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WO2024163909A2 - Prédiction de performance de programmes transcriptionnels fondamentaux - Google Patents

Prédiction de performance de programmes transcriptionnels fondamentaux Download PDF

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Publication number
WO2024163909A2
WO2024163909A2 PCT/US2024/014269 US2024014269W WO2024163909A2 WO 2024163909 A2 WO2024163909 A2 WO 2024163909A2 US 2024014269 W US2024014269 W US 2024014269W WO 2024163909 A2 WO2024163909 A2 WO 2024163909A2
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Prior art keywords
repressor
input
group
regulatory
dna binding
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WO2024163909A3 (fr
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Corey J. Wilson
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Georgia Tech Research Institute
Georgia Tech Research Corp
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Georgia Tech Research Institute
Georgia Tech Research Corp
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G16B5/10—Boolean models
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation

Definitions

  • the present disclosure relates to nucleic acid constructs and uses thereof.
  • Bio computation at its core, is the ability to engineer and develop systems capable of converting information (inputs) into a programmable gene expression (output(s)).
  • Gene regulation in biological systems can be viewed as a molecular computer. That is, gene expression can be modeled as on-off states of Boolean (digital) logic, which can integrate multiple digital inputs into a desired output.
  • Boolean digital
  • living cells can be programmed with genetic parts such as promoters, transcription factors, and metabolic genes to encode logical operations that integrate environmental and cellular signals.
  • Synthetic genetic logic gates have been engineered, including those capable of accomplishing Boolean functions (e.g., AND, OR, and NOT functions), which have been employed for pharmaceutical and biotechnological applications.
  • TFs transcription factors
  • TFs DNA-binding proteins capable of blocking (or recruiting) RNA polymerase activity at the site of genetic promoters, and these functions can be combined in modular ways to engineer synthetic gene networks.
  • early bacterial gene circuits were based on a core set of repressors, namely, TetR, LacI, and bacteriophage ⁇ cl, which have been extensively studied.
  • the present disclosure provides constructs and cell compositions for reprogramming cells.
  • the present disclosure also provides methods using constructs and cell compositions to modify and/or monitor cells.
  • the present disclosure expands transcriptional programming from logical operations having two inputs that can provide up to 16 logical operations to three inputs that can provide provide up to 256 logical operations, e.g., to form a Turing complete and scalable decision- making platform technology for biocomputing and biological intelligence.
  • the integrated biological circuit can increase biological computing capacity while minimizing metabolic burden.
  • exemplary systems and methods employing modeling of transcriptional programming that leverages systems of engineered transcription factors to impart decision-making (e.g., Boolean logic) in chassis cells to predict the performance of transcriptional programs.
  • decision-making e.g., Boolean logic
  • the exemplary systems and methods can be employed as a predictive tool to guide and accelerate the design of transcriptional programs.
  • the exemplary system employs the development and experimental characterization of a large collection of network capable single-INPUT logical operations - i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations.
  • engineered BUFFER repressor
  • engineered NOT antirepressor
  • the system can model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates, and compressed NOR gates).
  • the system can model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates, and complementary B NIMPLY A gates).
  • a study was conducted and the results demonstrated that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit.
  • a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains; a second group of one or more regulatory core domains; one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose- responsive anti-repressor; and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the cellobiose-responsive anti-repressor comprises cellobiose- responsive anti-repressor E A1 , E A2 , E A3 , or a variant thereof.
  • the first group of the one or more regulatory core domains, the second group of the one or more regulatory core domains, and a third group of one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose-responsive anti-repressor, to form a three-input transcription program.
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises a second repressor.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises an anti-repressor
  • the first group of one or more regulatory core domains comprises at first anti-repressor, and the second group of one or more regulatory core domains comprises a second anti-repressor.
  • a method of modifying a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of a cell comprising a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains, a second group of one or more regulatory core domains formed of a cellobiose-responsive anti-repressor, one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains, and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the cellobiose-responsive anti-repressor comprises cellobiose- responsive anti-repressor E A1 , E A2 , E A3 , or a variant thereof.
  • the first group of the one or more regulatory core domains, the second group of the one or more regulatory core domains, and a third group of one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose-responsive anti-repressor, to form a three-input transcription program.
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises a second repressor.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises an anti-repressor
  • the first group of one or more regulatory core domains comprises a first anti-repressor
  • the second group of one or more regulatory core domains comprises a second anti-repressor
  • the method of modifying a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of the cell of any preceding aspect, wherein the cell comprises the construct of any preceding aspect.
  • a method of modifying a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of a cell comprising a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains, a second group of one or more regulatory core domains formed of a cellobiose-responsive anti-repressor, one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains, and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the cellobiose-responsive anti-repressor comprises cellobiose- responsive anti-repressor E A1 , E A2 , E A3 , or a variant thereof.
  • the first group of the one or more regulatory core domains, the second group of the one or more regulatory core domains, and a third group of one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose-responsive anti-repressor, to form a three-input transcription program.
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises a second repressor.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises an anti-repressor
  • the first group of one or more regulatory core domains comprises at first anti-repressor, and the second group of one or more regulatory core domains comprises a second anti-repressor.
  • the method of modifying a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of the cell of any preceding aspect, wherein the cell comprises the construct of any preceding aspect.
  • a method to predict transcriptional programming of gene expression, the method comprising: providing a gene model having network repressors and network anti-repressors, wherein the network repressors and network anti-repressors are designated for binding to a plurality of candidate DNA binding function (e.g., YQR
  • a plurality of candidate DNA binding function e.g.
  • a method comprising: providing a gene model having network repressors and network anti-repressors, wherein the network repressors and network anti-repressors are designated for binding to a plurality of candidate DNA binding function (e.g., YQR
  • a plurality of candidate DNA binding function e.g., YQR
  • the method further includes applying one or more of the plurality of candidate DNA binding function as a second 2-node single-in single-out (SISO) networks of transcription factors binned via the alternate DNA binding function following the application of the one or more of the plurality of candidate DNA binding function as the first 2-node SISO networks to provide a combination of the first 2-node SISO networks and the second 2-node SISO networks, wherein the combination of the first 2-node SISO networks and the second 2-node SISO networks is equivalent to a multiple input single output (MISO) network.
  • SISO 2-node single-in single-out
  • the outputted predicted value is for the combination.
  • the plurality of candidate DNA binding function is applied via two or more of: a 1 -INPUT BUFFER logic operation (e.g., single repressor), a 1 -INPUT NOT logic gate operation (e.g., having a single anti-repressor), a 2-INPUT AND logic gate operation (e.g., having a repressor pair), a 2-INPUT NOR logic gate operation (e.g., having an antirepressor pair), a 2-INPUT A NIMPLY B logic gate operation (e.g., having a repressor/anti-repressor pair), a 2-INPUT B NIMPLY A logic gate operation (e.g., having an anti-repressor/repressor pair), a 2-INPUT XNOR logic gate operation, and a 2-INPUT NAND logic gate operation.
  • a 1 -INPUT BUFFER logic operation e.g., single repressor
  • a 1 -INPUT NOT logic gate operation
  • the 2-INPUT AND logic gate operation comprises: wherein ⁇ is a value for fluorescence in the absence of inducer, ⁇ is a value constant for a maximum fluorescence relative to basal expression of an OFF-state, is a coarse-grained Hill function (e.g., having a value of 0 or 1), and A A (I) is a coarse-grained antithetical Hill-function for anti- repression (e.g., where 0 INPUT corresponds to the ON-state).
  • the 2-INPUT NOR logic gate operation comprises: [0043] In some embodiments, the 2-INPUT A NIMPLY B logic gate operation comprises:
  • the 2-INPUT B NIMPLY A logic gate operation comprises:
  • the gene model comprises: a lactose repressor (LacI) topology having (i) a regulatory core domain (RCD) and (ii) a DNA binding domain (DBD).
  • LacI lactose repressor
  • each 2-node SISO network comprises (i) a single transcription factor expressed on the pLacI plasmid (Novagen) (e.g., having pl 5a origin (copy number 20-30/cell)) and (ii) a super folder fluorescent protein (GFP) reporter (e.g., expressed on the pZS*22-sfGFP plasmid containing a pSClOl origin).
  • pLacI plasmid Novagen
  • GFP super folder fluorescent protein
  • the REU is a measure of fluorescence of a reporter fusion to a nucleic acid sequence (e.g., first 25 AA) of a gene of interest.
  • Figs, la-lf shows an example of circuit compression is shown.
  • Fig. 1 shows a NOR gate that can constructed using inversion (left) or by using anti-repressors (middle). The logical truth table is shown to the right.
  • Fig. lb shows a workflow for engineering a cellobiose anti-repressor.
  • Fig. 1c shows a transcriptional regulation of GFP expression by the TFs in Fig. lb.
  • Fig. Id shows the putative transcriptional programming design space for repressor/anti-repressor pairs is shown. IPTG is known to inhibit fucose-responsive TFs.
  • Fig. le shows reduced design space for transcriptional programming based on orthogonality.
  • Fig. 2 shows the validation of anti-CelRs equipped with alternate DBDs.
  • Fig. 3a shows an expression of GFP versus LacLGFP fusion reporters from five synthetic promoters is shown.
  • Fig. 3b shows an expression of LacLGFP versus LacI-mKate from five synthetic promoters is shown.
  • Fig. 3c shows an RBS library applied to three promoters does not show modularity.
  • Fig. 3d shows the genetic schematic of an REU standard curve for Nanoluc is shown.
  • Fig. 3e shows the measured REU as a function of AHL concentration is shown for Fig. 3d.
  • Fig. 3f shows activity of full-length Nanoluc as a function of REU is shown to correlate linearly.
  • Fig. 3g shows the genetic schematic of an REU standard curve for LacI is shown.
  • Fig. 3h shows the measured REU as a function of AHL concentration is shown for Fig. 3g.
  • Fig. 2i shows REU(LacI) correlates linearly with REU(Nanoluc) over varying transcription levels.
  • Figs. 4a - 41 shows a genetic schematic for the transcription factor titration circuit and associated operation.
  • Fig. 4a shows the genetic schematic for the transcription factor titration circuit.
  • Fig. 4b shows an example dataset generated from the measurement of RbsR(YQR) in the circuit from Fig. 4a.
  • REUin corresponds to the REU value at which the TF is expressed
  • REUout corresponds to the measured fluorescence of the LacLGFP reporter.
  • Fig. 4c shows the performance metric for Fig. 4b.
  • Fig. 4d shows the genetic context of an RBS library designed for modulating the constitutive expression of TFs.
  • Fig. 4e shows data corresponding to select RBSs from Fig. 3d.
