] 7501 gccatccgta agatgctttt ctgtgactgg tgagtactca accaagtcat tctgagaata [0458] 7561 gtgtatgcgg cgaccgagtt gctcttgccc ggcgtcaata cgggataata ccgcgccaca [0459] 7621 tagcagaact ttaaaagtgc tcatcattgg aaaacgttct tcggggcgaa aactctcaag [0460] 7681 gatcttaccg ctgttgagat ccagttcgat gtaacccact cgtgcaccca actgatcttc [0461] 7741 agcatctttt actttcacca gcgtttctgg gtgagcaaaa acaggaaggc aaaatgccgc [0462] 7801 aaaaaaggga ataagggcga cacggaaatg ttgaatactc atactcttcc tttttcaata [0463] 7861 ttattgaagc atttatcagg gttattgtct catgagcgga tacatatttg aatgtattta [0464] 7921 gaaaaataaa caaatagggg ttccgcgcac atttccccga aaagtgccac ctgacgtc (SEQ ID NO:8) [0465] Equalizer-M plasmid [0466] source 1..7978 [0467] /organism="pDN-D2ir.tetR_P2A_EGFP_FF4_miRNA, Equalizer-M [0468] plasmid, complete sequence" [0469] /mol_type="other DNA" [0470] /note="other sequences; artificial sequences; vectors." [0471] regulatory 236..858 [0472] /regulatory_class="promoter" [0473] /note="CMV-tetO2 promoter" [0474] regulatory 1401..1613 [0475] /note="bGlob_int" [0476] regulatory 1631..1987 [0477] /note="htetR" [0478] regulatory 3092..3113 [0479] /note="FF4" [0480] regulatory 3120..3495 [0481] /note="miRNA FF4" [0482] gene 2372..3091 [0483] /gene="EGFP" [0484] CDS 2372..3091 [0485] /gene="EGFP" [0486] /codon_start=1 [0487] /product="EGFP" [0488] /translation="MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTL KFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDD GNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIK VNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLL EFVTAAGITLGMDELYK" (SEQ ID NO:9) [0489] BASE COUNT 1879 a 2060 c 2009 g 2030 t [0490] ORIGIN [0491] 1 gacggatcgg gagatctccc gatcccctat ggtgcactct cagtacaatc tgctctgatg [0492] 61 ccgcatagtt aagccagtat ctgctccctg cttgtgtgtt ggaggtcgct gagtagtgcg [0493] 121 cgagcaaaat ttaagctaca acaaggcaag gcttgaccga caattgcatg aagaatctgc [0494] 181 ttagggttag gcgttttgcg ctgcttcgcg atgtacgggc cagatatacg cgttgacatt [0495] 241 gattattgac tagttattaa tagtaatcaa ttacggggtc attagttcat agcccatata [0496] 301 tggagttccg cgttacataa cttacggtaa atggcccgcc tggctgaccg cccaacgacc [0497] 361 cccgcccatt gacgtcaata atgacgtatg ttcccatagt aacgccaata gggactttcc [0498] 421 attgacgtca atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt [0499] 481 atcatatgcc aagtacgccc cctattgacg tcaatgacgg taaatggccc gcctggcatt [0500] 541 atgcccagta catgacctta tgggactttc ctacttggca gtacatctac gtattagtca [0501] 601 tcgctattac catggtgatg cggttttggc agtacatcaa tgggcgtgga tagcggtttg [0502] 661 actcacgggg atttccaagt ctccacccca ttgacgtcaa tgggagtttg ttttggcacc [0503] 721 aaaatcaacg ggactttcca aaatgtcgta acaactccgc cccattgacg caaatgggcg [0504] 781 gtaggcgtgt acggtgggag gtctatataa gcatccctat cagtgataga gatcagatct [0505] 841 ccctatcagt gatagagagc tgttttgacc tccatagaag acaccgggac cgatccagcc [0506] 901 tccggactct agcgtttaaa cttaagctgg gtacccgggg atcctctagg gcctctgagc [0507] 961 tattccagaa gtagtgaaga ggcttttttg gaggcctagg cttttgcaaa aagctccgga [0508] 1021 tcgatcctga gaacttcagg gtgagtttgg ggacccttga ttgttctttc tttttcgcta [0509] 1081 ttgtaaaatt catgttatat ggagggggca aagttttcag ggtgttgttt agaatgggaa [0510] 1141 gatgtccctt gtatcaccat ggaccctcat gataattttg tttctttcac tttctactct [0511] 1201 gttgacaacc attgtctcct cttattttct tttcattttc tgtaactttt tcgttaaact [0512] 1261 ttagcttgca tttgtaacga atttttaaat tcacttttgt ttatttgtca gattgtaagt [0513] 1321 actttctcta atcacttttt tttcaaggca atcagggtat attatattgt acttcagcac [0514] 1381 agttttagag aacaattgtt ataattaaat gataaggtag aatatttctg catataaatt [0515] 1441 ctggctggcg tggaaatatt cttattggta gaaacaacta catcctggtc atcatcctgc [0516] 1501 ctttctcttt atggttacaa tgatatacac tgtttgagat gaggataaaa tactctgagt [0517] 1561 ccaaaccggg cccctctgct aaccatgttc atgccttctt ctttttccta caggtcctgc [0518] 1621 aggcgccacc atggagtcta gactggacaa gagcaaagtc ataaactctg ctctggaatt [0519] 1681 actcaatgaa gtcggtatcg aaggcctgac gacaaggaaa ctcgctcaaa agctgggagt [0520] 1741 tgagcagcct accctgtact ggcacgtgaa gaacaagcgg gccctgctcg atgccctggc [0521] 1801 aatcgagatg ctggacaggc atcataccca cttctgcccc ctggaaggcg agtcatggca [0522] 1861 agactttctg cggaacaacg ccaagtcatt ccgctgtgct ctcctctcac atcgcgacgg [0523] 1921 ggctaaagtg catctcggca cccgcccaac agagaaacag tacgaaaccc tggaaaatca [0524] 1981 gctcgcgttc ctgtgtcagc aaggcttctc cctggagaac gcactgtacg ctctgtccgc [0525] 2041 cgtgggccac tttacactgg gctgcgtatt ggaggatcag gagcatcaag tagcaaaaga [0526] 2101 ggaaagagag acacctacca ccgattctat gcccccactt ctgagacaag caattgagct [0527] 2161 gttcgaccat cagggagccg aacctgcctt ccttttcggc ctggaactaa tcatatgtgg [0528] 2221 cctggagaaa cagctaaagt gcgaaagcgg cgggccaaaa aagaagagaa agggtgacgg [0529] 2281 tgctggttta attaacatgg gaagcggagc tactaacttc agcctgctga agcaggctgg [0530] 2341 agacgtggag gagaaccctg gacctgctag catggtgagc aagggcgagg agctgttcac [0531] 2401 cggggtggtg cccatcctgg tcgagctgga cggcgacgta aacggccaca agttcagcgt [0532] 2461 gtccggcgag ggcgagggcg atgccaccta cggcaagctg accctgaagt tcatctgcac [0533] 2521 caccggcaag ctgcccgtgc cctggcccac cctcgtgacc accctgacct acggcgtgca [0534] 2581 gtgcttcagc cgctaccccg accacatgaa gcagcacgac ttcttcaagt ccgccatgcc [0535] 2641 cgaaggctac gtccaggagc gcaccatctt cttcaaggac gacggcaact acaagacccg [0536] 2701 cgccgaggtg aagttcgagg gcgacaccct ggtgaaccgc atcgagctga agggcatcga [0537] 2761 cttcaaggag gacggcaaca tcctggggca caagctggag tacaactaca acagccacaa [0538] 2821 cgtctatatc atggccgaca agcagaagaa cggcatcaag gtgaacttca agatccgcca [0539] 2881 caacatcgag gacggcagcg tgcagctcgc cgaccactac cagcagaaca cccccatcgg [0540] 2941 cgacggcccc gtgctgctgc ccgacaacca ctacctgagc acccagtccg ccctgagcaa [0541] 3001 agaccccaac gagaagcgcg atcacatggt cctgctggag ttcgtgaccg ccgccgggat [0542] 3061 cactctcggc atggacgagc tgtacaagta gccgcttgaa gtctttaatt aaaaagcttg [0543] 3121 tgagtatgtg ctcgcttcgg cagcacatat actatgtcga atgaggcttc agtactttac [0544] 3181 agaatcgttg cctgcacatc ttggaaacac ttgctgggat tacttcttca ggttaaccca [0545] 3241 acagaaggct cgagtgctgt tgacagtgag cgccgcttga agtctttaat taaatagtga [0546] 3301 agccacagat gtatttaatt aaagacttca agcggtgcct actgcctcgg agaattcaag [0547] 3361 gggctacttt aggagcaatt atcttgttta ctaaaactga ataccttgct atctctttga [0548] 3421 tacattttta caaagctgaa ttaaaatggt ataaattaaa tcactttttt caattgtttc [0549] 3481 cttttttttc ctcaggcggc cgcaatcaac ctctggatta caaaatttgt gaaagattga [0550] 3541 ctggtattct taactatgtt gctcctttta cgctatgtgg atacgctgct ttaatgcctt [0551] 3601 tgtatcatgc tattgcttcc cgtatggctt tcattttctc ctccttgtat aaatcctggt [0552] 3661 tgctgtctct ttatgaggag ttgtggcccg ttgtcaggca acgtggcgtg gtgtgcactg [0553] 3721 tgtttgctga cgcaaccccc actggttggg gcattgccac cacctgtcag ctcctttccg [0554] 3781 ggactttcgc tttccccctc cctattgcca cggcggaact catcgccgcc tgccttgccc [0555] 3841 gctgctggac aggggctcgg ctgttgggca ctgacaattc cgtggtgttg tcctcgagtc [0556] 3901 tagagggccc gtttaaaccc gctgatcagc ctcgactgtg ccttctagtt gccagccatc [0557] 3961 tgttgtttgc ccctcccccg tgccttcctt gaccctggaa ggtgccactc ccactgtcct [0558] 4021 ttcctaataa aatgaggaaa ttgcatcgca ttgtctgagt aggtgtcatt ctattctggg [0559] 4081 gggtggggtg gggcaggaca gcaaggggga ggattgggaa gacaatagca ggcatgctgg [0560] 4141 ggatgcggtg ggctctatgg cttctgaggc ggaaagaacc agctggggct ctagggggta [0561] 4201 tccccacgcg ccctgtagcg gcgcattaag cgcggcgggt gtggtggtta cgcgcagcgt [0562] 4261 gaccgctaca cttgccagcg ccctagcgcc cgctcctttc gctttcttcc cttcctttct [0563] 4321 cgccacgttc gccggctttc cccgtcaagc tctaaatcgg gggctccctt tagggttccg [0564] 4381 atttagtgct ttacggcacc tcgaccccaa aaaacttgat tagggtgatg gttcacgtac [0565] 4441 ctagaagttc ctattccgaa gttcctattc tctagaaagt ataggaactt ccttggccaa [0566] 4501 aaagcctgaa ctcaccgcga cgtctgtcga gaagtttctg atcgaaaagt tcgacagcgt [0567] 4561 ctccgacctg atgcagctct cggagggcga agaatctcgt gctttcagct tcgatgtagg [0568] 4621 agggcgtgga tatgtcctgc gggtaaatag ctgcgccgat ggtttctaca aagatcgtta [0569] 4681 tgtttatcgg cactttgcat cggccgcgct cccgattccg gaagtgcttg acattgggga [0570] 4741 attcagcgag agcctgacct attgcatctc ccgccgtgca cagggtgtca cgttgcaaga [0571] 4801 cctgcctgaa accgaactgc ccgctgttct gcagccggtc gcggaggcca tggatgcgat [0572] 4861 cgctgcggcc gatcttagcc agacgagcgg gttcggccca ttcggaccgc aaggaatcgg [0573] 4921 tcaatacact acatggcgtg atttcatatg cgcgattgct gatccccatg tgtatcactg [0574] 4981 gcaaactgtg atggacgaca ccgtcagtgc gtccgtcgcg caggctctcg atgagctgat [0575] 5041 gctttgggcc gaggactgcc ccgaagtccg gcacctcgtg cacgcggatt tcggctccaa [0576] 5101 caatgtcctg acggacaatg gccgcataac agcggtcatt gactggagcg aggcgatgtt [0577] 5161 cggggattcc caatacgagg tcgccaacat cttcttctgg aggccgtggt tggcttgtat [0578] 5221 ggagcagcag acgcgctact tcgagcggag gcatccggag cttgcaggat cgccgcggct [0579] 5281 ccgggcgtat atgctccgca ttggtcttga ccaactctat cagagcttgg ttgacggcaa [0580] 5341 tttcgatgat gcagcttggg cgcagggtcg atgcgacgca atcgtccgat ccggagccgg [0581] 5401 gactgtcggg cgtacacaaa tcgcccgcag aagcgcggcc gtctggaccg atggctgtgt [0582] 5461 agaagtactc gccgatagtg gaaaccgacg ccccagcact cgtccgaggg caaaggaata [0583] 5521 gcacgtacta cgagatttcg attccaccgc cgccttctat gaaaggttgg gcttcggaat [0584] 5581 cgttttccgg gacgccggct ggatgatcct ccagcgcggg gatctcatgc tggagttctt [0585] 5641 cgcccacccc aacttgttta ttgcagctta taatggttac aaataaagca atagcatcac [0586] 5701 aaatttcaca aataaagcat ttttttcact gcattctagt tgtggtttgt ccaaactcat [0587] 5761 caatgtatct tatcatgtct gtataccgtc gacctctagc tagagcttgg cgtaatcatg [0588] 5821 gtcatagctg tttcctgtgt gaaattgtta tccgctcaca attccacaca acatacgagc [0589] 5881 cggaagcata aagtgtaaag cctggggtgc ctaatgagtg agctaactca cattaattgc [0590] 5941 gttgcgctca ctgcccgctt tccagtcggg aaacctgtcg tgccagctgc attaatgaat [0591] 6001 cggccaacgc gcggggagag gcggtttgcg tattgggcgc tcttccgctt cctcgctcac [0592] 6061 tgactcgctg cgctcggtcg ttcggctgcg gcgagcggta tcagctcact caaaggcggt [0593] 6121 aatacggtta tccacagaat caggggataa cgcaggaaag aacatgtgag caaaaggcca [0594] 6181 gcaaaaggcc aggaaccgta aaaaggccgc gttgctggcg tttttccata ggctccgccc [0595] 6241 ccctgacgag catcacaaaa atcgacgctc aagtcagagg tggcgaaacc cgacaggact [0596] 6301 ataaagatac caggcgtttc cccctggaag ctccctcgtg cgctctcctg ttccgaccct [0597] 6361 gccgcttacc ggatacctgt ccgcctttct cccttcggga agcgtggcgc tttctcatag [0598] 6421 ctcacgctgt aggtatctca gttcggtgta ggtcgttcgc tccaagctgg gctgtgtgca [0599] 6481 cgaacccccc gttcagcccg accgctgcgc cttatccggt aactatcgtc ttgagtccaa [0600] 6541 cccggtaaga cacgacttat cgccactggc agcagccact ggtaacagga ttagcagagc [0601] 6601 gaggtatgta ggcggtgcta cagagttctt gaagtggtgg cctaactacg gctacactag [0602] 6661 aaggacagta tttggtatct gcgctctgct gaagccagtt accttcggaa aaagagttgg [0603] 6721 tagctcttga tccggcaaac aaaccaccgc tggtagcggt ggtttttttg tttgcaagca [0604] 6781 gcagattacg cgcagaaaaa aaggatctca agaagatcct ttgatctttt ctacggggtc [0605] 6841 tgacgctcag tggaacgaaa actcacgtta agggattttg gtcatgagat tatcaaaaag [0606] 6901 gatcttcacc tagatccttt taaattaaaa atgaagtttt aaatcaatct aaagtatata [0607] 6961 tgagtaaact tggtctgaca gttaccaatg cttaatcagt gaggcaccta tctcagcgat [0608] 7021 ctgtctattt cgttcatcca tagttgcctg actccccgtc gtgtagataa ctacgatacg [0609] 7081 ggagggctta ccatctggcc ccagtgctgc aatgataccg cgagacccac gctcaccggc [0610] 7141 tccagattta tcagcaataa accagccagc cggaagggcc gagcgcagaa gtggtcctgc [0611] 7201 aactttatcc gcctccatcc agtctattaa ttgttgccgg gaagctagag taagtagttc [0612] 7261 gccagttaat agtttgcgca acgttgttgc cattgctaca ggcatcgtgg tgtcacgctc [0613] 7321 gtcgtttggt atggcttcat tcagctccgg ttcccaacga tcaaggcgag ttacatgatc [0614] 7381 ccccatgttg tgcaaaaaag cggttagctc cttcggtcct ccgatcgttg tcagaagtaa [0615] 7441 gttggccgca gtgttatcac tcatggttat ggcagcactg cataattctc ttactgtcat [0616] 7501 gccatccgta agatgctttt ctgtgactgg tgagtactca accaagtcat tctgagaata [0617] 7561 gtgtatgcgg cgaccgagtt gctcttgccc ggcgtcaata cgggataata ccgcgccaca [0618] 7621 tagcagaact ttaaaagtgc tcatcattgg aaaacgttct tcggggcgaa aactctcaag [0619] 7681 gatcttaccg ctgttgagat ccagttcgat gtaacccact cgtgcaccca actgatcttc [0620] 7741 agcatctttt actttcacca gcgtttctgg gtgagcaaaa acaggaaggc aaaatgccgc [0621] 7801 aaaaaaggga ataagggcga cacggaaatg ttgaatactc atactcttcc tttttcaata [0622] 7861 ttattgaagc atttatcagg gttattgtct catgagcgga tacatatttg aatgtattta [0623] 7921 gaaaaataaa caaatagggg ttccgcgcac atttccccga aaagtgccac ctgacgtc (SEQ ID NO:10) [0624] Equalizer-L plasmid [0625] source 1..8000 [0626] /organism="pDN-D2ir.FF4_tetR_P2A_EGFP_FF4_miRNA, [0627] Equalizer-L plasmid, complete sequence" [0628] /mol_type="other DNA" [0629] /note="other sequences; artificial sequences; vectors." [0630] regulatory 236..858 [0631] /regulatory_class="promoter" [0632] /note="CMV-tetO2 promoter" [0633] regulatory 1401..1613 [0634] /note="bGlob_int" [0635] regulatory 1622..1643 [0636] /note="FF4" [0637] regulatory 1653..2009 [0638] /note="htetR" [0639] regulatory 3114..3135 [0640] /note="FF4" [0641] regulatory 3142..3517 [0642] /note="miRNA FF4" [0643] gene 2394..3113 [0644] /gene="EGFP" [0645] CDS 2394..3113 [0646] /gene="EGFP" [0647] /codon_start=1 [0648] /product="EGFP" [0649] /translation="MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTL KFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDD GNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIK VNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLL EFVTAAGITLGMDELYK" (SEQ ID NO:11) [0650] BASE COUNT 1886 a 2064 c 2012 g 2038 t [0651] ORIGIN [0652] 1 gacggatcgg gagatctccc gatcccctat ggtgcactct cagtacaatc tgctctgatg [0653] 61 ccgcatagtt aagccagtat ctgctccctg cttgtgtgtt ggaggtcgct gagtagtgcg [0654] 121 cgagcaaaat ttaagctaca acaaggcaag gcttgaccga caattgcatg aagaatctgc [0655] 181 ttagggttag gcgttttgcg ctgcttcgcg atgtacgggc cagatatacg cgttgacatt [0656] 241 gattattgac tagttattaa tagtaatcaa ttacggggtc attagttcat agcccatata [0657] 301 tggagttccg cgttacataa cttacggtaa atggcccgcc tggctgaccg cccaacgacc [0658] 361 cccgcccatt gacgtcaata atgacgtatg ttcccatagt aacgccaata gggactttcc [0659] 421 attgacgtca atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt [0660] 481 atcatatgcc aagtacgccc cctattgacg tcaatgacgg taaatggccc gcctggcatt [0661] 541 atgcccagta catgacctta tgggactttc ctacttggca gtacatctac gtattagtca [0662] 601 tcgctattac catggtgatg cggttttggc agtacatcaa tgggcgtgga tagcggtttg [0663] 661 actcacgggg atttccaagt ctccacccca ttgacgtcaa tgggagtttg ttttggcacc [0664] 721 aaaatcaacg ggactttcca aaatgtcgta acaactccgc cccattgacg caaatgggcg [0665] 781 gtaggcgtgt acggtgggag gtctatataa gcatccctat cagtgataga gatcagatct [0666] 841 ccctatcagt gatagagagc tgttttgacc tccatagaag acaccgggac cgatccagcc [0667] 901 tccggactct agcgtttaaa cttaagctgg gtacccgggg atcctctagg gcctctgagc [0668] 961 tattccagaa gtagtgaaga ggcttttttg gaggcctagg cttttgcaaa aagctccgga [0669] 1021 tcgatcctga gaacttcagg gtgagtttgg ggacccttga ttgttctttc tttttcgcta [0670] 1081 ttgtaaaatt catgttatat ggagggggca aagttttcag ggtgttgttt agaatgggaa [0671] 1141 gatgtccctt gtatcaccat ggaccctcat gataattttg tttctttcac tttctactct [0672] 1201 gttgacaacc attgtctcct cttattttct tttcattttc tgtaactttt tcgttaaact [0673] 1261 ttagcttgca tttgtaacga atttttaaat tcacttttgt ttatttgtca gattgtaagt [0674] 1321 actttctcta atcacttttt tttcaaggca atcagggtat attatattgt acttcagcac [0675] 1381 agttttagag aacaattgtt ataattaaat gataaggtag aatatttctg catataaatt [0676] 1441 ctggctggcg tggaaatatt cttattggta gaaacaacta catcctggtc atcatcctgc [0677] 1501 ctttctcttt atggttacaa tgatatacac tgtttgagat gaggataaaa tactctgagt [0678] 1561 ccaaaccggg cccctctgct aaccatgttc atgccttctt ctttttccta caggtcctgc [0679] 1621 accgcttgaa gtctttaatt aaaggcgcca ccatggagtc tagactggac aagagcaaag [0680] 1681 tcataaactc tgctctggaa ttactcaatg aagtcggtat cgaaggcctg acgacaagga [0681] 1741 aactcgctca aaagctggga gttgagcagc ctaccctgta ctggcacgtg aagaacaagc [0682] 1801 gggccctgct cgatgccctg gcaatcgaga tgctggacag gcatcatacc cacttctgcc [0683] 1861 ccctggaagg cgagtcatgg caagactttc tgcggaacaa cgccaagtca ttccgctgtg [0684] 1921 ctctcctctc acatcgcgac ggggctaaag tgcatctcgg cacccgccca acagagaaac [0685] 1981 agtacgaaac cctggaaaat cagctcgcgt tcctgtgtca gcaaggcttc tccctggaga [0686] 2041 acgcactgta cgctctgtcc gccgtgggcc actttacact gggctgcgta ttggaggatc [0687] 2101 aggagcatca agtagcaaaa gaggaaagag agacacctac caccgattct atgcccccac [0688] 2161 ttctgagaca agcaattgag ctgttcgacc atcagggagc cgaacctgcc ttccttttcg [0689] 2221 gcctggaact aatcatatgt ggcctggaga aacagctaaa gtgcgaaagc ggcgggccaa [0690] 2281 aaaagaagag aaagggtgac ggtgctggtt taattaacat gggaagcgga gctactaact [0691] 2341 tcagcctgct gaagcaggct ggagacgtgg aggagaaccc tggacctgct agcatggtga [0692] 2401 gcaagggcga ggagctgttc accggggtgg tgcccatcct ggtcgagctg gacggcgacg [0693] 2461 taaacggcca caagttcagc gtgtccggcg agggcgaggg cgatgccacc tacggcaagc [0694] 2521 tgaccctgaa gttcatctgc accaccggca agctgcccgt gccctggccc accctcgtga [0695] 2581 ccaccctgac ctacggcgtg cagtgcttca gccgctaccc cgaccacatg aagcagcacg [0696] 2641 acttcttcaa gtccgccatg cccgaaggct acgtccagga gcgcaccatc ttcttcaagg [0697] 2701 acgacggcaa ctacaagacc cgcgccgagg tgaagttcga gggcgacacc ctggtgaacc [0698] 2761 gcatcgagct gaagggcatc gacttcaagg aggacggcaa catcctgggg cacaagctgg [0699] 2821 agtacaacta caacagccac aacgtctata tcatggccga caagcagaag aacggcatca [0700] 2881 aggtgaactt caagatccgc cacaacatcg aggacggcag cgtgcagctc gccgaccact [0701] 2941 accagcagaa cacccccatc ggcgacggcc ccgtgctgct gcccgacaac cactacctga [0702] 3001 gcacccagtc cgccctgagc aaagacccca acgagaagcg cgatcacatg gtcctgctgg [0703] 3061 agttcgtgac cgccgccggg atcactctcg gcatggacga gctgtacaag tagccgcttg [0704] 3121 aagtctttaa ttaaaaagct tgtgagtatg tgctcgcttc ggcagcacat atactatgtc [0705] 3181 gaatgaggct tcagtacttt acagaatcgt tgcctgcaca tcttggaaac acttgctggg [0706] 3241 attacttctt caggttaacc caacagaagg ctcgagtgct gttgacagtg agcgccgctt [0707] 3301 gaagtcttta attaaatagt gaagccacag atgtatttaa ttaaagactt caagcggtgc [0708] 3361 ctactgcctc ggagaattca aggggctact ttaggagcaa ttatcttgtt tactaaaact [0709] 3421 gaataccttg ctatctcttt gatacatttt tacaaagctg aattaaaatg gtataaatta [0710] 3481 aatcactttt ttcaattgtt tccttttttt tcctcaggcg gccgcaatca acctctggat [0711] 3541 tacaaaattt gtgaaagatt gactggtatt cttaactatg ttgctccttt tacgctatgt [0712] 3601 ggatacgctg ctttaatgcc tttgtatcat gctattgctt cccgtatggc tttcattttc [0713] 3661 tcctccttgt ataaatcctg gttgctgtct ctttatgagg agttgtggcc cgttgtcagg [0714] 3721 