  • FIG. 34 shows the genetic schematic for BUFFER/NOT gates.
  • Figs. 4g - 4i shows BUFFER gate performances are shown compared to the predicted expression levels based on REU modeling.
  • Figs. 4j - 41 shows NOT gate performances compared to predicted expression levels based on REU modeling.
  • Fig. 5 shows an investigation of the impact of GOI length on LacLGFP fusion reporters.
  • Fig. 6 shows the non-modularity of RBS libraries applied to different promoters.
  • Fig. 7 shows non-modularity of ribozymes.
  • Fig. 8a shows the workflow for predictive transcriptional program design is shown for an A IMPLY B gate.
  • Fig. 8b shows the wiring diagram for a 3-input consensus program is shown (left) along with the measured performance (right).
  • Figa. 9a - 9c show titration curves for all LacI and 1(A) (Fig. 9a), all RbsR and R(A) (Fig. 9b), and all CelR and E(A) (Fig. 9c).
  • Fig. 10 shows transcription factor orthogonality.
  • Fig. 11 shows additional examples of 2-input circuits.
  • Fig. 12a shows the biosynthetic pathway for lycopene production.
  • Fig. 12b shows the genetic schematic for measuring REU of each Crt gene.
  • Fig. 12c shows the dose-response functions of the three circuits from Fig. 12b.
  • the vertical dashed lines indicate the induced concentration that yields an REU of 100.
  • Fig. 12d shows the genetic schematic for the fully refactored synthase pathway.
  • Fig. 12e shows the measured lycopene titer from the circuit in Fig. 12d.
  • Fig. 12f The genetic schematic for measuring REU of the Crt genes in an operon.
  • Fig. 512 shows the measured REU values for the three constructs from Fig. 12f.
  • Fig. 512 shows the genetic schematic for the functional operon.
  • Fig. 13 shows the measured lycopene titer from the circuit along with the titer achieved from expressing the circuit in Fig. 12d at the same levels.
  • Fig. 14 shows a comparison of DNA sequence-based models of expression.
  • Fig. 15a- 15g shows modular components used in a design space.
  • Fig. 15a shows the performance card of a repressor (X+) and the abstraction of metrics to a logical BUFFER operation.
  • Fig. 15b shows the performance card of an anti-repressor (XA) and the abstraction of metrics to a logical NOT operation.
  • Fig. 15c shows the design space overview in which each of the 5 X + or X A RCDs can be paired with 1 of 8 ADRs and directed to 1 of 2 operator positions (OPs), resulting in a putative design space of 80 BUFFER and 80 NOT operations.
  • FIG. 15d - 15g shows example genetic architectures for a PROXIMAL architecture with an operator position downstream of the promoter in which the transcription factor interferes with RNA polymerase’s ability to transcribe DNA (Fig. 15d), a CORE architecture featuring an operator intercalated between the -35 and -10 hexamers of the synthetic trc promoter in E. coli in which the transcription factor competes with RNA polymerase binding to DNA (Fig. 15e).
  • Figs. 15f — 15g shows a two-input architecture.
  • Fig. 15f shows PROXIMAL SE-PA architecture, as shown in Fig. 15d, with two transcription factors directed to the operator.
  • Fig. 15g shows CORE SE-PA architecture as shown in Fig. 15e, with two transcription factors directed to the DNA operator.
  • Figs. 16a - 16b show example combinatorial sets of SE-PA AND gates.
  • Fig. 16a shows an illustration of non-synonymous repressor pairs combined with 8 ADRs yielding 80 putative PROXIMAL SE-PA AND gates.
  • Repressors classified as non-operational (Fig. 26) are shown faded, and incompatible repressor pairs (Fig. 28) are highlighted in red.
  • Fig. 16b shows CORE SE-PA architecture AND gates. Elimination of non- operational and incompatible repressors results in 72 CORE SE-PA AND gates.
  • Figs. 17a - 17b show SE-PA AND operation and NOR operation predictive models using BUFFER SISO and NOT SISO parameters.
  • Fig. 17a shows an AND gate logic modeled using a quadratic function of IX and IY, which control the repressor state functions A J and Ay. Each term has a coefficient ⁇ 0, ⁇ 1, ⁇ 2, or ⁇ 3, which are estimated as functions of BUFFER gate parameters ⁇ x, ⁇ Y, ⁇ X, and ⁇ Y (also see Fig. 15a). Functions for parameters a0, al, a2, and a3 are derived using four assumptions corresponding to each INPUT condition.
  • NOR gate logic is modeled analogous to AND logic, however, with a pair of NOT gates parameterized with anti-repressor state functions (also see Fig. IB). Given that Ay and Ay functions capture the ON-OFF state inversion from the repressor to anti-repressor phenotype, ⁇ 0, ⁇ 1, ⁇ 2 , and ⁇ 3 parameters are estimated with the same functions for both AND and NOR models.
  • Figs. 18a - 18b show results showing the correlation between predicted and measured OUTPUT of 133 SE-PA AND gates. Under-predictions and over-predictions fall above and below the theoretical value of 1 (red line), respectively.
  • Fig. 18a shows correlation results for 61 PROXIMAL SE-PA AND gates across the 4 INPUT conditions.
  • INPUTS A and B correspond to repressors X + and Y + ’ respectively) and can be inferred from each BUFFER pair depicted in Fig. 16b.
  • Fig. 18b shows the correlation between predicted and measured OUTPUT of 72 CORE SE-PA AND gates.
  • INPUTS A and B correspond to repressors X + and Y + and can be inferred from each BUFFER pair depicted in Fig. 16b.
  • Figs. 19a - 19b shows a combinatorial set of 131 SE-PA NOR gates.
  • Fig. 19b shows an illustration of non-synonymous anti-repressor pairs combined with 8 ADRs yielding 80 putative PROXIMAL SE-PA NOR gates.
  • Anti-repressors classified as nonoperational are shown faded, and incompatible anti-repressor pairs (see Fig. 21b) are highlighted in red. These non-operational pairs result in a reduced space of 60 proximal SE-PA NOR gates.
  • Fig. 19b shows CORE SE-PA architecture NOR gates. Elimination of non-operational and incompatible anti-repressors results in 71 CORE SE-PA.
  • Figs. 20a - 20b show results showing the correlation between predicted and measured OUTPUT of 131 SE-PA NOR gates. Under-predictions and over-predictions fall above and below the theoretical value of 1 (red line), respectively.
  • Fig. 20a shows correlation results for 60 PROXIMAL SE-PA NOR gates across the 4 INPUT conditions.
  • INPUTS A and B correspond to anti-repressors X A and Y A , respectively, and can be inferred from each NOT pair depicted in Fig. 5A.
  • Fig. 20b shows the correlation between predicted and measured OUTPUT of 71 CORE SE-PA NOR gates.
  • INPUTS A and B correspond to repressors X + and Y + and can be inferred from each NOT pair depicted in Fig. 19b.
  • Figs. 21a - 211 show results for 12 SE-PA NIMPLY logic gates at the CORE operator position.
  • Signal INPUTs IPTG, Ribose, Fucose, and Fructose
  • Figs. 21a - 21f show an A NIMPLY B logic employing a repressor which responds to INPUT A and antirepressor which responds to INPUT B.
  • FIG. 21g - 211 show complimentary A NIMPLY B logic utilizing an anti-repressor and repressor
  • Figs. 22a - 22b show NIMPLY predictive models using BUFFER and NOT gate parameters.
  • Fig. 22a shows an A NIMPLY B gate logic is modeled using a quadratic function of lx and IY, which controls the repressor state function and anti-repressor state function .
  • Each term has a coefficient ⁇ 0, ⁇ 1, ⁇ 2, or ⁇ 3, which are estimated as functions of BUFFER and NOT gate parameters ⁇ x, ⁇ Y, ⁇ X, and ⁇ Y.
  • Fig. 22b shows a B NIMPLY A gate logic modeled analogous to the A NIMPLY B logic but with an anti-repressor state function and repressor state function
  • Figs. 23a - 231 shows results for 6 SERI AND operations and 6 SERI NOR operations.
  • Figs. 23a - 23f shows AND logic gates employing a repressor directed to a cognate PROXIMAL operator (top input), and second repressor directed to a cognate CORE operator (bottom input). Results for OUTPUT prediction using SE-PA SISO parameters, prediction using SERI SISO parameters, and measured OUTPUT are shown on the right.
  • Figs. 9g - 91 show NOR logic gates employing antirepressors
  • Figs. 24 (part 1) - 24 (part 5) show PROXIMAL BUFFER and NOT Gate Performance Cards.
  • Fig. 24 (Part 1) shows LacI performance cards, Fig. 24 (Part 2) shows RbsR ( performance cards, Fig. 24 (Part 3) shows CelR performance cards, Fig. 24 (Part 4) shows GalR performance cards, and Fig. 24 (Part 5) shows FruR performance cards.
  • Figs. 24 (part 6) - 24 (part 10) show PROXIMAL NOT gate performance cards. Each card is analogous to those in Parts 1-5 but includes respective metrics for NOT gates.
  • Fig. 24 (Part 6) shows Anti-Laci performance cards
  • Fig. 24 (Part 7) shows Anti-RbsR performance cards
  • Fig. 24 (Part 8) shows PurR performance cards
  • Fig. 24 (Part 9) shows Anti-GalS performance cards
  • Fig. 24 (Part 10) shows Anti-FruR performance cards.
  • Fig. 25 shows CORE BUFFER and NOT Gate Performance Cards. Each card displays experimental ON and OFF state OUTPUT values, INPUT signal type, DNA operator (ADR) type, and system performance metrics. Card outline color depicts the phenotype of each operation, consistent with Figure S3.
  • Each card is analogous to those in Parts 1-5 but includes respective metrics for NOT gates.
  • S2 - Part 6 Anti -LacI (IA ADR) performance cards,
  • S2 - Part 7) Anti-RbsR (RAADR) performance cards,
  • S2 - Part 9) Anti-Gal S (SAADR) performance cards, and
  • Fig. 26 shows operational and non-operational SISO logic gates.
  • Operational gates consist of either (A) repressor - i.e., BUFFER logic - or (B) anti-repressor i.e., NOT logic - phenotypes. Classification of non-operational gates as either (C) super- repressor or (D) nonfunctional phenotypes.
  • Figs. 27a - 27e show example genetic architectures.
  • Fig. 27a shows a PROXIMAL architecture with an operator position downstream of the promoter. Transcription factor blocks RNA polymerase from transcribing DNA to regulate expression.