caacgtggcg tggtgtgcac tgtgtttgct gacgcaaccc ccactggttg gggcattgcc [0715] 3781 accacctgtc agctcctttc cgggactttc gctttccccc tccctattgc cacggcggaa [0716] 3841 ctcatcgccg cctgccttgc ccgctgctgg acaggggctc ggctgttggg cactgacaat [0717] 3901 tccgtggtgt tgtcctcgag tctagagggc ccgtttaaac ccgctgatca gcctcgactg [0718] 3961 tgccttctag ttgccagcca tctgttgttt gcccctcccc cgtgccttcc ttgaccctgg [0719] 4021 aaggtgccac tcccactgtc ctttcctaat aaaatgagga aattgcatcg cattgtctga [0720] 4081 gtaggtgtca ttctattctg gggggtgggg tggggcagga cagcaagggg gaggattggg [0721] 4141 aagacaatag caggcatgct ggggatgcgg tgggctctat ggcttctgag gcggaaagaa [0722] 4201 ccagctgggg ctctaggggg tatccccacg cgccctgtag cggcgcatta agcgcggcgg [0723] 4261 gtgtggtggt tacgcgcagc gtgaccgcta cacttgccag cgccctagcg cccgctcctt [0724] 4321 tcgctttctt cccttccttt ctcgccacgt tcgccggctt tccccgtcaa gctctaaatc [0725] 4381 gggggctccc tttagggttc cgatttagtg ctttacggca cctcgacccc aaaaaacttg [0726] 4441 attagggtga tggttcacgt acctagaagt tcctattccg aagttcctat tctctagaaa [0727] 4501 gtataggaac ttccttggcc aaaaagcctg aactcaccgc gacgtctgtc gagaagtttc [0728] 4561 tgatcgaaaa gttcgacagc gtctccgacc tgatgcagct ctcggagggc gaagaatctc [0729] 4621 gtgctttcag cttcgatgta ggagggcgtg gatatgtcct gcgggtaaat agctgcgccg [0730] 4681 atggtttcta caaagatcgt tatgtttatc ggcactttgc atcggccgcg ctcccgattc [0731] 4741 cggaagtgct tgacattggg gaattcagcg agagcctgac ctattgcatc tcccgccgtg [0732] 4801 cacagggtgt cacgttgcaa gacctgcctg aaaccgaact gcccgctgtt ctgcagccgg [0733] 4861 tcgcggaggc catggatgcg atcgctgcgg ccgatcttag ccagacgagc gggttcggcc [0734] 4921 cattcggacc gcaaggaatc ggtcaataca ctacatggcg tgatttcata tgcgcgattg [0735] 4981 ctgatcccca tgtgtatcac tggcaaactg tgatggacga caccgtcagt gcgtccgtcg [0736] 5041 cgcaggctct cgatgagctg atgctttggg ccgaggactg ccccgaagtc cggcacctcg [0737] 5101 tgcacgcgga tttcggctcc aacaatgtcc tgacggacaa tggccgcata acagcggtca [0738] 5161 ttgactggag cgaggcgatg ttcggggatt cccaatacga ggtcgccaac atcttcttct [0739] 5221 ggaggccgtg gttggcttgt atggagcagc agacgcgcta cttcgagcgg aggcatccgg [0740] 5281 agcttgcagg atcgccgcgg ctccgggcgt atatgctccg cattggtctt gaccaactct [0741] 5341 atcagagctt ggttgacggc aatttcgatg atgcagcttg ggcgcagggt cgatgcgacg [0742] 5401 caatcgtccg atccggagcc gggactgtcg ggcgtacaca aatcgcccgc agaagcgcgg [0743] 5461 ccgtctggac cgatggctgt gtagaagtac tcgccgatag tggaaaccga cgccccagca [0744] 5521 ctcgtccgag ggcaaaggaa tagcacgtac tacgagattt cgattccacc gccgccttct [0745] 5581 atgaaaggtt gggcttcgga atcgttttcc gggacgccgg ctggatgatc ctccagcgcg [0746] 5641 gggatctcat gctggagttc ttcgcccacc ccaacttgtt tattgcagct tataatggtt [0747] 5701 acaaataaag caatagcatc acaaatttca caaataaagc atttttttca ctgcattcta [0748] 5761 gttgtggttt gtccaaactc atcaatgtat cttatcatgt ctgtataccg tcgacctcta [0749] 5821 gctagagctt ggcgtaatca tggtcatagc tgtttcctgt gtgaaattgt tatccgctca [0750] 5881 caattccaca caacatacga gccggaagca taaagtgtaa agcctggggt gcctaatgag [0751] 5941 tgagctaact cacattaatt gcgttgcgct cactgcccgc tttccagtcg ggaaacctgt [0752] 6001 cgtgccagct gcattaatga atcggccaac gcgcggggag aggcggtttg cgtattgggc [0753] 6061 gctcttccgc ttcctcgctc actgactcgc tgcgctcggt cgttcggctg cggcgagcgg [0754] 6121 tatcagctca ctcaaaggcg gtaatacggt tatccacaga atcaggggat aacgcaggaa [0755] 6181 agaacatgtg agcaaaaggc cagcaaaagg ccaggaaccg taaaaaggcc gcgttgctgg [0756] 6241 cgtttttcca taggctccgc ccccctgacg agcatcacaa aaatcgacgc tcaagtcaga [0757] 6301 ggtggcgaaa cccgacagga ctataaagat accaggcgtt tccccctgga agctccctcg [0758] 6361 tgcgctctcc tgttccgacc ctgccgctta ccggatacct gtccgccttt ctcccttcgg [0759] 6421 gaagcgtggc gctttctcat agctcacgct gtaggtatct cagttcggtg taggtcgttc [0760] 6481 gctccaagct gggctgtgtg cacgaacccc ccgttcagcc cgaccgctgc gccttatccg [0761] 6541 gtaactatcg tcttgagtcc aacccggtaa gacacgactt atcgccactg gcagcagcca [0762] 6601 ctggtaacag gattagcaga gcgaggtatg taggcggtgc tacagagttc ttgaagtggt [0763] 6661 ggcctaacta cggctacact agaaggacag tatttggtat ctgcgctctg ctgaagccag [0764] 6721 ttaccttcgg aaaaagagtt ggtagctctt gatccggcaa acaaaccacc gctggtagcg [0765] 6781 gtggtttttt tgtttgcaag cagcagatta cgcgcagaaa aaaaggatct caagaagatc [0766] 6841 ctttgatctt ttctacgggg tctgacgctc agtggaacga aaactcacgt taagggattt [0767] 6901 tggtcatgag attatcaaaa aggatcttca cctagatcct tttaaattaa aaatgaagtt [0768] 6961 ttaaatcaat ctaaagtata tatgagtaaa cttggtctga cagttaccaa tgcttaatca [0769] 7021 gtgaggcacc tatctcagcg atctgtctat ttcgttcatc catagttgcc tgactccccg [0770] 7081 tcgtgtagat aactacgata cgggagggct taccatctgg ccccagtgct gcaatgatac [0771] 7141 cgcgagaccc acgctcaccg gctccagatt tatcagcaat aaaccagcca gccggaaggg [0772] 7201 ccgagcgcag aagtggtcct gcaactttat ccgcctccat ccagtctatt aattgttgcc [0773] 7261 gggaagctag agtaagtagt tcgccagtta atagtttgcg caacgttgtt gccattgcta [0774] 7321 caggcatcgt ggtgtcacgc tcgtcgtttg gtatggcttc attcagctcc ggttcccaac [0775] 7381 gatcaaggcg agttacatga tcccccatgt tgtgcaaaaa agcggttagc tccttcggtc [0776] 7441 ctccgatcgt tgtcagaagt aagttggccg cagtgttatc actcatggtt atggcagcac [0777] 7501 tgcataattc tcttactgtc atgccatccg taagatgctt ttctgtgact ggtgagtact [0778] 7561 caaccaagtc attctgagaa tagtgtatgc ggcgaccgag ttgctcttgc ccggcgtcaa [0779] 7621 tacgggataa taccgcgcca catagcagaa ctttaaaagt gctcatcatt ggaaaacgtt [0780] 7681 cttcggggcg aaaactctca aggatcttac cgctgttgag atccagttcg atgtaaccca [0781] 7741 ctcgtgcacc caactgatct tcagcatctt ttactttcac cagcgtttct gggtgagcaa [0782] 7801 aaacaggaag gcaaaatgcc gcaaaaaagg gaataagggc gacacggaaa tgttgaatac [0783] 7861 tcatactctt cctttttcaa tattattgaa gcatttatca gggttattgt ctcatgagcg [0784] 7921 gatacatatt tgaatgtatt tagaaaaata aacaaatagg ggttccgcgc acatttcccc [0785] 7981 gaaaagtgcc acctgacgtc (SEQ ID NO:12) [0786] LacI-based NF-only circuit; pDN- D2ir_CMV_immearly_gap_LacO2_LacI based NF-only circuit,
Figure imgf000100_0001
complete sequence [0787] source 1..8561 [0788] /organism="pDN- D2ir_CMV_immearly_gap_LacO2_LacI(Krab)_P2A_EGFP, complete sequence" [0789] /mol_type="other DNA" [0790] /note="other sequences; artificial sequences; vectors." [0791] regulatory 236..812 [0792] /regulatory_class="promoter" [0793] /note="CMV_immearly promoter" [0794] regulatory 929..990 [0795] /regulatory_class="other" [0796] /note="LacO2" [0797] regulatory 1182..1754 [0798] /regulatory_class="other" [0799] /note="bGlob_int" [0800] regulatory 1772..3235 [0801] /regulatory_class="other" [0802] /note="LacI_Krab" [0803] gene 3359..4078 [0804] /gene="EGFP" [0805] CDS 3359..4078 [0806] /gene="EGFP" [0807] /codon_start=1 [0808] /product="EGFP" [0809] /translation="MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTL KFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDD GNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIK VNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLL EFVTAAGITLGMDELYK" (SEQ ID NO:13) [0810] BASE COUNT 1984 a 2238 c 2214 g 2125 t [0811] ORIGIN [0812] 1 gacggatcgg gagatctccc gatcccctat ggtgcactct cagtacaatc tgctctgatg [0813] 61 ccgcatagtt aagccagtat ctgctccctg cttgtgtgtt ggaggtcgct gagtagtgcg [0814] 121 cgagcaaaat ttaagctaca acaaggcaag gcttgaccga caattgcatg aagaatctgc [0815] 181 ttagggttag gcgttttgcg ctgcttcgcg atgtacgggc cagatatacg cgttgacatt [0816] 241 gattattgac tagttattaa tagtaatcaa ttacggggtc attagttcat agcccatata [0817] 301 tggagttccg cgttacataa cttacggtaa atggcccgcc tggctgaccg cccaacgacc [0818] 361 cccgcccatt gacgtcaata atgacgtatg ttcccatagt aacgccaata gggactttcc [0819] 421 attgacgtca atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt [0820] 481 atcatatgcc aagtacgccc cctattgacg tcaatgacgg taaatggccc gcctggcatt [0821] 541 atgcccagta catgacctta tgggactttc ctacttggca gtacatctac gtattagtca [0822] 601 tcgctattac catggtgatg cggttttggc agtacatcaa tgggcgtgga tagcggtttg [0823] 661 actcacgggg atttccaagt ctccacccca ttgacgtcaa tgggagtttg ttttggcacc [0824] 721 aaaatcaacg ggactttcca aaatgtcgta acaactccgc cccattgacg caaatgggcg [0825] 781 gtaggcgtgt acggtgggag gtctatataa gcagagctcg tttagtgaac cgtcagatcg [0826] 841 cctggagcaa ttccacaaca cttttgtctt ataccaactt tccgtaccac ttcctaccct [0827] 901 cgtaaagtcg acaccggggc ccagatctaa ttgtgagcgc tcacaattcc acaacctaga [0828] 961 attgtgagcg ctcacaattc cacaacctag ggatcccgcg ctgttttgac ctccatagaa [0829] 1021 gacaccggga ccgatccagc ctccggactc tagcgtttaa acttaagctg ggtacccggg [0830] 1081 gatcctctag ggcctctgag ctattccaga agtagtgaag aggctttttt ggaggcctag [0831] 1141 gcttttgcaa aaagctccgg atcgatcctg agaacttcag ggtgagtttg gggacccttg [0832] 1201 attgttcttt ctttttcgct attgtaaaat tcatgttata tggagggggc aaagttttca [0833] 1261 gggtgttgtt tagaatggga agatgtccct tgtatcacca tggaccctca tgataatttt [0834] 1321 gtttctttca ctttctactc tgttgacaac cattgtctcc tcttattttc ttttcatttt [0835] 1381 ctgtaacttt ttcgttaaac tttagcttgc atttgtaacg aatttttaaa ttcacttttg [0836] 1441 tttatttgtc agattgtaag tactttctct aatcactttt ttttcaaggc aatcagggta [0837] 1501 tattatattg tacttcagca cagttttaga gaacaattgt tataattaaa tgataaggta [0838] 1561 gaatatttct gcatataaat tctggctggc gtggaaatat tcttattggt agaaacaact [0839] 1621 acatcctggt catcatcctg cctttctctt tatggttaca atgatataca ctgtttgaga [0840] 1681 tgaggataaa atactctgag tccaaaccgg