  • Fig. 27b shows a CORE architecture featuring an operator intercalated between the -35 and -10 hexamers of the synthetic trc promoter in E. coli. Transcription factor competes with RNA polymerase to bind DNA to regulate output expression.
  • Figs. 27c - 27e show two-input architectures.
  • Fig. 27c shows a PROXIMAL SE-PA architecture as shown in Fig. 27a, with two transcription factors directed to the operator.
  • FIG. 27d shows a CORE SE-PA architecture, as shown in Fig. 27b, with two transcription factors directed to the operator.
  • Fig. 27e shows SERI architecture featuring a CORE operator and a second (non-synonymous) PROXIMAL operator.
  • Figs. 28a - 28b show compatible and incompatible AND gate components.
  • Fig. 28b shows two compatible BUFFER operations constitute an AND gate when the OFF-state OUTPUT of either repressor is lower than the ON-state OUTPUT of the other.
  • Fig. 28b shows a pair of BUFFER operations are incompatible when the inequalities shown in Fig. 28a are not met. Incompatible pairs are unlikely to produce a functional AND gate in that relative ON-state OUTPUT cannot be achieved across four input conditions.
  • Figs. 29a - 29b show histograms of prediction error.
  • Fig. 29a shows error, defined as the ratio of measured to predicted OUTPUT, for all 133 SE-PA AND gates across all four INPUT conditions (the error is equivalent to values given in plots illustrated in Figs. 18a - 18b). Values below 1 are overpredictions, and values above 1 are underpredictions. Blue bars indicate ⁇ 2-fold error, and red bars indicate > 2-fold error in either direction.
  • Fig. 29b shows histograms of prediction error for 131 NOR gates given in Figs. 20a - 20b.
  • Figs. 30a - 30b shows compatible and incompatible NOR gate components. Fig.
  • FIG. 31b shows a complementary B NIMPLY A operation utilizing an BUFFER operation.
  • Figs. 31c - 3 Id show CORE SE-PA NIMPLY logic.
  • Fig. 31c shows an example A NIMPLY B logic gate shown in Fig. 3 la at the CORE operator position.
  • Fig. 3 Id shows a complimentary B NIMPLY A operation shown in Fig. 3 lb also directed to the CORE position. All NIMPLY gates respond to the same two INPUTs; however, variations of transcription factor phenotypes and DNA operator position yield differences in performance.
  • Figs. 32a - 321 show results for PROXIMAL SE-PA NIMPLY Logic, which is analogous to Figs.
  • Figs. 32a - 32f show an X NIMPLY Y logic employing a repressor which responds to INPUT A and anti- repressor which responds to INPUT B.
  • Figs. 32g - 321 show complimentary a B NIMPLY A logic utilizing an anti-repressor and repressor
  • Figs. 33a - 331 show results for Insulated SERI AND Gates and NOR Gates. Specifically, results for 6 insulated SERI AND operations and 6 insulated SERI NOR operations (analogous gates to those in Figs. 23a - 231, with the addition of the genetic insulator RiboJIO).
  • Figs. 33a - 33f show AND logic gates employing a repressor directed to a cognate PROXIMAL operator (top input), and second repressor directed to a cognate CORE operator (bottom input). Results for OUTPUT prediction using SERI SISO parameters and measured OUTPUT are shown on the right.
  • Figs. 33g - 331 show insulated NOR logic gates employing anti-repressors via the SERI genetic architecture.
  • composition refers to any agent that has a beneficial biological effect. Beneficial biological effects include both therapeutic effects, e.g., treatment of a disorder or other undesirable physiological condition, and prophylactic effects, e.g., prevention of a disorder or other undesirable physiological condition.
  • composition also encompass pharmaceutically acceptable, pharmacologically active derivatives of beneficial agents specifically mentioned herein, including, but not limited to, a vector, polynucleotide, cells, salts, esters, amides, proagents, active metabolites, isomers, fragments, analogs, and the like.
  • composition includes the composition per se as well as pharmaceutically acceptable, pharmacologically active vector, polynucleotide, salts, esters, amides, proagents, conjugates, active metabolites, isomers, fragments, analogs, etc.
  • compositions, methods, etc. include the recited elements, but do not exclude others.
  • Consisting essentially of' when used to define compositions and methods shall mean including the recited elements, but excluding other elements of any essential significance to the combination. Thus, a composition consisting essentially of the elements as defined herein would not exclude trace contaminants from the isolation and purification method and pharmaceutically acceptable carriers, such as phosphate buffered saline, preservatives, and the like.
  • Consisting of' shall mean excluding more than trace elements of other ingredients and substantial method steps for administering the compositions provided and/or claimed in this disclosure. Embodiments defined by each of these transition terms are within the scope of this disclosure.
  • An "increase” can refer to any change that results in a greater amount of a symptom, disease, composition, condition, or activity.
  • An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount.
  • the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100%, or more, increase so long as the increase is statistically significant.
  • a “decrease” can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity.
  • a substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance.
  • a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed.
  • a decrease can be any individual, median, or average decrease in a condition, symptom, activity, composition in a statistically significant amount.
  • the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% decrease so long as the decrease is statistically significant.
  • Inhibit means to decrease an activity, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, response, condition, or disease. This may also include, for example, a 10% reduction in the activity, response, condition, or disease as compared to the native or control level. Thus, the reduction can be a 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%, or any amount of reduction in between as compared to native or control levels.
  • prevent or other forms of the word, such as “preventing” or “prevention,” is meant to stop a particular event or characteristic, to stabilize or delay the development or progression of a particular event or characteristic, or to minimize the chances that a particular event or characteristic will occur. Prevent does not require comparison to a control as it is typically more absolute than, for example, reduce. As used herein, something could be reduced but not prevented, but something that is reduced could also be prevented. Likewise, something could be prevented but not reduced, but something that is prevented could also be reduced. It is understood that where reduce or prevent are used, unless specifically indicated otherwise, the use of the other word is also expressly disclosed.
  • the term “subject” refers to any individual who is the target of administration or treatment.
  • the subject can be a vertebrate, for example, a mammal.
  • the subject can be human, non-human primate, bovine, equine, porcine, canine, or feline.
  • the subject can also be a guinea pig, rat, hamster, rabbit, mouse, or mole.
  • the subject can be a human or veterinary patient.
  • patient refers to a subject under the treatment of a clinician, e.g., a physician.
  • a “promoter,” as used herein, refers to a sequence in DNA that mediates the initiation of transcription by an RNApolymerase.
  • Transcriptional promoters may comprise one ormore of a number of different sequence elements as follows: 1) sequence elements present at the site of transcription initiation; 2) sequence elements present upstream of the transcription initiation site and; 3) sequence elements down- stream of the transcription initiation site.
  • the individual sequence elements function as sites on the DNA, where RNA polymerases and transcription factors that facilitate positioning of RNA polymerases on the DNA bind.
  • a “transcription factor” refers to a sequence-specific DNA-binding protein that controls the rate of transcription of genetic information from DNA to messenger RNA, by binding to a specific DNA sequence.
  • a “transcription terminator” or a “terminator” refers to a segment of a nucleic acid sequence that marks the end of gene in genomic DNA during the transcription process, or gene expression. This sequence mediates or signals the end of transcription by providing signaling nucleotides in newly synthesized RNA transcripts that trigger an RNA polymerase to release the DNA and newly synthesized RNA.
  • the word “vector” refers to any vehicle that carries a polynucleotide into a cell for the expression of the polynucleotide in the cell.
  • the vector may be, for example, a plasmid, a virus, a phage particle, or a nanoparticle.
  • a “bacterial plasmid” is a small extrachromosomal DNA molecule that can be incorporated into another cell that is physically separated from the chromosomal DNA and is easily replicated. Once transformed into a suitable host, the vector may replicate and function independently of the host genome, or may, in some instances, integrate into the genome itself.
  • the vector is a DNA construct containing a DNA sequence which is operably linked to a suitable control sequence capable of effecting the expression of the DNA in a suitable host cell.
  • control sequences can include a promoter to effect transcription, an optional operator sequence to control such transcription, a sequence encoding suitable mRNA ribosome binding sites, and sequences that control the termination of transcription and translation.
  • administer refers to delivering a composition, substance, inhibitor, or medication to a subject or object by one or more the following routes: oral, topical, intravenous, subcutaneous, transcutaneous, transdermal, intramuscular, intra-joint, parenteral, intra-arteriole, intradermal, intraventricular, intracranial, intraperitoneal, intralesional, intranasal, rectal, vaginal, by inhalation or via an implanted reservoir.
  • parenteral includes subcutaneous, intravenous, intramuscular, intra- articular, intra-synovial, intrastemal, intrathecal, intrahepatic, intralesional, and intracranial injections or infusion techniques.
  • a “host” refers to an organism or cell into which a heterologous component (polynucleotide, polypeptide, other molecule, cell) has been introduced.
  • a “host cell” refers to an in vivo or in vitro eukaryotic cell, prokaryotic cell (e.g., bacterial or archaeal cell), or cell from a multicellular organism (e.g., a cell line) cultured as a unicellular entity, into which a heterologous polynucleotide or polypeptide has been introduced.
  • the cell is selected from the group consisting of: an archaeal cell, a bacterial cell, a eukaryotic cell, a eukaryotic single-cell organism, a somatic cell, a germ cell, a stem cell, a plant cell, an algal cell, an animal cell, in an invertebrate cell, a vertebrate cell, a fish cell, a frog cell, a bird cell, an insect cell, a mammalian cell, a pig cell, a cow cell, a goat cell, a sheep cell, a rodent cell, a rat cell, a mouse cell, a non-human primate cell, and a human cell.
  • the cell is in vitro. In some cases, the cell is in vivo.
  • an "effective amount” is an amount sufficient to affect beneficial or desired results.
  • An effective amount can be administered in one or more administrations, applications or dosages.
  • Effective amount encompasses, without limitation, an amount that can ameliorate, reverse, mitigate, prevent, or diagnose a symptom or sign of a medical condition or disorder (e.g., HIV-1 infection). Unless dictated otherwise, explicitly or by context, an “effective amount” is not limited to a minimal amount sufficient to ameliorate a condition.
  • the severity of a disease or disorder, as well as the ability of a treatment to prevent, treat, or mitigate the disease or disorder, can be measured, without implying any limitation, by a biomarker or by a clinical parameter.
  • microbiota refers to the range of microorganisms that may be commensal, symbiotic, or pathogenic found in and on all multicellular organisms, including plants and animals. These include bacteria, archaea, protists, fungi, and viruses and have been found to be crucial for the immunologic, hormonal, and metabolic homeostasis of the host.
  • monitoring refers to the actions of observing and checking the progress or quality of a treatment or procedure over a period of time.
  • monitoring refers to the actions of observing and checking for changes to the GI tract microbiome following the administration of a cell comprising a construct to (re)program to transcriptional regulation of the microbiome.
  • a “nucleotide” is a compound consisting of a nucleoside, which consists of a nitrogenous base and a 5-carbon sugar, linked to a phosphate group forming the basic structural unit of nucleic acids, such as DNA or RNA.
  • the four types of nucleotides are adenine (A), cytosine (C), guanine (G), and thymine (T), each of which are bound together by a phosphodiester bond to form a nucleic acid molecule.
  • a “nucleic acid” is a chemical compound that serves as the primary information- carrying molecules in cells and make up the cellular genetic material. Nucleic acids comprise nucleotides, which are the monomers made of a 5-carbon sugar (usually ribose or deoxyribose), a phosphate group, and a nitrogenous base. A nucleic acid can also be a deoxyribonucleic acid (DNA) or a ribonucleic acid (RNA).
  • DNA deoxyribonucleic acid
  • RNA ribonucleic acid
  • percent identity and “% identity,” as applied to polynucleotide sequences, refer to the percentage of residue matches between at least two polynucleotide sequences aligned using a standardized algorithm. Such an algorithm may insert, in a standardized and reproducible way, gaps in the sequences being compared in order to optimize alignment between two sequences and, therefore, achieve a more meaningful comparison of the two sequences. Percent identity for a nucleic acid sequence may be determined as understood in the art. (See, e.g., U.S. Pat. No. 7,396,664, which is incorporated herein by reference in its entirety).
  • NCBI National Center for Biotechnology Information
  • BLAST Basic Local Alignment Search Tool
  • the BLAST software suite includes various sequence analysis programs, including “blastn,” that is used to align a known polynucleotide sequence with other polynucleotide sequences from a variety of databases.
  • blastn a tool that is used to align a known polynucleotide sequence with other polynucleotide sequences from a variety of databases.
  • BLAST 2 Sequences also available is a tool called “BLAST 2 Sequences” which is used for direct pairwise comparison of two nucleotide sequences. “BLAST 2 Sequences” can be accessed and used interactively at the NCBI website.
  • the “BLAST 2 Sequences” tool can be used for both blastn and blastp (discussed above).
  • Percent identity may be measured over the length of an entire defined polynucleotide sequence or may be measured over a shorter length, for example, over the length of a fragment taken from a larger, defined sequence, for instance, a fragment of at least 20, at least 30, at least 40, at least 50, at least 70, at least 100, or at least 200 contiguous nucleotides. Such lengths are exemplary only, and it is understood that any fragment length may be used to describe a length over which percentage identity may be measured.
  • a “full length” polynucleotide sequence is one containing at least a translation initiation codon (e.g., methionine) followed by an open reading frame and a translation termination codon.
  • a “full length” polynucleotide sequence encodes a “full length” polypeptide sequence.
  • a “variant,” “mutant,” or “derivative” of a particular nucleic acid sequence may be defined as a nucleic acid sequence having at least 50% sequence identity to the particular nucleic acid sequence over a certain length of one of the nucleic acid sequences using blastn with the “BLAST 2 Sequences” tool available at the National Center for Biotechnology Information's website. (See Tatiana A. Tatusova, Thomas L. Madden (1999), “Blast 2 sequences — a new tool for comparing protein and nucleotide sequences”, FEMS Microbiol Lett. 174:247-250).
  • a variant polynucleotide may show, for example, at least 60%, at least 70%, at least 80%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 98%, or at least 99% or greater sequence identity over a certain defined length relative to a reference polynucleotide.
  • upstream refers to the relative position of a genetic sequence, either DNA or RNA. Upstream relates to the 5’ to 3’ direction relative to the start site of transcription, wherein upstream is usually closer to the 5’ end of a genetic sequence.
  • downstream refers to the relative position of a genetic sequence, either DNA or RNA. Downstream relates to the 5’ to 3’ direction relative the start site of transcription, wherein downstream is usually closer to the 3’ end of a genetic sequence.
  • Gene includes a nucleic acid fragment that expresses a functional molecule such as, but not limited to, a specific protein, including regulatory sequences preceding (5’ noncoding sequences) and following (3’ non-coding sequences) the coding sequence.
  • Native gene refers to a gene as found in its natural endogenous location with its own regulatory sequences.
  • knock-out represents a DNA sequence of a cell that has been rendered partially or completely inoperative by targeting with a Cas protein; for example, a DNA sequence prior to knock-out could have encoded an amino acid sequence or could have had a regulatory function (e.g., promoter).
  • a regulatory function e.g., promoter
  • knock-in represents the replacement or insertion of a DNA sequence at a specific DNA sequence in cell by targeting with a Cas protein (for example, by homologous recombination (HR), wherein a suitable donor DNA polynucleotide is also used)
  • examples of knock-ins are a specific insertion of a heterologous amino acid coding sequence in a coding region of a gene, or a specific insertion of a transcriptional regulatory element in a genetic locus.
  • domain means a contiguous stretch of nucleotides (that can be RNA, DNA, and/or RNA-DNA-combination sequence) or amino acids.
  • An “enhancer” is a DNA sequence that can stimulate promoter activity and may be an innate element of the promoter or a heterologous element inserted to enhance the level or tissue-specificity of a promoter. Promoters may be derived in their entirety from a native gene or be composed of different elements derived from different promoters found in nature and/or comprise synthetic DNA segments. It is understood by those skilled in the art that different promoters may direct the expression of a gene in different tissues or cell types, at different stages of development, or in response to different environmental conditions. It is further recognized that since, in most cases, the exact boundaries of regulatory sequences have not been completely defined, DNA fragments of some variation may have identical promoter activity.
  • the present disclosure provides transcriptional programming using logical operations having three or more inputs that can provide up to 256 logical operations, e.g., to form a Turing complete and scalable decision-making platform technology for biocomputing and biological intelligence.
  • a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains; a second group of one or more regulatory core domains; one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose- responsive anti-repressor; and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the cellobiose-responsive anti-repressor comprises cellobiose- responsive anti-repressor E A1 , E A2 , E A3 , or a variant thereof.
  • the first group of the one or more regulatory core domains, the second group of the one or more regulatory core domains, and a third group of one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose-responsive anti-repressor, to form a three-input transcription program.
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises a second repressor.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises an anti-repressor.
  • the first group of one or more regulatory core domains comprises at first anti-repressor
  • the second group of one or more regulatory core domains comprises a second anti-repressor
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof. In some embodiments, the first group of one or more regulatory core domains comprises one, two, three, four, five or more repressors or one, two, three, four, five or more anti-repressor, or a combination thereof. In some embodiments, the first group of one or more regulatory core domains comprises at least two repressors, at least two anti-repressors, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof. In some embodiments, the second group of one or more regulatory core domains comprises one, two, three, four, five or more repressors or one, two, three, four, five or more anti-repressor, or a combination thereof. In some embodiments, the second group of one or more regulatory core domains comprises at least two repressor or at least two anti-repressors, or a combination thereof.
  • the first group of one or more regulatory core domains is specifically recognized by a first agent.
  • the first agent is isopropyl-P- D- 1 -thiogalactopyranoside.
  • the second group of one or more regulatory core domains is specifically recognized by a second agent.
  • the second agent is D- ribose.
  • the first and second groups of the one or more regulatory core domains are linked to a same DNA binding domain. In some embodiments, the first and second groups of the one or more regulatory core domains are linked to different DNA binding domains.
  • the construct comprises a plurality of nucleic acid sequences encoding a first group of two regulatory core domains, a second group of two regulatory core domains, and three DNA binding domains, wherein the first group of the regulatory core domains and the second group of the regulatory core domains are each linked to one of the three DNA binding domains, and three DNA operator elements that are each specifically recognized by one of the three DNA binding domains.
  • the construct further comprises a nucleic acid sequence encoding a reporter including, but not limited to green fluorescent protein (GFP), yellow fluorescent protein (YFP), blue fluorescent protein (BFP), cyane fluorescent protein (CFP), monomeric red fluorescent protein (mRFP), Discosoma striata (DsRed), mCherry, mOrange, tdTomato, mSTrawberry, mPlum, photoactivatable GFP (PA-GFP), Venus, Kaede, monomeric kusabira orange (mKO), Dronpa, enhanced CFP (ECFP), Emerald, Cyan fluorescent protein for energy transfer (CyPet), super CFP (SCFP), Cerulean, photoswitchable CFP (PS-CFP2), photoactivatable RFP1 (PA-RFP1), photoactivatable mCherry (PA-mCherry), monomeric teal fluorescent protein (mTFP1), Eos fluorescent protein (EosFP), Dendra, TagB
  • GFP green fluorescent protein
  • the construct is coupled to a nucleic acid sequence encoding components of a CRISPR gene editing system.
  • CRISPR Clustered regularly interspaced short palindromic repeats
  • CRISPR/-Cas9 CRISPR-associated system
  • off-target DNA cleavage Cong et al., 2013; Doudna, 2020; Fu et al., 2013; Jinek et al., 2013
  • Cas9 recognizes mismatches are poorly understood (Kim et al., 2019; Liu et al., 2020; Slaymaker and Gaudelli, 2021).
  • the construct further comprises a nucleic acid sequence encoding a dead Cas9 endonuclease (dCas9) and a single guide RNA (sgRNA).
  • dCas9 dead Cas9 endonuclease
  • sgRNA single guide RNA
  • the dCas9 also known as an endonuclease deficient Cas, is a variant form of the parent Cas9, whose endonuclease activity is removed by mutating the endonuclease domains. It should be understood however that dCas9 may still possess binding activity to guide RNA and targeted DNA strands.
  • variants or fragment are meant a functional fragment or functional variant of a native Cas protein, or a protein that shares at least 30%, between 30% and 35%, at least 35%, between 35% and 40%, at least 40%, between 40% and 45%, at least 45%, between 45% and 50%, at least 50%, 50%, between 50% and 55%, at least 55%, between 55% and 60%, at least 60%, between 60% and 65%, at least 65%, between 65% and 70%, at least 70%, between 70% and 75%, at least 75%, between 75% and 80%, at least 80%, between 80% and 85%, at least 85%, between 85% and 90%, at least 90%, between 90% and 95%, at least 95%, between 95% and 96%, at least 96%, between 96% and 97%, at least 97%, between 97% and 98%, at least 98%, between 98% and 99%, or at least 99% sequence identity to a parent Ca
  • single guide RNA and “sgRNA” are used interchangeably herein and relate to a synthetic fusion of two RNA molecules, a crRNA (CRISPR RNA) comprising a variable targeting domain (linked to a tracr mate sequence that hybridizes to a tracrRNA), fused to a tracrRNA (trans-activating CRISPR RNA).