gcccctctgc taaccatgtt catgccttct [0841] 1741 tctttttcct acaggtcctg caggcgccac catgaaacca gtaacgttat acgatgtcgc [0842] 1801 agagtatgcc ggtgtctctt atcagaccgt ttcccgcgtg gtgaaccagg ccagccacgt [0843] 1861 ttctgcgaaa acgcgggaaa aagtggaagc ggcgatggcg gagctgaatt acattcccaa [0844] 1921 ccgcgtggca caacaactgg cgggcaaaca gtcgttgctg attggcgttg ccacctccag [0845] 1981 tctggccctg cacgcgccgt cgcaaattgt cgcggcgatt aaatctcgcg ccgatcaact [0846] 2041 gggtgccagc gtggtggtgt cgatggtaga acgaagcggc gtcgaagcct gtaaagcggc [0847] 2101 ggtgcacaat cttctcgcgc aacgcgtcag tgggctgatc attaactatc cgctggatga [0848] 2161 ccaggatgcc attgctgtgg aagctgcctg cactaatgtt ccggcgttat ttcttgatgt [0849] 2221 ctctgaccag acacccatca acagtattat tttctcccat gaagacggta cgcgactggg [0850] 2281 cgtggagcat ctggtcgcat tgggtcacca gcaaatcgcg ctgttagcgg gcccattaag [0851] 2341 ttctgtctcg gcgcgtctgc gtctggctgg ctggcataaa tatctcactc gcaatcaaat [0852] 2401 tcagccgata gcggaacggg aaggcgactg gagtgccatg tccggttttc aacaaaccat [0853] 2461 gcaaatgctg aatgagggca tcgttcccac tgcgatgctg gttgccaacg atcagatggc [0854] 2521 gctgggcgca atgcgcgcca ttaccgagtc cgggctgcgc gttggtgcgg atatctcggt [0855] 2581 agtgggatac gacgataccg aagacagctc atgttatatc ccgccgttaa ccaccatcaa [0856] 2641 acaggatttt cgcctgctgg ggcaaaccag cgtggaccgc ttgctgcaac tctctcaggg [0857] 2701 ccaggcggtg aagggcaatc agctgttgcc cgtctcactg gtgaaaagaa aaaccaccct [0858] 2761 ggcgcccaat acgcaaaccg cctctccccg cgcgttggcc gattcattaa tgcagctggc [0859] 2821 acgacaggtt tcccgactgg aaagcgggca gccaaaaaag aagagaaagg tcgacggcgg [0860] 2881 tggtgctttg tctcctcagc actctgctgt cactcaagga agtatcatca agaacaagga [0861] 2941 gggcatggat gctaagtcac taactgcctg gtcccggaca ctggtgacct tcaaggatgt [0862] 3001 atttgtggac ttcaccaggg aggagtggaa gctgctggac actgctcagc agatcgtgta [0863] 3061 cagaaatgtg atgctggaga actataagaa cctggtttcc ttgggttatc agcttactaa [0864] 3121 gccagatgtg atcctccggt tggagaaggg agaagagccc tggctggtgg agagagaaat [0865] 3181 tcaccaagag acccatcctg attcagagac tgcatttgaa atcaaatcat cagttggcgg [0866] 3241 gccaaaaaag aagagaaagg gtgacggtgc tggtttaatt aacatgggaa gcggagctac [0867] 3301 taacttcagc ctgctgaagc aggctggaga cgtggaggag aaccctggac ctgctagcat [0868] 3361 ggtgagcaag ggcgaggagc tgttcaccgg ggtggtgccc atcctggtcg agctggacgg [0869] 3421 cgacgtaaac ggccacaagt tcagcgtgtc cggcgagggc gagggcgatg ccacctacgg [0870] 3481 caagctgacc ctgaagttca tctgcaccac cggcaagctg cccgtgccct ggcccaccct [0871] 3541 cgtgaccacc ctgacctacg gcgtgcagtg cttcagccgc taccccgacc acatgaagca [0872] 3601 gcacgacttc ttcaagtccg ccatgcccga aggctacgtc caggagcgca ccatcttctt [0873] 3661 caaggacgac ggcaactaca agacccgcgc cgaggtgaag ttcgagggcg acaccctggt [0874] 3721 gaaccgcatc gagctgaagg gcatcgactt caaggaggac ggcaacatcc tggggcacaa [0875] 3781 gctggagtac aactacaaca gccacaacgt ctatatcatg gccgacaagc agaagaacgg [0876] 3841 catcaaggtg aacttcaaga tccgccacaa catcgaggac ggcagcgtgc agctcgccga [0877] 3901 ccactaccag cagaacaccc ccatcggcga cggccccgtg ctgctgcccg acaaccacta [0878] 3961 cctgagcacc cagtccgccc tgagcaaaga ccccaacgag aagcgcgatc acatggtcct [0879] 4021 gctggagttc gtgaccgccg ccgggatcac tctcggcatg gacgagctgt acaagtaggc [0880] 4081 ggccgcaatc aacctctgga ttacaaaatt tgtgaaagat tgactggtat tcttaactat [0881] 4141 gttgctcctt ttacgctatg tggatacgct gctttaatgc ctttgtatca tgctattgct [0882] 4201 tcccgtatgg ctttcatttt ctcctccttg tataaatcct ggttgctgtc tctttatgag [0883] 4261 gagttgtggc ccgttgtcag gcaacgtggc gtggtgtgca ctgtgtttgc tgacgcaacc [0884] 4321 cccactggtt ggggcattgc caccacctgt cagctccttt ccgggacttt cgctttcccc [0885] 4381 ctccctattg ccacggcgga actcatcgcc gcctgccttg cccgctgctg gacaggggct [0886] 4441 cggctgttgg gcactgacaa ttccgtggtg ttgtcctcga gtctagaggg cccgtttaaa [0887] 4501 cccgctgatc agcctcgact gtgccttcta gttgccagcc atctgttgtt tgcccctccc [0888] 4561 ccgtgccttc cttgaccctg gaaggtgcca ctcccactgt cctttcctaa taaaatgagg [0889] 4621 aaattgcatc gcattgtctg agtaggtgtc attctattct ggggggtggg gtggggcagg [0890] 4681 acagcaaggg ggaggattgg gaagacaata gcaggcatgc tggggatgcg gtgggctcta [0891] 4741 tggcttctga ggcggaaaga accagctggg gctctagggg gtatccccac gcgccctgta [0892] 4801 gcggcgcatt aagcgcggcg ggtgtggtgg ttacgcgcag cgtgaccgct acacttgcca [0893] 4861 gcgccctagc gcccgctcct ttcgctttct tcccttcctt tctcgccacg ttcgccggct [0894] 4921 ttccccgtca agctctaaat cgggggctcc ctttagggtt ccgatttagt gctttacggc [0895] 4981 acctcgaccc caaaaaactt gattagggtg atggttcacg tacctagaag ttcctattcc [0896] 5041 gaagttccta ttctctagaa agtataggaa cttccttggc caaaaagcct gaactcaccg [0897] 5101 cgacgtctgt cgagaagttt ctgatcgaaa agttcgacag cgtctccgac ctgatgcagc [0898] 5161 tctcggaggg cgaagaatct cgtgctttca gcttcgatgt aggagggcgt ggatatgtcc [0899] 5221 tgcgggtaaa tagctgcgcc gatggtttct acaaagatcg ttatgtttat cggcactttg [0900] 5281 catcggccgc gctcccgatt ccggaagtgc ttgacattgg ggaattcagc gagagcctga [0901] 5341 cctattgcat ctcccgccgt gcacagggtg tcacgttgca agacctgcct gaaaccgaac [0902] 5401 tgcccgctgt tctgcagccg gtcgcggagg ccatggatgc gatcgctgcg gccgatctta [0903] 5461 gccagacgag cgggttcggc ccattcggac cgcaaggaat cggtcaatac actacatggc [0904] 5521 gtgatttcat atgcgcgatt gctgatcccc atgtgtatca ctggcaaact gtgatggacg [0905] 5581 acaccgtcag tgcgtccgtc gcgcaggctc tcgatgagct gatgctttgg gccgaggact [0906] 5641 gccccgaagt ccggcacctc gtgcacgcgg atttcggctc caacaatgtc ctgacggaca [0907] 5701 atggccgcat aacagcggtc attgactgga gcgaggcgat gttcggggat tcccaatacg [0908] 5761 aggtcgccaa catcttcttc tggaggccgt ggttggcttg tatggagcag cagacgcgct [0909] 5821 acttcgagcg gaggcatccg gagcttgcag gatcgccgcg gctccgggcg tatatgctcc [0910] 5881 gcattggtct tgaccaactc tatcagagct tggttgacgg caatttcgat gatgcagctt [0911] 5941 gggcgcaggg tcgatgcgac gcaatcgtcc gatccggagc cgggactgtc gggcgtacac [0912] 6001 aaatcgcccg cagaagcgcg gccgtctgga ccgatggctg tgtagaagta ctcgccgata [0913] 6061 gtggaaaccg acgccccagc actcgtccga gggcaaagga atagcacgta ctacgagatt [0914] 6121 tcgattccac cgccgccttc tatgaaaggt tgggcttcgg aatcgttttc cgggacgccg [0915] 6181 gctggatgat cctccagcgc ggggatctca tgctggagtt cttcgcccac cccaacttgt [0916] 6241 ttattgcagc ttataatggt tacaaataaa gcaatagcat cacaaatttc acaaataaag [0917] 6301 catttttttc actgcattct agttgtggtt tgtccaaact catcaatgta tcttatcatg [0918] 6361 tctgtatacc gtcgacctct agctagagct tggcgtaatc atggtcatag ctgtttcctg [0919] 6421 tgtgaaattg ttatccgctc acaattccac acaacatacg agccggaagc ataaagtgta [0920] 6481 aagcctgggg tgcctaatga gtgagctaac tcacattaat tgcgttgcgc tcactgcccg [0921] 6541 ctttccagtc gggaaacctg tcgtgccagc tgcattaatg aatcggccaa cgcgcgggga [0922] 6601 gaggcggttt gcgtattggg cgctcttccg cttcctcgct cactgactcg ctgcgctcgg [0923] 6661 tcgttcggct gcggcgagcg gtatcagctc actcaaaggc ggtaatacgg ttatccacag [0924] 6721 aatcagggga taacgcagga aagaacatgt gagcaaaagg ccagcaaaag gccaggaacc [0925] 6781 gtaaaaaggc cgcgttgctg gcgtttttcc ataggctccg cccccctgac gagcatcaca [0926] 6841 aaaatcgacg ctcaagtcag aggtggcgaa acccgacagg actataaaga taccaggcgt [0927] 6901 ttccccctgg aagctccctc gtgcgctctc ctgttccgac cctgccgctt accggatacc [0928] 6961 tgtccgcctt tctcccttcg ggaagcgtgg cgctttctca tagctcacgc tgtaggtatc [0929] 7021 tcagttcggt gtaggtcgtt cgctccaagc tgggctgtgt gcacgaaccc cccgttcagc [0930] 7081 ccgaccgctg cgccttatcc ggtaactatc gtcttgagtc caacccggta agacacgact [0931] 7141 tatcgccact ggcagcagcc actggtaaca ggattagcag agcgaggtat gtaggcggtg [0932] 7201 ctacagagtt cttgaagtgg tggcctaact acggctacac tagaaggaca gtatttggta [0933] 7261 tctgcgctct gctgaagcca gttaccttcg gaaaaagagt tggtagctct tgatccggca [0934] 7321 aacaaaccac cgctggtagc ggtggttttt ttgtttgcaa gcagcagatt acgcgcagaa [0935] 7381 aaaaaggatc tcaagaagat cctttgatct tttctacggg gtctgacgct cagtggaacg [0936] 7441 aaaactcacg ttaagggatt ttggtcatga gattatcaaa aaggatcttc acctagatcc [0937] 7501 ttttaaatta aaaatgaagt tttaaatcaa tctaaagtat atatgagtaa acttggtctg [0938] 7561 acagttacca atgcttaatc agtgaggcac ctatctcagc gatctgtcta tttcgttcat [0939] 7621 ccatagttgc ctgactcccc gtcgtgtaga taactacgat acgggagggc ttaccatctg [0940] 7681 gccccagtgc tgcaatgata ccgcgagacc cacgctcacc ggctccagat ttatcagcaa [0941] 7741 taaaccagcc agccggaagg gccgagcgca gaagtggtcc tgcaacttta tccgcctcca [0942] 7801 tccagtctat taattgttgc cgggaagcta gagtaagtag ttcgccagtt aatagtttgc [0943] 7861 gcaacgttgt tgccattgct acaggcatcg tggtgtcacg ctcgtcgttt ggtatggctt [0944] 7921 cattcagctc cggttcccaa cgatcaaggc gagttacatg atcccccatg ttgtgcaaaa [0945] 7981 aagcggttag ctccttcggt cctccgatcg ttgtcagaag taagttggcc gcagtgttat [0946] 8041 cactcatggt tatggcagca ctgcataatt ctcttactgt catgccatcc gtaagatgct [0947] 8101 tttctgtgac tggtgagtac tcaaccaagt cattctgaga atagtgtatg cggcgaccga [0948] 8161 gttgctcttg cccggcgtca atacgggata ataccgcgcc acatagcaga actttaaaag [0949] 8221 tgctcatcat tggaaaacgt tcttcggggc gaaaactctc aaggatctta ccgctgttga [0950] 8281 gatccagttc gatgtaaccc actcgtgcac ccaactgatc ttcagcatct tttactttca [0951] 8341 ccagcgtttc tgggtgagca aaaacaggaa ggcaaaatgc cgcaaaaaag ggaataaggg [0952] 8401 cgacacggaa atgttgaata ctcatactct tcctttttca atattattga agcatttatc [0953] 8461 agggttattg tctcatgagc ggatacatat ttgaatgtat ttagaaaaat aaacaaatag [0954] 8521 gggttccgcg cacatttccc cgaaaagtgc cacctgacgt c (SEQ ID NO:14) [0955] LacI-based Equalizer (NF+IFF) circuit; pDN- D2ir_CMV_immearly_mir(FF4)_LacO2_bGlob intron-MiR(FF4) [0956] source 1..8993 [0957] /organism="pDN-D2ir_CMV_immearly_mir(FF4)_LacO2_bGlob intron-MiR(FF4) target_LacI(Krab)_P2A_eGFP-MiR(FF4), complete sequence" [0958] /mol_type="other DNA" [0959] /note="other sequences; artificial sequences; vectors." [0960] regulatory 236..812 [0961] /regulatory_class="promoter" [0962] /note="CMV_immearly promoter" [0963] regulatory 929..990 [0964] /regulatory_class="other" [0965] /note="LacO2" [0966] regulatory 1182..1754 [0967] /regulatory_class="other" [0968] /note="bGlob_int" [0969] regulatory 1763..1784 [0970] /regulatory_class="other" [0971] /note="FF4" [0972] regulatory 1794..3257 [0973] /regulatory_class="other" [0974] /note="LacI_Krab" [0975] gene 3381..4100 [0976] /gene="EGFP" [0977] CDS 3381..4100 [0978] /gene="EGFP" [0979] /codon_start=1 [0980] /product="EGFP" [0981] /translation="MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTL KFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDD GNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIK VNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLL EFVTAAGITLGMDELYK" (SEQ ID NO:15) [0982] regulatory 4107..4128 [0983] /regulatory_class="other" [0984] /note="FF4" [0985] regulatory 4135..4510 [0986] /regulatory_class="other" [0987] /note="miRNA_FF4" [0988] BASE COUNT 2105 a 2321 c 2296 g 2271 t [0989] ORIGIN [0990] 1 gacggatcgg gagatctccc gatcccctat ggtgcactct cagtacaatc tgctctgatg [0991] 61 ccgcatagtt aagccagtat ctgctccctg cttgtgtgtt ggaggtcgct gagtagtgcg [0992] 121 cgagcaaaat ttaagctaca acaaggcaag gcttgaccga caattgcatg aagaatctgc [0993] 181 ttagggttag gcgttttgcg ctgcttcgcg atgtacgggc cagatatacg cgttgacatt [0994] 241 gattattgac tagttattaa tagtaatcaa ttacggggtc attagttcat agcccatata [0995] 301 tggagttccg cgttacataa cttacggtaa atggcccgcc tggctgaccg cccaacgacc [0996] 361 cccgcccatt gacgtcaata atgacgtatg ttcccatagt aacgccaata gggactttcc [0997] 421 attgacgtca atgggtggag tatttacggt aaactgccca cttggcagta catcaagtgt [0998] 481 atcatatgcc aagtacgccc cctattgacg tcaatgacgg taaatggccc gcctggcatt [0999] 541 atgcccagta catgacctta tgggactttc ctacttggca gtacatctac gtattagtca [1000] 601 tcgctattac catggtgatg cggttttggc agtacatcaa tgggcgtgga tagcggtttg [1001] 661 actcacgggg atttccaagt ctccacccca ttgacgtcaa tgggagtttg ttttggcacc [1002] 721 aaaatcaacg ggactttcca aaatgtcgta acaactccgc cccattgacg caaatgggcg [1003] 781 gtaggcgtgt acggtgggag gtctatataa gcagagctcg tttagtgaac cgtcagatcg [1004] 841 cctggagcaa ttccacaaca cttttgtctt ataccaactt tccgtaccac ttcctaccct [1005] 901 cgtaaagtcg acaccggggc ccagatctaa ttgtgagcgc tcacaattcc acaacctaga [1006] 961 attgtgagcg ctcacaattc cacaacctag ggatcccgcg ctgttttgac ctccatagaa [1007] 1021 gacaccggga ccgatccagc ctccggactc tagcgtttaa acttaagctg ggtacccggg [1008] 1081 gatcctctag ggcctctgag ctattccaga agtagtgaag aggctttttt ggaggcctag [1009] 1141 gcttttgcaa aaagctccgg atcgatcctg agaacttcag ggtgagtttg gggacccttg [1010] 1201 attgttcttt ctttttcgct attgtaaaat tcatgttata tggagggggc aaagttttca [1011] 1261 gggtgttgtt tagaatggga agatgtccct tgtatcacca tggaccctca tgataatttt [1012] 1321 gtttctttca ctttctactc tgttgacaac cattgtctcc tcttattttc ttttcatttt [1013] 1381 ctgtaacttt ttcgttaaac tttagcttgc atttgtaacg aatttttaaa ttcacttttg [1014] 1441 tttatttgtc agattgtaag tactttctct aatcactttt ttttcaaggc aatcagggta [1015] 1501 tattatattg tacttcagca cagttttaga gaacaattgt tataattaaa tgataaggta [1016] 1561 gaatatttct gcatataaat tctggctggc gtggaaatat tcttattggt agaaacaact [1017] 1621 acatcctggt catcatcctg cctttctctt tatggttaca atgatataca ctgtttgaga [1018] 1681 tgaggataaa atactctgag tccaaaccgg gcccctctgc taaccatgtt catgccttct [1019] 1741 tctttttcct acaggtcctg caccgcttga agtctttaat taaaggcgcc accatgaaac [1020] 1801 cagtaacgtt atacgatgtc gcagagtatg ccggtgtctc ttatcagacc gtttcccgcg [1021] 1861 tggtgaacca ggccagccac gtttctgcga aaacgcggga aaaagtggaa gcggcgatgg [1022] 1921 cggagctgaa ttacattccc aaccgcgtgg cacaacaact ggcgggcaaa cagtcgttgc [1023] 1981 tgattggcgt tgccacctcc agtctggccc tgcacgcgcc gtcgcaaatt gtcgcggcga [1024] 2041 ttaaatctcg cgccgatcaa ctgggtgcca gcgtggtggt gtcgatggta gaacgaagcg [1025] 2101 gcgtcgaagc ctgtaaagcg gcggtgcaca atcttctcgc gcaacgcgtc agtgggctga [1026] 2161 tcattaacta tccgctggat gaccaggatg ccattgctgt ggaagctgcc tgcactaatg [1027] 2221 ttccggcgtt atttcttgat gtctctgacc agacacccat caacagtatt attttctccc [1028] 2281 atgaagacgg tacgcgactg ggcgtggagc atctggtcgc attgggtcac cagcaaatcg [1029] 2341 cgctgttagc gggcccatta agttctgtct cggcgcgtct gcgtctggct ggctggcata [1030] 2401 aatatctcac tcgcaatcaa attcagccga tagcggaacg ggaaggcgac tggagtgcca [1031] 2461 tgtccggttt tcaacaaacc atgcaaatgc tgaatgaggg catcgttccc actgcgatgc [1032] 2521 tggttgccaa cgatcagatg gcgctgggcg caatgcgcgc cattaccgag tccgggctgc [1033] 2581 gcgttggtgc ggatatctcg gtagtgggat acgacgatac cgaagacagc tcatgttata [1034] 2641 tcccgccgtt aaccaccatc aaacaggatt ttcgcctgct ggggcaaacc agcgtggacc [1035] 2701 gcttgctgca actctctcag ggccaggcgg tgaagggcaa tcagctgttg cccgtctcac [1036] 2761 tggtgaaaag aaaaaccacc ctggcgccca atacgcaaac cgcctctccc cgcgcgttgg [1037] 2821 ccgattcatt aatgcagctg gcacgacagg tttcccgact ggaaagcggg cagccaaaaa [1038] 2881 agaagagaaa ggtcgacggc ggtggtgctt tgtctcctca gcactctgct gtcactcaag [1039] 2941 gaagtatcat caagaacaag gagggcatgg atgctaagtc actaactgcc tggtcccgga [1040] 3001 cactggtgac cttcaaggat gtatttgtgg acttcaccag ggaggagtgg aagctgctgg [1041] 3061 acactgctca gcagatcgtg tacagaaatg tgatgctgga gaactataag aacctggttt [1042] 3121 ccttgggtta tcagcttact aagccagatg tgatcctccg gttggagaag ggagaagagc [1043] 3181 cctggctggt ggagagagaa attcaccaag agacccatcc tgattcagag actgcatttg [1044] 3241 aaatcaaatc atcagttggc gggccaaaaa agaagagaaa gggtgacggt gctggtttaa [1045] 3301 ttaacatggg aagcggagct actaacttca gcctgctgaa gcaggctgga gacgtggagg [1046] 3361 agaaccctgg acctgctagc atggtgagca agggcgagga gctgttcacc ggggtggtgc [1047] 3421 ccatcctggt cgagctggac ggcgacgtaa acggccacaa gttcagcgtg tccggcgagg [1048] 3481 gcgagggcga tgccacctac ggcaagctga ccctgaagtt catctgcacc accggcaagc [1049] 3541 tgcccgtgcc ctggcccacc ctcgtgacca ccctgaccta cggcgtgcag tgcttcagcc [1050] 3601 gctaccccga ccacatgaag cagcacgact tcttcaagtc cgccatgccc gaaggctacg [1051] 3661 tccaggagcg caccatcttc ttcaaggacg acggcaacta caagacccgc gccgaggtga [1052] 3721 agttcgaggg cgacaccctg gtgaaccgca tcgagctgaa gggcatcgac ttcaaggagg [1053] 3781 acggcaacat cctggggcac aagctggagt acaactacaa cagccacaac gtctatatca [1054] 3841 tggccgacaa gcagaagaac ggcatcaagg tgaacttcaa gatccgccac aacatcgagg [1055] 3901 acggcagcgt gcagctcgcc gaccactacc agcagaacac ccccatcggc gacggccccg [1056] 3961 tgctgctgcc cgacaaccac tacctgagca cccagtccgc cctgagcaaa gaccccaacg [1057] 4021 agaagcgcga tcacatggtc ctgctggagt tcgtgaccgc cgccgggatc actctcggca [1058] 4081 tggacgagct gtacaagtag aagcttccgc ttgaagtctt taattaaaga gcttgtgagt [1059] 4141 atgtgctcgc ttcggcagca catatactat gtcgaatgag gcttcagtac tttacagaat [1060] 4201 cgttgcctgc acatcttgga aacacttgct gggattactt cttcaggtta acccaacaga [1061] 4261 aggctcgagt gctgttgaca gtgagcgccg cttgaagtct ttaattaaat agtgaagcca [1062] 4321 cagatgtatt taattaaaga cttcaagcgg tgcctactgc ctcggagaat tcaaggggct [1063] 4381 actttaggag caattatctt gtttactaaa actgaatacc ttgctatctc tttgatacat [1064] 4441 ttttacaaag ctgaattaaa atggtataaa ttaaatcact tttttcaatt gtttcctttt [1065] 4501 ttttcctcag gcggccgcaa tcaacctctg gattacaaaa tttgtgaaag attgactggt [1066] 4561 attcttaact atgttgctcc ttttacgcta tgtggatacg ctgctttaat gcctttgtat [1067] 4621 catgctattg cttcccgtat ggctttcatt ttctcctcct tgtataaatc ctggttgctg [1068] 4681 tctctttatg aggagttgtg gcccgttgtc aggcaacgtg gcgtggtgtg cactgtgttt [1069] 4741 gctgacgcaa cccccactgg ttggggcatt gccaccacct gtcagctcct ttccgggact [1070] 4801 ttcgctttcc ccctccctat tgccacggcg gaactcatcg ccgcctgcct tgcccgctgc [1071] 4861 tggacagggg ctcggctgtt gggcactgac aattccgtgg tgttgtcctc gagtctagag [1072] 4921 ggcccgttta aacccgctga tcagcctcga ctgtgccttc tagttgccag ccatctgttg [1073] 4981 tttgcccctc ccccgtgcct tccttgaccc tggaaggtgc cactcccact gtcctttcct [1074] 5041 aataaaatga ggaaattgca tcgcattgtc tgagtaggtg tcattctatt ctggggggtg [1075] 5101 gggtggggca ggacagcaag ggggaggatt gggaagacaa tagcaggcat gctggggatg [1076] 5161 cggtgggctc tatggcttct gaggcggaaa gaaccagctg gggctctagg gggtatcccc [1077] 5221 acgcgccctg tagcggcgca ttaagcgcgg cgggtgtggt ggttacgcgc agcgtgaccg [1078] 5281 ctacacttgc cagcgcccta gcgcccgctc ctttcgcttt cttcccttcc tttctcgcca [1079] 5341 cgttcgccgg ctttccccgt caagctctaa atcgggggct ccctttaggg ttccgattta [1080] 5401 gtgctttacg gcacctcgac cccaaaaaac ttgattaggg tgatggttca cgtacctaga [1081] 5461 agttcctatt ccgaagttcc tattctctag aaagtatagg aacttccttg gccaaaaagc [1082] 5521 ctgaactcac cgcgacgtct gtcgagaagt ttctgatcga aaagttcgac agcgtctccg [1083] 5581 acctgatgca gctctcggag ggcgaagaat ctcgtgcttt cagcttcgat gtaggagggc [1084] 5641 gtggatatgt cctgcgggta aatagctgcg ccgatggttt ctacaaagat cgttatgttt [1085] 5701 atcggcactt tgcatcggcc gcgctcccga ttccggaagt gcttgacatt ggggaattca [1086] 5761 gcgagagcct gacctattgc atctcccgcc gtgcacaggg tgtcacgttg caagacctgc [1087] 5821 ctgaaaccga