  • CRISPR RNA crRNA
  • variable targeting domain linked to a tracr mate sequence that hybridizes to a tracrRNA
  • trans-activating CRISPR RNA trans-activating CRISPR RNA
  • the single guide RNA can comprise a crRNA or crRNA fragment and a tracrRNA or tracrRNA fragment of the CRISPR/Cas system that can form a complex with a Cas endonuclease, wherein said guide RNA/Cas endonuclease complex can direct the Cas endonuclease to a DNA target site, enabling the Cas endonuclease to recognize, optionally bind to, and optionally nick or cleave (introduce a single or double-strand break) the DNA target site.
  • the construct can be introduced and/or integrated into the cell by techniques commonly known in the art, including, but not limited to, the method of transformation.
  • Transformation of a cellular organism with DNA means introducing DNA into an organism so that at least a portion of the DNA is replicable, either as an extrachromosomal element or by chromosomal integration.
  • the term “transformed” refers to a cell in which DNA was introduced.
  • the cell is termed "host cell,” and it may be either prokaryotic or eukaryotic. Typical prokaryotic host cells include various strains of E. coli. Typical eukaryotic host cells are mammalian, such as gastrointestinal cells of human origin.
  • the introduced DNA sequence may be from the same species as the host cell or a different species from the host cell, or it may be a hybrid DNA sequence containing some foreign and some homologous DNA.
  • the present disclosure also provides methods of using nucleic acid constructs and/or cell compositions to modify and/or monitor a GI microbiome.
  • a method of modifying a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of a cell comprising a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains, a second group of one or more regulatory core domains formed of a cellobiose-responsive anti-repressor, one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains, and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the cellobiose-responsive anti-repressor comprises cellobiose- responsive anti-repressor E A1 , E A2 , E A3 , or a variant thereof.
  • the first group of the regulatory core domains, the second group of the regulatory core domains, and a third group of one or more regulatory core domains are each linked to one of the DNA binding domains formed of a cellobiose- responsive anti-repressor, to form a three-input transcription program.
  • the first group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the second group of one or more regulatory core domains comprises at least one repressor, at least one anti-repressor, or a combination thereof.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises a second repressor.
  • the first group of one or more regulatory core domains comprises a first repressor
  • the second group of one or more regulatory core domains comprises an anti-repressor
  • the first group of one or more regulatory core domains comprises at first anti-repressor
  • the second group of one or more regulatory core domains comprises a second anti-repressor
  • the method of modifying a gastrointestinal tract microbiome in a subject comprises administering to the subject an effective amount of the cell of any preceding aspect, wherein the cell comprises the construct of any preceding aspect.
  • modifying a gastrointestinal tract microbiome refers to transcriptionally increasing or decreasing functions, cell numbers, or combinations thereof in a host organism, such as humans, to promote or revert the host GI tract to a normal functioning state.
  • the method of modifying a GI tract microbiome also refers to transcriptionally increasing or decreasing functions, cell numbers, gene expression, or combinations thereof in a host organism to facilitate the understanding of disease pathogeneses associated with the GI tract and further understanding bacterial populations within the GI tract microbiome.
  • a method to predict transcriptional programming of gene expression, the method comprising: providing a gene model having network repressors and network anti-repressors, wherein the network repressors and network anti-repressors are designated for binding to a plurality of candidate DNA binding function (e.g., YQR
  • a plurality of candidate DNA binding function e.g.
  • a method comprising: providing a gene model having network repressors and network anti-repressors, wherein the network repressors and network anti-repressors are designated for binding to a plurality of candidate DNA binding functions (e.g., YQR
  • a plurality of candidate DNA binding functions e.g., YQR
  • the method further includes applying one or more of the plurality of candidate DNA binding functions as a second 2-node single-in single-out (SISO) networks of transcription factors binned via the alternate DNA binding function following the application of the one or more of the plurality of candidate DNA binding function as the first 2-node SISO networks to provide a combination of the first 2-node SISO networks and the second 2-node SISO networks, wherein the combination of the first 2-node SISO networks and the second 2-node SISO networks is equivalent to a multiple input single output (MISO) network.
  • SISO 2-node single-in single-out
  • the outputted predicted value is for the combination.
  • the plurality of candidate DNA binding function is applied via two or more of: a 1 -INPUT BUFFER logic operation (e.g., single repressor), a 1 -INPUT NOT logic gate operation (e.g., having a single anti-repressor), a 2-INPUT AND logic gate operation (e.g., having a repressor pair), a 2-INPUT NOR logic gate operation (e.g., having an antirepressor pair), a 2-INPUT A NIMPLY B logic gate operation (e.g., having a repressor/anti-repressor pair), a 2-INPUT B NIMPLY A logic gate operation (e.g., having an anti-repressor/repressor pair), a 2-INPUT XNOR logic gate operation, and a 2-INPUT NAND logic gate operation.
  • a 1 -INPUT BUFFER logic operation e.g., single repressor
  • the 2-INPUT AND logic gate operation comprises: wherein ⁇ is a value for fluorescence in the absence of inducer, ⁇ is a value constant for a maximum fluorescence relative to basal expression of an OFF-state, A+(I) is a coarse-grained Hill function (e.g., having a value of 0 or 1), and A A (I) is a coarse-grained antithetical Hill-function for anti- repression (e.g., where 0 INPUT corresponds to the ON-state).
  • the 2-INPUT NOR logic gate operation comprises:
  • the 2-INPUT A NIMPLY B logic gate operation comprises:
  • the 2-INPUT B NIMPLY A logic gate operation comprises:
  • the gene model comprises a lactose repressor (LacI) topology having (i) a regulatory core domain (RCD) and (ii) a DNA binding domain (DBD).
  • LacI lactose repressor
  • each 2-node SISO network comprises (i) a single transcription factor expressed on the pLacI plasmid (Novagen) (e.g., having pl 5a origin (copy number 20-30/cell)) and (ii) a super folder fluorescent protein (GFP) reporter (e.g., expressed on the pZS*22-sfGFP plasmid containing a pSClOl origin).
  • pLacI plasmid Novagen
  • GFP super folder fluorescent protein
  • the REU is a measure of fluorescence of a reporter fusion to a nucleic acid sequence (e.g., first 25 AA) of a gene of interest.
  • a method of monitoring a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of a cell comprising a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains, a second group of one or more regulatory core domains, one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains, and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains.
  • the method of monitoring a gastrointestinal tract microbiome in a subject comprising administering to the subject an effective amount of the cell of any preceding aspect, wherein the cell comprises the construct of any preceding aspect.
  • monitoring a gastrointestinal tract microbiome refers to the processes of observing and/or routinely checking the increases or decreases in functions, cell numbers, or combinations thereof caused by transcriptionally (re)programming a host microbiome. It should be understood that the process of monitoring can be performed as often or as sparingly necessary to observe a desired effect.
  • the host can be monitored every day, every 2 days, every 3 days, every 4 days, every 5 days, every 6 days, every 7 days, or more.
  • the host can be monitored every week, every 2 weeks, every 3 weeks, every 4 weeks, or more.
  • the host can be monitored every month, every 2 months, every 3 months, every 4 months, every 5 months, every 6 months, every 7 months, every 8 months, every 9 months, every 10 months, every 11 months, every 12 months, or more.
  • the host can be monitored every year, every 2 years, every 3 years, every 4 years, every 5 years, or more.
  • the host can be monitored 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
  • a method of treating or preventing a disease or disorder in a subject in need thereof comprising administering to the subject an effective amount of a cell comprising a construct comprising a plurality of nucleic acid sequences encoding a first group of one or more regulatory core domains, a second group of one or more regulatory core domains, one or more DNA binding domains, wherein the first group of the one or more regulatory core domains and the second group of the one or more regulatory core domains are each linked to one of the DNA binding domains, and one or more DNA operator elements, wherein the one or more DNA operator elements are each specifically recognized by one of the DNA binding domains, and wherein the construct transcriptionally (re)programs a bacterial population within the subject’s GI tract to improve the host’s health.
  • a method of treating or preventing a disease or disorder in a subject in need thereof comprising administering to the subject an effective amount of the cell comprising the construct of any preceding aspect, wherein the construct transcriptionally (re)programs a bacterial population within the subject’s GI tract to improve the host’s health.
  • the method (re)programs the bacterial population into a therapeutic bacteria.
  • the bacterial population comprises a Bacteroides species including, but not limited to B. thetaiotaomicron (Bt), B. fragilis (Bf), B. vulgatus (Bv), B. ovatus (Bo), or B. uniformis (Bu).
  • the disease or disorder includes, but are not limited to a cancer, a gastrointestinal disease, a congenital disease or disorder, an infectious disease, or combinations thereof.