actgcccgct gttctgcagc cggtcgcgga ggccatggat gcgatcgctg [1088] 5881 cggccgatct tagccagacg agcgggttcg gcccattcgg accgcaagga atcggtcaat [1089] 5941 acactacatg gcgtgatttc atatgcgcga ttgctgatcc ccatgtgtat cactggcaaa [1090] 6001 ctgtgatgga cgacaccgtc agtgcgtccg tcgcgcaggc tctcgatgag ctgatgcttt [1091] 6061 gggccgagga ctgccccgaa gtccggcacc tcgtgcacgc ggatttcggc tccaacaatg [1092] 6121 tcctgacgga caatggccgc ataacagcgg tcattgactg gagcgaggcg atgttcgggg [1093] 6181 attcccaata cgaggtcgcc aacatcttct tctggaggcc gtggttggct tgtatggagc [1094] 6241 agcagacgcg ctacttcgag cggaggcatc cggagcttgc aggatcgccg cggctccggg [1095] 6301 cgtatatgct ccgcattggt cttgaccaac tctatcagag cttggttgac ggcaatttcg [1096] 6361 atgatgcagc ttgggcgcag ggtcgatgcg acgcaatcgt ccgatccgga gccgggactg [1097] 6421 tcgggcgtac acaaatcgcc cgcagaagcg cggccgtctg gaccgatggc tgtgtagaag [1098] 6481 tactcgccga tagtggaaac cgacgcccca gcactcgtcc gagggcaaag gaatagcacg [1099] 6541 tactacgaga tttcgattcc accgccgcct tctatgaaag gttgggcttc ggaatcgttt [1100] 6601 tccgggacgc cggctggatg atcctccagc gcggggatct catgctggag ttcttcgccc [1101] 6661 accccaactt gtttattgca gcttataatg gttacaaata aagcaatagc atcacaaatt [1102] 6721 tcacaaataa agcatttttt tcactgcatt ctagttgtgg tttgtccaaa ctcatcaatg [1103] 6781 tatcttatca tgtctgtata ccgtcgacct ctagctagag cttggcgtaa tcatggtcat [1104] 6841 agctgtttcc tgtgtgaaat tgttatccgc tcacaattcc acacaacata cgagccggaa [1105] 6901 gcataaagtg taaagcctgg ggtgcctaat gagtgagcta actcacatta attgcgttgc [1106] 6961 gctcactgcc cgctttccag tcgggaaacc tgtcgtgcca gctgcattaa tgaatcggcc [1107] 7021 aacgcgcggg gagaggcggt ttgcgtattg ggcgctcttc cgcttcctcg ctcactgact [1108] 7081 cgctgcgctc ggtcgttcgg ctgcggcgag cggtatcagc tcactcaaag gcggtaatac [1109] 7141 ggttatccac agaatcaggg gataacgcag gaaagaacat gtgagcaaaa ggccagcaaa [1110] 7201 aggccaggaa ccgtaaaaag gccgcgttgc tggcgttttt ccataggctc cgcccccctg [1111] 7261 acgagcatca caaaaatcga cgctcaagtc agaggtggcg aaacccgaca ggactataaa [1112] 7321 gataccaggc gtttccccct ggaagctccc tcgtgcgctc tcctgttccg accctgccgc [1113] 7381 ttaccggata cctgtccgcc tttctccctt cgggaagcgt ggcgctttct catagctcac [1114] 7441 gctgtaggta tctcagttcg gtgtaggtcg ttcgctccaa gctgggctgt gtgcacgaac [1115] 7501 cccccgttca gcccgaccgc tgcgccttat ccggtaacta tcgtcttgag tccaacccgg [1116] 7561 taagacacga cttatcgcca ctggcagcag ccactggtaa caggattagc agagcgaggt [1117] 7621 atgtaggcgg tgctacagag ttcttgaagt ggtggcctaa ctacggctac actagaagga [1118] 7681 cagtatttgg tatctgcgct ctgctgaagc cagttacctt cggaaaaaga gttggtagct [1119] 7741 cttgatccgg caaacaaacc accgctggta gcggtggttt ttttgtttgc aagcagcaga [1120] 7801 ttacgcgcag aaaaaaagga tctcaagaag atcctttgat cttttctacg gggtctgacg [1121] 7861 ctcagtggaa cgaaaactca cgttaaggga ttttggtcat gagattatca aaaaggatct [1122] 7921 tcacctagat ccttttaaat taaaaatgaa gttttaaatc aatctaaagt atatatgagt [1123] 7981 aaacttggtc tgacagttac caatgcttaa tcagtgaggc acctatctca gcgatctgtc [1124] 8041 tatttcgttc atccatagtt gcctgactcc ccgtcgtgta gataactacg atacgggagg [1125] 8101 gcttaccatc tggccccagt gctgcaatga taccgcgaga cccacgctca ccggctccag [1126] 8161 atttatcagc aataaaccag ccagccggaa gggccgagcg cagaagtggt cctgcaactt [1127] 8221 tatccgcctc catccagtct attaattgtt gccgggaagc tagagtaagt agttcgccag [1128] 8281 ttaatagttt gcgcaacgtt gttgccattg ctacaggcat cgtggtgtca cgctcgtcgt [1129] 8341 ttggtatggc ttcattcagc tccggttccc aacgatcaag gcgagttaca tgatccccca [1130] 8401 tgttgtgcaa aaaagcggtt agctccttcg gtcctccgat cgttgtcaga agtaagttgg [1131] 8461 ccgcagtgtt atcactcatg gttatggcag cactgcataa ttctcttact gtcatgccat [1132] 8521 ccgtaagatg cttttctgtg actggtgagt actcaaccaa gtcattctga gaatagtgta [1133] 8581 tgcggcgacc gagttgctct tgcccggcgt caatacggga taataccgcg ccacatagca [1134] 8641 gaactttaaa agtgctcatc attggaaaac gttcttcggg gcgaaaactc tcaaggatct [1135] 8701 taccgctgtt gagatccagt tcgatgtaac ccactcgtgc acccaactga tcttcagcat [1136] 8761 cttttacttt caccagcgtt tctgggtgag caaaaacagg aaggcaaaat gccgcaaaaa [1137] 8821 agggaataag ggcgacacgg aaatgttgaa tactcatact cttccttttt caatattatt [1138] 8881 gaagcattta tcagggttat tgtctcatga gcggatacat atttgaatgt atttagaaaa [1139] 8941 ataaacaaat aggggttccg cgcacatttc cccgaaaagt gccacctgac gtc (SEQ ID NO:16) REFERENCES [1140] 1. Rice, A. M. & McLysaght, A. Dosage sensitivity is a major determinant of human copy number variant pathogenicity. Nat Commun 8, 14366 (2017). [1141] 2. Kfir, S. et al. Pathway- and expression level-dependent effects of oncogenic n- ras: p27(kip1) mislocalization by the ral-GEF pathway and erk-mediated interference with smad signaling. Mol Cell Biol 25, 8239–8250 (2005). [1142] 3. Liu, Y. & Lehmann, M. A genomic response to the yeast transcription factor GAL4 in drosophila. Fly 2, 92–98 (2008). [1143] 4. Wang, X. & Li, S. Protein mislocalization: mechanisms, functions and clinical applications in cancer. Biochim Biophys Acta 1846, 13–25 (2014). [1144] 5. Moriya, H. Quantitative nature of overexpression experiments. Molecular Biology of the Cell 26, 3932–3939 (2015). [1145] 6. Kintaka, R., Makanae, K. & Moriya, H. Cellular growth defects triggered by an overload of protein localization processes. Sci Rep 6, 31774 (2016). [1146] 7. Wietek, J. et al. Anion-conducting channelrhodopsins with tuned spectra and modified kinetics engineered for optogenetic manipulation of behavior. Scientific Reports 7, 14957 (2017). [1147] 8. Cardinale, S. & Arkin, A. P. Contextualizing context for synthetic biology– identifying causes of failure of synthetic biological systems. Biotechnol J 7, 856–866 (2012). [1148] 9. Randall, A., Guye, P., Gupta, S., Duportet, X. & Weiss, R. Design and connection of robust genetic circuits.Meth Enzymol 497, 159–186 (2011). [1149] 10. Tseng, W.-C., Haselton, F. R. & Giorgio, T. D. Transfection by cationic liposomes using simultaneous single cell 733 measurements of plasmid delivery and transgene expression. Journal of Biological Chemistry 272, 25641– 73425647 (1997). [1150] 11. Segall-Shapiro, T. H., Sontag, E. D. & Voigt, C. A. Engineered promoters enable constant gene expression at any copy number in bacteria. Nature Biotechnology 36, 352 (2018). [1151] 12. Buzi, G. & Khammash, M. Implementation considerations, not topological differences, are the main determi- 738 nants of noise suppression properties in feedback and incoherent feedforward circuits. PLOS Computational 739 Biology 12, 1–16 (2016). [1152] 13. Osella, M., Bosia, C., Cora´, D. & Caselle, M. The role of incoherent microRNA-mediated feedforward loops in noise buffering. PLoS Computational Biology 7, e1001101 (2011). [1153] 14. Bleris, L. et al. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Molecular Systems Biology 7, 519 (2011). [1154] 15. Lillacci, G., Benenson, Y. & Khammash, M. Synthetic control systems for high performance gene expression in mammalian cells. Nucleic acids research 46, 9855–9863 (2018). [1155] 16. Jones, R. D. et al. An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nat Commun 11, 5690 (2020). [1156] 17. Acar, M., Pando, B. F., Arnold, F. H., Elowitz, M. B. & van Oudenaarden, A. A general mechanism for network-dosage compensation in gene circuits. Science 329, 1656– 1660 (2010). [1157] 18. Peng, W., Song, R. & Acar, M. Noise reduction facilitated by dosage compensation in gene networks. Nat. Commun.7, 12959 (2016). [1158] 19. Song, R., Liu, P. & Acar, M. Network-dosage compensation topologies as recurrent network motifs in natural gene networks. BMC Syst. Biol.8, 69 (2014). [1159] 20. Skinner, S. O. et al. Single-cell analysis of transcription kinetics across the cell cycle. elife 5, e12175 (2016). [1160] 21. Rinaudo, K. et al. A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol.25, 795–801 (2007). [1161] 22. Leisner, M., Bleris, L., Lohmueller, J., Xie, Z. & Benenson, Y. Rationally designed logic integration of regulatory signals in mammalian cells. Nature Nanotechnology 5, 666–670 (2010). [1162] 23. Schreiber, J., Arter, M., Lapique, N., Haefliger, B. & Benenson, Y. Model- guided combinatorial optimization of complex synthetic gene networks. Molecular systems biology 12, 899 (2016). [1163] 24. Lam, J. K. W., Chow, M. Y. T., Zhang, Y. & Leung, S. W. S. sirna versus mirna as therapeutics for gene silencing. Molecular therapy. Nucleic acids 4, e252 (2015). [1164] 25. Klironomos, F. D. & Berg, J. Quantitative analysis of competition in posttranscriptional regulation reveals a novel signature in target expression variation. Biophys J 104, 951–958 (2013). [1165] 26. Ryan, M. D. & Drew, J. Foot-and-mouth disease virus 2A oligopeptide mediated cleavage of an artificial polyprotein. EMBO J 13, 928–933 (1994). [1166] 27. Kim, J. H. et al. High cleavage efficiency of a 2A peptide derived from porcine teschovirus-1 in human cell lines, zebrafish and mice. PLoS One 6, e18556 (2011). [1167] 28. Nevozhay, D., Adams, R. M., Murphy, K. F., Josic´, K. & Bala´zsi, G. Negative autoregulation linearizes the 770 dose–response and suppresses the heterogeneity of gene expression. Proceedings of the National Academy 771 of Sciences 106, 5123–5128 (2009). [1168] 29. Nevozhay, D., Zal, T. & Bala´zsi, G. Transferring a synthetic gene circuit from yeast to mammalian cells. Nature Communications 4, 1451 (2013). [1169] 30. Farquhar, K. S. et al. Role of network-mediated stochasticity in mammalian drug resistance. Nature Communications 10, 2766 (2019). [1170] 31. Weber, W. & Fussenegger, M. Artificial mammalian gene regulation networks-novel approaches for gene therapy and bioengineering. Journal of Biotechnology 98, 161–187 (2002). [1171] 32. Siciliano, V. et al. Construction and modelling of an inducible positive feedback loop stably integrated in a mammalian cell-line. PLoS Computational Biology 7, e1002074 (2011). [1172] 33. Costello, A. et al. Leaky expression of the TET-on system hinders control of endogenous miRNA abundance. Biotechnology Journal 14, e1800219 (2019). [1173] 34. Leisner, M., Bleris, L., Lohmueller, J., Xie, Z. & Benenson, Y. Rationally designed logic integration of regulatory signals in mammalian cells. Nature Nanotechnology 5, 666–670 (2010). [1174] 35. Cohen, R. N., van der Aa, M. A. E. M., Macaraeg, N., Lee, A. P. & Szoka, F. C. Quantification of plasmid DNA 785 copies in the nucleus after lipoplex and polyplex transfection. Journal of Controlled Release 135, 166–174786 (2009). [1175] 36. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002). [1176] 37. Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005). [1177] 38. Bindels, D. S. et al. mScarlet: a bright monomeric red fluorescent protein for cellular imaging. Nat Methods 14, 53–56 (2017). [1178] 39. Griesbeck, O., Baird, G. S., Campbell, R. E., Zacharias, D. A. & Tsien, R. Y. Reducing the environmental sensitivity of yellow fluorescent protein. mechanism and applications. J Biol Chem 276, 29188–29194 (2001). [1179] 40. Balleza, E., Kim, J. M. & Cluzel, P. Systematic characterization of maturation time of fluorescent proteins in living cells. Nat Methods 15, 47–51 (2018). [1180] 41. Cranfill, P. J. et al. Quantitative assessment of fluorescent proteins. Nat Methods 13, 557–562 (2016). [1181] 42. Liu, B. et al. Influence of fluorescent protein maturation on FRET measurements in living cells. ACS Sens 3,1735–1742 (2018). [1182] 43. Lin, C.-Y. et al. Enhancing protein expression in HEK-293 cells by lowering culture temperature. PLoS ONE 10, e0123562 (2015). [1183] 44. Van Craenenbroeck, K., Vanhoenacker, P. & Haegeman, G. Episomal vectors for gene expression in mammalian cells. Eur J Biochem 267, 5665–5678 (2000). [1184] 45. Yates, J. L., Warren, N. & Sugden, B. Stable replication of plasmids derived from epstein-barr virus in various mammalian cells. Nature 313, 812–815 (1985). [1185] 46. Jalanko, A., Kallio, A., Ruohonen-Lehto, M., So¨ derlund, H. & Ulmanen, I. An EBV-based mammalian cell 807 expression vector for efficient expression of cloned coding sequences. Biochim Biophys Acta 949, 206–212808 (1988). [1186] 47. Yates, J. L., Camiolo, S. M. & Bashaw, J. M. The minimal replicator of Epstein-Barr virus oriP. J. Virol.74, 4512–4522 (2000). [1187] 48. Kanda, T., Otter, M. & Wahl, G. M. Coupling of mitotic chromosome tethering and replication competence in Epstein-Barr virus-based plasmids. Mol Cell Biol 21, 3576–3588 (2001). [1188] 49. Hodin, T. L., Najrana, T. & Yates, J. L. Efficient replication of Epstein-Barr virus-derived plasmids requires tethering by EBNA1 to host chromosomes. J Virol 87, 13020– 13028 (2013). [1189] 50. He, J., Yang, Q. & Chang, L.-J. Dynamic DNA methylation and histone modifications contribute to lentiviral transgene silencing in murine embryonic carcinoma cells. J Virol 79, 13497–13508 (2005). [1190] 51. Van Craenenbroeck, K., Vanhoenacker, P., Duchau, H. & Haegeman, G. Molecular integrity and usefulness of episomal expression vectors derived from BK and epstein– barr virus. Gene 253, 293–301 (2000). [1191] 52. Kiyosue, K. & Miwa, Y. Epstein-Barr virus-derived vector suitable for long- term expression in neurons. Heliyon 6, e03504 (2020). [1192] 53. Mu˜cke, S. et al. Suitability of Epstein-Barr virus-based episomal vectors for expression of cytokine genes in human lymphoma cells. Gene Ther.4, 82–92 (1997). [1193] 54. Mei, W.-H., Qian, G.-X., Zhang, X.-Q., Zhang, P. & Lu, J. Sustained expression of Epstein-Barr virus episomal vector mediated factor VIII in vivo following muscle electroporation. Haemophilia 12, 271–279 (2006). [1194] 55. Thyagarajan, B. et al. A single EBV-based vector for stable episomal maintenance and expression of GFP in human embryonic stem cells. Regen. Med.4, 239–250 (2009). [1195] 56. Paillard, F. Promoter attenuation in gene therapy: causes and remedies. Hum Gene Ther 8, 2009–2010 (1997). [1196] 57. Moritz, B., Becker, P. B. & Go¨ pfert, U. CMV promoter mutants with a reduced propensity to productivity loss in CHO cells. Sci Rep 5, 16952 (2015). [1197] 58. Ray, J. C. J. & Igoshin, O. A. Interplay of gene expression noise and ultrasensitive dynamics affects bacterial operon organization. PLoS computational biology 8, e1002672 (2012). [1198] 59. Quarton, T. et al. Uncoupling gene expression noise along the central dogma using genome engineered human cell lines. Nucleic Acids Res 48, 9406–9413 (2020). [1199] 60. Reus, J. B., Trivino-Soto, G. S., Wu, L. I., Kokott, K. & Lim, E. S. SV40 large t antigen is not responsible for the loss of STING in 293T cells but can inhibit cGAS- STING interferon induction. Viruses 12 (2020). [1200] 61. Mizuguchi, H., Xu, Z., Ishii-Watabe, A., Uchida, E. & Hayakawa, T. IRES- dependent second gene expression 837 is significantly lower than cap-dependent first gene expression in a bicistronic vector. Mol Ther 1, 376–382838 (2000). [1201] 62. Frei, T. et al. Characterization and mitigation of gene expression burden in mammalian cells. Nat Commun 11, 4641 (2020). [1202] 63. Man-Sai, A., Francisco, S.-C. & Mora-Rodriguez, R. A biocomputational platform for the automated construction 842 of large-scale mathematical models of mirna- transcription factor networks for studies on gene dosage compen- 843 sation. In 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), 1–7 (IEEE, 2016). [1203] 64. Shao, Q. et al. Coupling of dna replication and negative feedback controls gene expression for cell-fate decisions. iScience 6, 1–12 (2018). [1204] 65. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nature Methods (2019). [1205] 66. Ghasemi, A. & Zahediasl, S. Normality tests for statistical analysis: a guide for non-statisticians. International journal of endocrinology and metabolism 10, 486–489 (2012). [1206] 67. Sanft, K. R. et al. Stochkit2: software for discrete stochastic simulation of biochemical systems with events. Bioinformatics 27, 2457–2458 (2011). [1207] Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the design as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the present disclosure, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present disclosure. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Claims

CLAIMS What is claimed is: 1. A gene expression system comprising a negative feedback loop and an incoherent feedforward control for expression of at least one gene of interest, wherein components of the negative feedback loop and incoherent feedforward control are optionally regulated by the same regulatory sequence.
2. The gene expression system of claim 1, wherein the negative feedback loop is a transcriptional negative feedback loop and/or wherein the incoherent feedforward control is a post- transcriptional incoherent feedforward control.
3. The gene expression system of claim 1 or 2, wherein the negative feedback loop component lacks regulation by an miRNA.
4. The gene expression system of claim 1, 2 or 3, wherein the negative feedback loop comprises a repressor that represses expression of its own gene.
5. The gene expression system of any one of claims 1-4, wherein the system lacks an incoherent feedforward loop component.
6. The gene expression system of any one of claims 1-5, wherein the system is further defined as comprising an expression construct comprising at least two components to regulate production of a gene product from the gene of interest, said components comprising: (a) a sequence encoding a repressor that represses expression of itself and the gene of interest, wherein the expression of the repressor sequence is regulated by a cognate operator site to which the repressor may bind, wherein the sequence encoding the repressor and the gene of interest are expressed from different promoters or are on a multicistronic vector or are separated by a ribosome-skipping sequence or an internal ribosome entry site, (b) one or more sequences encoding a miRNA located anywhere in the overall sequence of the system, optionally including a 5’ untranslated region, a 3’ untranslated region, or within any of the gene of the system; and (c) sequences including one or more miRNA target sites in the 5’ untranslated region, the 3’ untranslated region, or both.