  • the cancer includes, but is not limited to acoustic neuroma, adenocarcinoma, adrenal gland cancer, anal cancer, angiosarcoma (e.g., lymphangiosarcoma, lymphangioendotheliosarcoma, hemangiosarcoma), appendix cancer, benign monoclonal gammopathy, biliary cancer (e.g., cholangiocarcinoma), bladder cancer, breast cancer (e.g., adenocarcinoma of the breast, papillary carcinoma of the breast, mammary cancer, medullary carcinoma of the breast), bronchus cancer, carcinoid tumor, cervical cancer (e.g., cervical adenocarcinoma), choriocarcinoma, chordoma, craniopharyngioma, colorectal cancer (e.g., colon cancer, rectal cancer, colorectal adenocarcinoma), epithelial carcinoma
  • angiosarcoma
  • HCC hepatocellular cancer
  • lung cancer e.g., bronchogenic carcinoma, small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), adenocarcinoma of the lung
  • myelofibrosis MF
  • chronic idiopathic myelofibrosis osteosarcoma
  • ovarian cancer e.g., cystadenocarcinoma, ovarian embryonal carcinoma, ovarian adenocarcinoma
  • papillary adenocarcinoma pancreatic cancer (e.g., pancreatic adenocarcinoma, intraductal papillary mucinous neoplasm (IPMN), Islet cell tumors)
  • penile cancer e.g., Paget's disease of the penis and scrotum
  • pinealoma prostate cancer
  • prostate adenocarcinoma rectal cancer
  • rhabdomyosarcoma salivary gland cancer
  • skin cancer e.g., squamous cell carcinoma (SCC), keratoacanthoma (KA), melanoma, basal cell carcinoma (BCC)
  • small bowel cancer e.g.,
  • the gastrointestinal disease includes, but is not limited to heartburn, irritable bowel syndrome, lactose intolerance, gallstones, cholecystitis, cholangitis, anal fissure, hemorrhoids, proctitis, colon polyps, infective colitis, ulcerative colitis, ischemic colitis, Crohn’s disease, radiation colitis, celiac disease, diarrhea (chronic or acute), constipation (chronic or acute), diverticulosis, diverticulitis, acid reflux (gastroesophageal reflux (GER) or gastroesophageal reflux disease (GERD)), Hirschsprung disease, abdominal adhesions, achalasia, acute hepatic porphyria (AHP), anal fistulas, bowel incontinence, centrally mediated abdominal pain syndrome (CAPS), clostridioides difficile infection, cyclic vomiting syndrome (CVS), dyspepsia, eosin
  • the congenital disease or disorder includes, but is not limited to amniotic band syndrome, Angelman syndrome, Barth syndrome, chromosomal abnormalities (including, but not limited to abnormalities to chromosome 9, 10, 16, 18, 20, 21, 22, X chromosome, and Y chromosome), congenital adrenal hyperplasia, congenital hyperinsulinism, congenital sucrase-isomaltase deficiency (CSID), cystic fibrosis, De Lange syndrome, fetal alcohol syndrome, first arch syndrome, gestational diabetes, Haemophilia, heterochromia, Jacobsen syndrome, Katz syndrome, Klinefelter syndrome, Kabuki syndrome, Kyphosis, Larsen syndrome, Laurence-Moon syndrome, macrocephaly, Marfan syndrome, microcephaly, Nager’s syndrome, neonatal jaundice, neurofibromatosis, Noonan syndrome, Pallister-Killian syndrome, Pierre Robin syndrome, Poland syndrome, Prader-Willi syndrome,
  • the infectious disease includes, but is not limited to common cold, influenza (including, but not limited to human, bovine, avian, porcine, and simian strains of influenza), measles, acquired immune deficiency syndrome/human immunodeficiency virus (AIDS/HIV), anthrax, botulism, cholera, Campylobacter infections, chickenpox, chlamydia infections, cryptosporidosis, dengue fever, diphtheria, hemorrhagic fevers, Escherichia coli (E.
  • influenza including, but not limited to human, bovine, avian, porcine, and simian strains of influenza
  • measles including, but not limited to human, bovine, avian, porcine, and simian strains of influenza
  • AIDS/HIV acquired immune deficiency syndrome/human immunodeficiency virus
  • anthrax botulism
  • cholera Campylobacter infections
  • chickenpox chickenpox
  • the cell of any preceding aspect or the construct of any preceding aspect is administered in combination with a therapeutic agent.
  • the therapeutic agent includes, but is not limited to an antibiotic, a probiotic, an anti-inflammatory compound, a vitamin, a mineral, or combinations thereof.
  • the antibiotic includes, but is not limited to penicillins (including, but not limited to amoxicillin, clavulanate and amoxicillin, ampicillin, dicloxacillin, oxacillin, and penicillin V potassium), tetracyclines (including, but not limited to demeclocycline, doxycycline, eravacycline, minocycline, omadacycline, sarecycline, and tetracycline), cephalosporins (cefaclor, cefadroxil, cefdinir, cephalexin, cefprozil, cefepime, cefiderocol, cefotaxime, cefotetan, ceftaroline, cefazidme, ceftriaxone, and cefuroxime), quinolones (also referred to as fluoroquinolones include, but are not limited to ciprofloxacin, delafloxacin,
  • the probiotic comprises a food or supplement comprising a beneficial bacterial species including, but not limited to Bifidobacteria animalis, Bifidobacteria breve, Bifidobacteria bifidum, Bifidobacteria lactis, Bifidobacteria longum, Lactobcillus acidophilus, Lactobacillus reuteri, Lacticaseibacillus rhamnosus, Lacticaseibacillus casei, Lactiplantibacillus plantarum, Ligilactobacillus salivarius, Limosilactobacillus fermentum, Lactobacillus paracasei, Lactobacillus gasseri, Lactobacillus acidophilus, Saccharomyces boulardii, Limosilactobacillus reuteri, Bacillus coagulans, or Streptococcus thermophilus alone or in combination.
  • a beneficial bacterial species including, but not limited to Bifid
  • the anti-inflammatory compound includes, but is not limited to, a non-steroidal anti-inflammatory compound including, but is not limited to, aspirin, ibuprofen, ketoprofen, naproxen, steroids, glucocorticoids (including, but not limited to betamethasone, budesonide, dexamethasone, hydrocortisone, hydrocortisone acetate, methylprednisolone, prednisolone, prednisone, and triamcinolone), methotrexate, sulfasalazine, lefunomide, anti-Tumor Necrosis Factor (TNF) medications, cyclophosphamide, and mycophenolate used alone or in combination.
  • a non-steroidal anti-inflammatory compound including, but is not limited to, aspirin, ibuprofen, ketoprofen, naproxen, steroids, glucocorticoids (including, but not limited to betamethasone, budesonide,
  • the vitamin or mineral includes, but is not limited to, vitamin D, magnesium, vitamin K, vitamin A, riboflavin, vitamin B 12, thiamine, zinc, vitamin B6, biotin, vitamin C, folic acid, vitamin B3, calcium, iron, or derivatives thereof, given alone or in combination.
  • the cell of any preceding aspect or the construct of any preceding aspect is administered in combination with a lifestyle change including, but not limited to, dietary changes, exercise, physical therapy, or combinations thereof.
  • nucleic acid construct or cell of any preceding aspect and a pharmaceutically acceptable carrier selected from an excipient, a diluent, a salt, a buffer, a stabilizer, a lipid, an emulsion, and a nanoparticle.
  • a pharmaceutically acceptable carrier selected from an excipient, a diluent, a salt, a buffer, a stabilizer, a lipid, an emulsion, and a nanoparticle.
  • One or more active agents e.g., the nucleic acid construct
  • Salts, esters, amides, prodrugs, and other derivatives of the active agents can be prepared using standards procedures known to those skilled in the art of synthetic organic chemistry and described, for example, by March ( ⁇ 992 Advanced Organic Chemistry; Reactions, Mechanisms, and Structure, 4 th Ed. N.Y. Wiley-Interscience.
  • the cell comprising the construct or the native construct may be administered in such amounts, time, and route deemed necessary in order to achieve the desired result.
  • the exact amount of the cell comprising the construct or the native construct will vary from subject to subject, depending on the species, age, and general condition of the subject, the severity of the disease or disorder, the particular composition, its mode of administration, its mode of activity, and the like.
  • the cell comprising the construct or the native construct is preferably formulated in dosage unit form for ease of administration and uniformity of dosage. It will be understood, however, that the total daily usage of the cell comprising the construct or the native construct will be decided by the attending physician within the scope of sound medical judgment.
  • the specific therapeutically effective dose level for any particular subject will depend upon a variety of factors including the disease or disorder being treated and the severity of the disease or disorder; the activity of the cell comprising the construct or the native construct employed; the specific cell comprising the construct or the native construct employed; the age, body weight, general health, sex and diet of the patient; the time of administration, route of administration, and rate of excretion of the specific cell comprising the construct or the native construct employed; the duration of the treatment; drugs used in combination or coincidental with the specific cell comprising the construct or the native construct employed; and like factors well known in the medical arts.
  • the cell comprising the construct or the native construct may be administered by any route deemed appropriate to achieve the desired effect.
  • the cell comprising the construct or the native construct is administered via a variety of routes, including oral, intravenous, intramuscular, intra-arterial, intramedullary, intrathecal, subcutaneous, intraventricular, transdermal, intradermal, rectal, intravaginal, intraperitoneal, mucosal, nasal, buccal, enteral, sublingual; by intratracheal instillation, or bronchial instillation.
  • the most appropriate route of administration will depend upon a variety of factors, including the nature of the cell comprising the construct or the native construct (e.g., its stability in the environment of the gastrointestinal tract), the condition of the subject (e.g., whether the subject is able to tolerate the chosen route of administration), etc.
  • the exact amount of the cell comprising the construct or the native construct required to achieve a therapeutically or prophylactically effective amount will vary from subject to subject, depending on species, age, and general condition of a subject, severity of the side effects, identity of the particular compound(s), mode of administration, and the like.
  • the amount to be administered to, for example, a child or an adolescent can be determined by a medical practitioner or person skilled in the art and can be lower or the same as that administered to an adult.
  • T-Pro Transcriptional programming (T-Pro) using modular, synthetic TFs has proven to be a powerful and complementary approach for circuit design, particularly due to a reduction in design complexity achieved through circuit compression [I F], [16’], [17’].
  • T- Pro a scalable genetic circuit platform referred to as T- Pro [11’], [17’], [18’].
  • T- Pro a suite of over 100 transcriptional regulators that can be networked using synthetic promoters.
  • DBDs modular DNA-binding domains
  • engineered anti-repressors single-protein NOT gates
  • the present disclosure significantly expands the T-Pro technology through the development of a quantitatively predictive method for designing compressed genetic circuits.
  • a cellobiose-responsive anti-repressor has been engineered to enable the design of orthogonal 3-input transcriptional programs with previously developed IPTG- and D-ribose- responsive TFs.
  • REU relative expression unit
  • Example #1 Transcriptional programming with engineered cellobiose anti- repressors
  • Figs, la - le shows the engineering of a cellobiose anti -repressor to expand the transcriptional programming design space.
  • Fig. la shows an example of circuit compression.
  • Fig. lb shows the engineering of a corresponding anti-celR.
  • Fig. 1c shows the performance of variants with lacRBS regulating GFP.
  • Fig. Id shows the global design space for transcriptional programming.
  • Fig. le shows the constrained design space based on orthogonality.
  • the present disclosure attempted to directly evolve an anti-repressor using error- prone polymerase chain reaction (EP-PCR) on CCIRTAN. After generating a library with ⁇ 10 8 variants, the desired phenotype after screening with FACS was not identifiable.
  • EP-PCR error- prone polymerase chain reaction
  • FIG. 1 shows a unique anti-repressors ( E A1 , E A2 , E A3 , provided as SEQ ID NO: 1, SEQ ID NO: 3, SEQ ID NO: 5), highlighting the importance of the initial super-repressor mutation in evolving the anti-repressor phenotype (Figs. Ib-lc).
  • the anti-CelRs were validated to be equiped with four additional DNA-binding domains, DBDs, and maintain the anti-repressor phenotype (Fig. 2).
  • Fig. 2 shows validation of anti-CelRs (shown as Anti-CelR 1, 2, 3) equipped with alternate DBDs.
  • the transcriptional programming design space was expanded to include five repressor/anti-repressor pairs that can be equipped with seven unique DBDs (Fig. Id).