7. The system of any one of claims 1-6, wherein the regulatory sequence that regulates expression of the transcriptional negative feedback loop and post-transcriptional incoherent feedforward control comprises a constitutive promoter.
8. The system of any one of claims 1-6, wherein the regulatory sequence that regulates expression of the transcriptional negative feedback loop and post-transcriptional incoherent feedforward control comprises an inducible promoter.
9. The system of any one of claims 1-6, wherein the regulatory sequence that regulates expression of the transcriptional negative feedback loop and post-transcriptional incoherent feedforward control comprises a tissue-specific promoter.
10. The system of any one of claims 1-9, wherein the repressor is a tetracycline repressor, a Lac repressor that binds to one or more lacO operator sites, a dCas9, TALEN, or Zinc finger.
11. The system of claim 10, wherein when the TetR repressor is used, the cognate operator site to which the repressor may bind is one or more copies of tetO2.
12. The system of any one of claims 1-11, wherein the sequence encoding the miRNA is flanked by splicing sites.
13. The system of any one of claims 1-12, wherein the miRNA is miR-FF4, miR-FF3, miR- FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
14. The system of any one of claims 1-13, wherein the expression construct comprises two non-identical genes of interest, wherein a first gene of interest and a second gene of interest are separated by a ribosome-skipping sequence or an internal ribosome entry site, and wherein said first gene of interest, ribosome-skipping sequence or an internal ribosome entry site, and second gene of interest are downstream from the sequence encoding the repressor and separated from the sequence encoding the repressor by a ribosome-skipping sequence or an internal ribosome entry site.
15. The system of any one of claims 1-14, wherein the (a) sequence encoding the miRNA is downstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites.
16. The system of any one of claims 1-14, wherein the (a) sequence encoding the miRNA is upstream from (b) the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, and the gene of interest flanked by the miRNA target sites or the miRNA target sites are elsewhere in the expression construct.
17. The system of any one of claims 1-14, wherein the (a) sequence encoding the miRNA is within the sequence encoding the repressor, the ribosome-skipping sequence/ internal ribosome entry site, or the gene of interest flanked by the miRNA target sites.
18. The system of any one of claim 1-17, wherein the gene of interest is a reporter protein, a gene-editing reagent, a therapeutic protein, an enzyme, an optogenetic reagent, a chemogenetic reagent, or a combination thereof.
19. The system of claim 18, wherein the reporter protein is a fluorescent protein, a fluorescent indicator, a bioluminescent protein, or a bioluminescent indicator.
20. The system of claim 19, wherein the fluorescent protein is a blue, cyan, green, yellow, red, far-red, or infrared fluorescent protein.
21. The system of claim 18, wherein the gene-editing reagent is a CRISPR-Cas9 component.
22. The system of any one of claims 1-21, wherein the ribosome-skipping sequence is a 2A self-cleaving peptide.
23. The system of claim 22, wherein the 2A self-cleaving peptide is T2A, P2A, E2A, F2A, or a combination thereof.
24. The system of any one of claims 1-23, wherein the expression construct is on a plasmid or is on a chromosome.
25. The system of claim 24, wherein the plasmid is present in a cell.
26 The system of any one of claims 1-23 wherein the expression construct is on an episome
27. The system of claim 26, wherein the episome is present in a cell.
28. A polynucleotide comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: (a) one or more operator sites that regulate expression of promoter expression; (b) one or more miRNA target sites; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) one or more genes of interest; (f) one or more miRNA target sites; and (g) one or more miRNA-encoding sequences.
29. The polynucleotide of claim 28, wherein the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction another ribosome-skipping sequence or an internal ribosome entry site and another gene of interest different from the gene in (e).
30. The polynucleotide of claim 28 or 29, wherein the repressor is a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger.
31. The polynucleotide of any one of claims 28-30, wherein the sequence encoding the miRNA is flanked by splicing sites.
32. The polynucleotide of any one of claims 28-31, wherein the miRNA is miR-FF4, miR-FF3, miR-FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
33. The polynucleotide of any one of claims 28-32, wherein the operator site is tetO2 or lacO2.
34. The polynucleotide of any one of claims 28-33, wherein the expression construct comprises a regulatory sequence that regulates expression of the repressor, the gene of interest, and the miRNA.
35. The polynucleotide of any one of claims 28-33, wherein the repressor and miRNA are expressed from different regulatory sequences than the gene of interest.
36. The polynucleotide of any one of claims 28-35, wherein the ribosome-skipping sequence is a 2A self-cleaving peptide.
37. The polynucleotide of claim 36, wherein the 2A self-cleaving peptide is T2A, P2A, E2A, F2A, or a combination thereof.
38. The polynucleotide of any one of claims 28-37, wherein the polynucleotide is further defined as a vector.
39. The polynucleotide of claim 38, wherein the vector is a viral vector or not a viral vector.
40. The polynucleotide of claim 38 or 39, wherein the vector is a plasmid or an episome.
41. An episome, comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: (a) one or more operator sites that regulates expression of a repressor; (b) one or more miRNA target sites; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) one or more genes of interest; (f) one or more miRNA target sites; and (g) one or more miRNA-encoding sequences.
42. The episome of claim 41, wherein the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction another ribosome-skipping sequence or an internal ribosome entry site and another gene of interest different from the gene in (e).
43. The episome of claim 41 or 42, wherein the repressor is a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger.
44. The episome of any one of claims 41-43, wherein the sequence encoding the miRNA is flanked by splicing sites.
45. The episome of any one of claims 41-44, wherein the miRNA is miR-FF4, miR-FF3, miR- FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
46. The episome of any one of claims 41-45, wherein the operator site is tetO2 or lacO2.
47. The episome of any one of claims 41-46, wherein the expression construct comprises a regulatory sequence that regulates expression of the repressor, the gene of interest, and the miRNA.
48. The episome of any one of claims 41-47, wherein the ribosome-skipping sequence is a 2A self-cleaving peptide.
49. The episome of claim 48, wherein the 2A self-cleaving peptide is T2A, P2A, E2A, F2A, or a combination thereof.
50. The episome of any one of claims 41-49, wherein the polynucleotide is further defined as a vector.
51. The episome of claim 50, wherein the vector is a viral vector or not a viral vector.
52. The episome of claim 50 or 51, wherein the vector is a plasmid or an episome.
53. A plasmid, comprising an expression construct, wherein the expression construct comprises in a 5’ to 3’ direction: (a) one or more operator sites that regulates expression of a repressor; (b) one or more miRNA target sites; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) one or more genes of interest; (f) one or more miRNA target sites; and (g) one or more miRNA-encoding sequences.
54. The plasmid of claim 53, wherein the expression construct between (e) and (f) further comprises in a 5’ to 3’ direction another ribosome-skipping sequence or an internal ribosome entry site and another gene of interest different from the gene in (e).
55. The plasmid of claim 53 or 54, wherein the repressor is a tetracycline repressor, a Lac repressor that binds to a lacO operator site, a dCas9, TALEN, or Zinc finger.
56. The plasmid of any one of claims 53-55, wherein the sequence encoding the miRNA is flanked by splicing sites.
57. The plasmid of any one of claims 53-56, wherein the miRNA is miR-FF4, miR-FF3, miR- FF5, miR-FF6, miR-17, miR-21, miR-30a, miR-141, or miR-146a.
58. The plasmid of any one of claims 53-57, wherein the operator site is tetO2 or lacO.
59. The plasmid of any one of claims 53-58, wherein the expression construct comprises a regulatory sequence that regulates expression of the repressor, the gene of interest, and the miRNA.
60. The plasmid of any one of claims 53-59, wherein the ribosome-skipping sequence is a 2A self-cleaving peptide.
61. The plasmid of claim 60, wherein the 2A self-cleaving peptide is T2A, P2A, E2A, F2A, or a combination thereof.
62. A cell, comprising the system of any one of claims 1-27, the polynucleotide of any one of claims 28-40, the episome of any one of claims 41-52, or the plasmid of any one of claims 53-61.
63. The cell of claim 62, wherein the cell is a mammalian cell or a yeast cell.
64. The cell of any one of claims 62-63, wherein said cell is comprised in a cryopreservation medium.
65. A plurality of cells of any one of claims 62-64.
66. A composition, comprising the plurality of claim 65.
67. A method of regulating expression of at least one gene of interest, comprising the steps of: (1) providing a system, optionally in a cell or cell-free medium, wherein said system comprises a polynucleotide comprising an expression construct that comprises in a 5’ to 3’ direction: (a) one or more operator sites that regulates expression of a repressor; (b) one or more miRNA target site; (c) sequence encoding a repressor; (d) a ribosome-skipping sequence or an internal ribosome entry site; (e) one or more genes of interest; (f) one or more miRNA target sites; and (g) one or more miRNA-encoding sequences, wherein said miRNA-encoding sequence is flanked by splicing sites; (2) when desired, exposing the polynucleotides to an effective amount of a compound that induces expression from the expression construct by inhibiting the repressor, said expression from the expression construct producing a transcript that comprises sequence encoding the repressor, sequence encoding a gene product from the gene of interest, and the miRNA-encoding sequence, wherein the miRNA recognizes the miRNA target sites on the transcript, thereby resulting in inhibition of production of the gene product.
68. The method of claim 67, wherein the compound that induces expression is doxycycline or a functionally similar compound.
69. The method of claim 68, wherein an effective amount of doxycycline is about 0-1000 ng/mL.
70. The method of claim 68 or 69, wherein the functionally similar compound is tetracycline or minocycline.
71. The method of any one of claims 67-70, wherein the polynucleotide is further defined as a plasmid or episome.
72. The method of claim 71, wherein the method further comprises the step of transfecting or transducing the cells with an effective amount of the respective plasmid or episome.
73. The method of claim 72, wherein the effective amount of the plasmid or episome for transfecting or transducing is about 1-200 ng per approximately 24,000 cells.
74. A method of reducing gene expression variability between individual cells of a gene of interest, comprising the steps of transfecting or transducing a plurality of cells with a plasmid or episome comprising an expression construct that expresses a single transcript encoding one or more components of a transcriptional negative feedback loop and one or more components of a post-transcriptional incoherent feedforward control for expression of the gene of interest, wherein the components of the transcriptional negative feedback loop and the post-transcriptional incoherent feedforward control are regulated by the same regulatory sequence.
75. The method of claim 74, where the expression construct is further defined as expressing a single transcript encoding a tetracycline repressor, a gene product from the gene of interest, and a miRNA flanked by splicing sites, wherein following exposure of an effective amount of an inducer to inhibit the repressor, the transcript is expressed and the miRNA is spliced out to inhibit production of the gene product of interest.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114085873A (en) * 2021-11-16 2022-02-25 珠海中科先进技术研究院有限公司 Cancer cell state identification gene circuit group and preparation method thereof

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996040946A1 (en) * 1995-06-07 1996-12-19 Yale University An autoregulatory tetracycline-regulated system for inducible gene expression in eucaryotes

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996040946A1 (en) * 1995-06-07 1996-12-19 Yale University An autoregulatory tetracycline-regulated system for inducible gene expression in eucaryotes

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
BLERIS LEONIDAS, XIE ZHEN, GLASS DAVID, ADADEY ASA, SONTAG EDUARDO, BENENSON YAAKOV: "Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template", MOLECULAR SYSTEMS BIOLOGY, THE MACMILLAN BUILDING, LONDON, GB, vol. 7, no. 1, 1 January 2011 (2011-01-01), GB , XP093009758, ISSN: 1744-4292, DOI: 10.1038/msb.2011.49 *
DEANS, TL ET AL.: "A Tunable Genetic Switch Based on RNAi and Repressor Proteins for Regulating Gene Expression in Mammalian Cells", CELL, vol. 130, no. 2, 27 July 2007 (2007-07-27), pages 363 - 372, XP002479815, DOI: 10.1016/j. cell . 2007.05.04 5 *
REEVES GREGORY T.: "The engineering principles of combining a transcriptional incoherent feedforward loop with negative feedback", JOURNAL OF BIOLOGICAL ENGINEERING, vol. 13, no. 1, 1 December 2019 (2019-12-01), XP093009756, DOI: 10.1186/s13036-019-0190-3 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114085873A (en) * 2021-11-16 2022-02-25 珠海中科先进技术研究院有限公司 Cancer cell state identification gene circuit group and preparation method thereof

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