  • a constrained design space was developed based on orthogonality of ligand inputs and DBDs (Fig. le).
  • Genetic context dictates the non-modularity of circuit components. Synthetic genetic circuits can be modeled from first principles by accounting for the theoretical interactions between protein, DNA, and RNA components involved in the circuit. However, the absolute quantification of these macromolecules in living cells is difficult to achieve, requiring specialized techniques that are typically low throughput and costly to implement.
  • Transcriptional circuits can thus be modeled as the regulation of RNA polymerase (RNAP) flux from promoter inputs and promoter outputs, typically achieved through protein-based regulators of transcription e.g., TFs and CRISPRi), and accounting for the interactions between regulators, nucleic acids, RNAP, and ribosomes.
  • RNAP RNA polymerase
  • RPU One major limitation of the RPU concept is the phenomenon of local genetic context influencing the behavior of a circuit “part” (including, but not limited to, promoters, ribozymes, RBSs, genes, and terminators).
  • an RBS may be considered “strong” when paired with a particular promoter and gene of interest (GOI), but the same RBS may appear to be “weak” if the promoter or GOI sequence changes, potentially due to alternative folding of the mRNA or the introduction of unintended promoters, for example.
  • GOI promoter and gene of interest
  • the use of hammerhead ribozyme insulators[4’] has become common practice as they are intended to “normalize” mRNA structures by removing extraneous 5’ UTR sequences after transcription.
  • sequence-distinct ribozymes that have the same biophysical function can impact the actual expression level of a GOI from a given promoter owing to the local DNA (and resulting mRNA) sequences that arise during the composition of the expression cassette.
  • each promoter has one of five synthetic operators (O 1 , tta, gta, ttg, or agg) inserted between the -35 and -10 hexamers (core position) and an identical operator inserted downstream of the transcription start site (TSS, proximal position).
  • Figa. 3a - 3d shows modeling of genetic context is critical for the prediction of gene expression.
  • Fig. 3a shows GFP reporter inaccurately predicts expression level via RPU “promoter strength” not consistent when RBS and 5’ region of gene vary.
  • Fig. 3b shows the leader sequence of gene is most important for expression level; REU concept generalizes to different FPs.
  • Fig. 3c provides a demonstration of the non-modularity of RBS even with fixed promoter, ribozyme, and gene.
  • Fig. 3d provides an example of REU nanoluc.
  • Fig. 3e shows the standard curve.
  • Fig. 3f shows that the REU correlates with real protein expression levels.
  • Fig. 3g shows the REU lac circuit.
  • FIG. 3h shows the standard curve.
  • Fig. 3i shows that the REUlac and REU nano correlate from the same promoter over different transcript levels, bringing attention to SI fig comparing 5 promoters with constitutive LG vs. NG expression (the ratio is not conserved).
  • an REU standard curve was generated for Nanoluc [27’] luciferase using a synthetic LuxR-activated promoter (pLux m ) that produces a graded response to 3OC6 homoserine lactone (AHL) (Fig. 3d-3e).
  • the Nanoluc-GFP fusion reporter was replaced with the full Nanoluc gene and measured the catalytic activity via a luminescence assay.
  • Equation 1 ⁇ is a basal expression level, y is the span of the Hill function, x is the AHL concentration, x 50 is the EC50 AHL value, and n is the Hill slope.
  • Figs. 4a-41 show validation of the accuracy of fundamental gene circuit predictions.
  • the REU metric was applied to the predictive design of transcriptional programs by systematically characterizing the regulatory performance of 30 TFs (Fig. le) over a wide range of expression levels.
  • Transcriptional programming requires precise control over the expression levels of TFs from both constitutive and regulated promoters, as underexpression leads to weak transcriptional regulation, while overexpression can limit the dynamic range of a given TF.
  • a titration circuit was developed that uses the pLux m promoter to vary the expression level of a TF that, in turn, regulates its cognate synthetic promoter (Fig. 4a).
  • the LacI REU standard curve allows the conversion of the AHL dose to an input REU value (Fig. 3g-3h).
  • the circuit was characterized with and without the cognate inducer of the TF to generate Hill functions (Equations 2-3) describing the performance of the regulator (Fig. 4b).
  • a performance metric (Equation 4) was then calculated to determine the input REU level that yields optimal TF regulation (Fig. 4c). Equations 2 and 3 are the Hill functions for the TF titration circuit.
  • Equation 3 [0219]
  • REU 0N/0FF is the relative expression units of the GFP reporter.
  • the ON/OFF subscript denotes the ON or OFF state of the GFP reporters based on +/- inducter for anti-repressors.
  • REU lux 50 is the EC50 REUiux value. All 30 TFs were characterized using the titration circuit, revealing unique inducibility patterns for the different regulators (Fig. 4b, also see Fig 9). Fig. 9 shows TF titration curves.
  • Example #2 Multi-input transcriptional programs
  • Figs. 8a and 8b show the predictive design of complex circuits.
  • Fig. 8a shows an example workflow for predictive circuit design.
  • Fig. 8b shows example 3 input XNOR gate.
  • the program can be employed for 2 input gates, 3 input gates, 4 input gates, 5 input gates, among others.
  • Fig. 8a A user first provides a truth table mapping inducer input states to output gene expression states.
  • a generic circuit topology can then be assigned based on transcriptional programming rules (*expand in Supplement, hopefully with lookup table generated by ISYE lab). Multiple iterations of the circuit can then be simulated in silico by assigning different combinations of promoters, RBSs, and DBDs to the different regulators.
  • a circuit score can be calculated as in Cello to determine the optimal circuit architecture.
  • the model first predicts the outputs of promoters regulated by constitutively expressed TFs as described above. These output REU values are then linearly scaled based on RBS libraries characterized for each inducible promoter (Fig. 3c and Fig. 6). Linear scaling of REU is predicated on the assumption that transcription rates do not change when different RBSs or GOIs are combined with a promoter-ribozyme pair (see Equations 5-10 for a derivation of this assumption).
  • Modeling Gene Expression can be expressed as:
  • Equation 5 [mi is the concentration of mRNA I, ⁇ m i is the transcription initiation rate, [Gi is the concentration of gene, and Yi is the degradation rate of mRNA i.
  • Equation 6 [Pi is the concentration of protein i, ⁇ P i is the translation initiation rate, is the dilution rate of protein I, and t is the doubling time of strain. [0229] In Equation 10, J i is the lumped parameter for promoter flux.
  • the model assumptions include (i) the promoter flux corresponds to production of full-length transcripts, (ii) an average mRNA degradation rate can be assumed (0.00407 1/s) from literature, (iii) the transcription initiation rate not being influenced by changing the RBS sequence or GOI, and is constant as long as a promoter-ribozyme combination is maintained, and (iv) the protein dilution rate being dominated by cell division. This allows protein expression levels to be linearly scaled from a promoter by changing translation initiation rate.
  • Equation 11 For SEPA Regulation: [0232] In Equation 11, subscript “IC” indicates a particular induction condition (i.e., inducer combination). Subscript “TF n ” indicates the REU corresponding to a particular TF regulating a promoter, with “n+1” corresponding to a unique TF co-regulating the same promoter. Simply, for a SEPA regulation scheme, the REU level of an induction condition will equal the minimum of the set of REUs of that promoter when regulated by the single TFs.
  • Figs. 12a - 12i show expression of operons can be modeled for metabolic and strain engineering.
  • Table 1 shows the operons of Fig. 12a.
  • a useful application of T-Pro is the control of metabolic pathways for biomanufacturing.
  • the lycopene biosynthetic pathway is provided as an example to demonstrate how the REU metric can be used to inform the design of a multistep synthesis.
  • the production of lycopene in E. coli can be achieved by expressing three heterologous genes (crtE, crtB, and crtl) 28 (Fig. 12a), which are often organized into an operon [29’]-[31
  • overexpression of the native E. coli proteins Dxs and Idi has been shown to improve lycopene titer by increasing precursor concentrations [30’], [32’], [33’].
  • a synthetic crtEBI operon [34’] was initially placed under the control of R + YQR but experienced issues with toxicity during cloning and assaying of the circuit.
  • Fig. 14 shows a comparison of DNA sequence-based models of expression
  • E. coli strains used were NEB® 10-beta (for cloning) and 3.320 (JacZ13(Oc) lacI22 ⁇ - el4- relAl spoTl thiE1, Yale CGSC #5237) (for assays).
  • NEB® 10-beta for cloning
  • 3.320 JacZ13(Oc) lacI22 ⁇ - el4- relAl spoTl thiE1, Yale CGSC #5237
  • coli were routinely cultured aerobically in LB Miller medium (Fisher BP9723) at 37°C (unless otherwise specified), in M9 minimal medium (MM) (MM contains 3 g/L KH2PO4, 0.5 g/L NaCl, 6.78 g/L Na 2 HPO 4 , 1 g/L NH 4 C1, 0.1 mM CaCh, 2 mM MgSO 4 , 1 mM thiamine hydrochloride, 0.4% D-glucose, and 0.2% casamino acids), or on LB Miller agar (Fisher BP1425).
  • M9 minimal medium M9 minimal medium (MM) (MM contains 3 g/L KH2PO4, 0.5 g/L NaCl, 6.78 g/L Na 2 HPO 4 , 1 g/L NH 4 C1, 0.1 mM CaCh, 2 mM MgSO 4 , 1 mM thiamine hydrochloride, 0.4% D-glucose, and 0.2% casamino
  • Antibiotics for plasmid selection were used at the following concentrations: carbenicillin (Goldbio C-103-25)- 100 pg/ml; chloramphenicol (Goldbio C-105-25)- 25 pg/ml; kanamycin (Goldbio K-120-25)- 35 pg/ml.
  • inducers The following chemicals were used as inducers: Isopropyl-beta- D-thiogalactoside (IPTG, Goldbio 12481C); D-ribose (D-rib, Alfa Aesar Al 7894); Cellobiose (cello, Acros Organics 108461000); 3-Oxohexanoyl-homoserine lactone (AHL, Sigma K3007). Unless otherwise specified, the final concentrations used for each inducer were: 10 mM IPTG; 10 mM D-rib; 10 mM cello; 0.1 nM-10 ⁇ M 3OC6 AHL.
  • the operating constraints for said biotic programs are predicated on digitizing the INPUT to 0 or 1, where an INPUT 1 is achieved via the maintenance of saturating concentrations of the cognate inducer molecule - typically 10 mM. Digitizing the INPUT facilitates a constant level of OUTPUT - e.g., the amount of green fluorescent protein (GFP) is present at a steady state.
  • GFP green fluorescent protein
  • Figs. 15a -15b show modular components used in a design space and method thereof.
  • the fundamental 1 -INPUT logical operations in transcriptional programming are: i) BUFFER gates regulated via engineered repressors (Fig. 15a), and ii) NOT gates regulated via engineered anti -repressors (Fig. 15b).
  • antirepressors are an important and unique feature of transcriptional programming in that said transcription factors enable circuit compression. That is, the anti-repressor eliminates the need for the inversion of a repressor function to achieve the said logical operation. Anti-repression versus Inversion is provided below.
  • FIG. 15f Another important feature of transcriptional programing is the ability to direct two or more engineered transcription factors to a single DNA operator element - enabling the systematic construction of 2-INPUT logical operations, see Fig. 15f, 15g.
  • the design workflow for the 2-input operation is provided below..
  • Anti-repression versus Inversion Inversion is a process in which a single repressor is expressed on one layer and is directed to interact with a cognate DNA element to reject an output located on a second layer, and can be regarded as a NOT operation - notably, Cello circuits are constructed via the said inversion process 1.
  • anti-repressors reduce the NOT operation to a single layer and single promoter, and the reduction in components (e.g., promoters) is defined as circuit compression (Fig. 15b and Fig. 26).
  • the engineered transcription factors used in transcriptional programming were developed via modular design (Fig. 15c).
  • the engineered transcription factors via modular design are provided below.
  • the design template is based on the lactose repressor (LacI) topology, which can be decomposed into two functional regions: i) a regulatory core domain (RCD), and ii) a DNA binding domain (DBD).
  • the design template LacI may be a part of a large family of proteins that share a topology and putative mechanism of action 2.
  • the LacI/GalR protein family is made up of over 1,000 homologues.
  • the LacI/GalR transcription regulatory proteins mediate responses to a wide range of environmental and metabolic changes.
  • the general LacI/GalR topology can be defined by two fundamental domains - i.e., (i) a regulatory core domain and (ii) a DNA binding domain. Accordingly, we can regard this collection of paralogues as a putative design space - when carefully decomposed - positing that said functional domains can be mixed and matched to form new allosteric transcription factors.
  • LacI belongs to a large family of homologous transcription factors with similar topology that can process different INPUT ligands and bind to different DNA operators [31], [32], a putative design space can be gleaned. Accordingly, several groups have demonstrated that functional chimera can be constructed based on said engineering principles [29], [33]-[37],
  • Transcriptional Programming Transcriptional programming is predicated on a definitive bottom-up combinational rule set.
  • Single-input single-output operations (BUFFER and NOT) represent the fundamental binaries, that can be systematically combined to create all proper two-input single-output operations.
  • Complex circuit development via transcriptional programming e.g., OR, NAND, A IMPLY B, B IMPLY A, XOR, and XNOR) involve feeding forward information [3] may be performed.
  • the study systematically designed, built, and tested a large collection of BUFFER SISO and NOT SISO with corresponding metrology for said fundamental logical operations.
  • the study leveraged the standardized SISO data to design, build, and test the corresponding set of MISO logical operations (via transcriptional programming) allowed at a single operator-promoter position - that is, forming AND, NOR, A NIMPLY B, and B NIMPLY A operations.
  • Figs. 16a - 16b show example combinatorial set of SE-PA AND gates conducted in the study.
  • Each SISO system comprised (1) a single transcription factor expressed on the pLacI plasmid (Novagen), which contained the pl5a origin (copy number 20-30/cell), and (2) a super folder green fluorescent protein (GFP) reporter expressed on the pZS*22-sfGFP plasmid which contains the pSClOl origin (copy number 3-5/cell). Chloramphenicol and kanamycin resistance genes were used as selection markers for transcription factor and reporter plasmids, respectively.
  • Transcription factor and reporter plasmids were taken from previous works (Rondon et al., Groseclose et al.) and when necessary, ADR or operator variants were cloned using site-directed mutagenesis PCR (Phusion DNA Polymerase, NEB) with custom primers (Eurofins Genomics) followed by kinase, ligase and Dpnl reactions (KLD enzyme mix, NEB).
  • site-directed mutagenesis PCR Phusion DNA Polymerase, NEB
  • custom primers Eurofins Genomics
  • KLD enzyme mix NEB
  • the reactions were transformed into chemically competent DH5a cells (huA2 A(argF-lacZ)U169 phoA glnV44 cp80A(lacZ)M15 gyrA96 recAl relAl endAl thi-1 hsdR17; New England Biolabs) and plated on LB agar with appropriate antibiotic.
  • a transformant was cultured overnight and mini- prepped (Omega Bio-Tek) to yield each plasmid, and the sequence was confirmed with DNA sequencing (Eurofins Genomics).
  • a LacSTOP control plasmid which contained a LacI gene with ochre mutations at codons 2 and 3, was also cloned using this site-directed mutagenesis protocol.
  • the transcription factor plasmid contains either a single repressor (BUFFER), single anti-repressor (NOT), repressor pair (AND), antirepressor pair (NOR), or repressor/anti-repressor pair (NIMPLY). Transcription factor and corresponding reporter plasmids were double transformed into homemade chemically competent 3.32 E.
  • coli cells (Genotype lacZ13(Oc), lacI22, LAM-, el4-, relAl, spoTl, and thiEl, Yale CGSC #5237) and transformants were precultured for 6 hours in LB media with chloramphenicol (25 pg/mL, VWR Life Sciences) and kanamycin (35 pg/mL, VWR Life Sciences) antibiotics.
  • Precultures were then diluted in sextuplicate into glucose (100 mM, Fisher Scientific) M9 minimal media supplemented with 0.2% (w/v) casamino acids (VWR Life Sciences), 1 mM thiamine HC1 (Alfa Aesar), antibiotics, and respective inducers, and grown in a flat bottom 96-well microplate (Costar) for 16 hours (37 °C, 300 rpm). Microwell plates were sealed with Breathe-Easy membranes (Diversified Biotech) to prevent evaporation.
  • Inducer concentrations used are as follows: isopropyl- ⁇ -D-thiogalactoside (IPTG; 10 mM, reduced to 1 mM for IPTG-fucose gates), D-ribose (10 mM), cellobiose (10 mM), D-fucose (10 mM), fructose (10 mM), and adenine (1 mM).
  • IPTG isopropyl- ⁇ -D-thiogalactoside
  • D-ribose 10 mM
  • cellobiose 10 mM
  • D-fucose (10 mM) D-fucose (10 mM)
  • fructose (10 mM) and adenine (1 mM
  • LacSTOP control plasmid was also assayed with each reporter construct to determine the maximum expression level of each genetic architecture. Measurements were corrected by subtracting values of blank media from sample values, and fluorescence values were normalized to optical density in Microsoft Excel (Microsoft).
  • ⁇ and ⁇ values were calculated using normalized ON and OFF state OUTPUTS. Model parameters ⁇ 0, ⁇ 1, ⁇ 2 and ⁇ 3 were then evaluated using ⁇ and ⁇ values and plugged into the respective model equation for each 2-INPUT gate. Prediction values and experimental data were plotted using GraphPad (Prism) for correlation analysis. Prediction error was calculated for each INPUT condition across all gates as the ratio of measured OUTPUT to predicted OUTPUT.
  • Insulated SE-PA and SERI logic gates For each proximal AND and NOR RCD pair, the ADR variant with the largest prediction error (determined as the magnitude of fold change, averaged across all four INPUT conditions) was selected for the insulated genetic architecture case study. Insulated reporters were cloned using site-directed mutagenesis PCR as described previously using a template (O agg core O tg proximal RiboJIO GFP reporter, provided by Groseclose et al.).
  • the performance metrics of a BUFFER gate can be given by the: (i) fold induction, (ii) repression strength, (iii) and two-part traceability score - i.e., induction units (IU) and repression units (RU) - relative to a reference system (see Fig.
  • Equation 12 ⁇ is a constant representing the maximum fluorescence relative to basal expression of the OFF-state, A + (I) is a coarse-grained Hill function that can assume a value of 0 or 1, and & represents fluorescence in the absence of inducer; that is, the OFF-state (see Fig. 15a). Given that the transition region cannot maintain a setpoint, intermediate INPUT concentrations are excluded, analogous to the naive Hill model reported by Zong et al. [39] - as only the steady-state (binary) performance of interest of a given open-loop operation.
  • Equation 13 ⁇ is a constant representing the maximum fluorescence, minus ligand, relative to basal expression of the OFF-state, A A (I) is a coarse-grained antithetical Hill- function for anti-repression where 0 INPUT corresponds to the ON-state, and 1 INPUT corresponds to the OFF-state, and a represents fluorescence in the presents of inducer; that is, the OFF-state (see Fig. 15b).
  • the design space consisted of 5 nonsynonymous regulatory core domains, 8 alternate DNA binding operations, and 2 operator positions (see Fig. 15c).
  • the design space for the purported NOT operations was composed of 5 anti-RCDs (4 of which were antithetical to a given X + ), with complete overlap with respect to the given alternate DNA binding functions and cognate DNA operators.
  • 35 (-87%) resulted in objective (qualitative) BUFFER logic gating - i.e., having statistically significant differences between the ON-state (with ligand) and OFF-state (without ligand) based on a student T-test.
  • LacSTOP value 75,000 relative fluorescence units (rfu), OD600 normalized.
  • Max LacSTOP value 75,000 relative fluorescence units (rfu), OD600 normalized.
  • the study identified all BUFFER SISO logical operations with measurable dynamic ranges (i.e., statistical differences between the ON and OFF states), and in the second tier of the decision process, the study evaluated compatibility between two networked transcription factors.
  • MISO compatibility i.e., MISO compatibility
  • an objective 2-INPUT AND logic gate can be constructed - see example in Fig. 28a.
  • the OFF-state of I + YQR has a lower threshold relative to the ON-state of R + YQR - likewise for the complementary ON and OFF states. Accordingly, the two BUFFER operations can be regarded as being compatible with respect to forming a 2-INPUT, SE-PA directed AND logic gate - i.e., when directed to a cognate operator at a fixed position.
  • the pairwise (2-INPUT) network space for AND gate construction is represented by 80 operations at the PROXIMAL position and 80 operations at the CORE position.
  • the putative network space is reduced to 62 PROXIMAL AND gates (Fig. 16a) and 72 CORE AND gates (Fig. 16b) - without factoring in putative incompatibilities.
  • the putative networked space is further reduced by one - i.e., to 61 PROXIMAL AND gates and 72 CORE AND gates - resulting in a total of 133 2-INPUT logical operations that are purportedly functional (Figs. 16a and 16b).
  • Equation 13 QAND is the OUTPUT expression, is the Hill state function of repressor X + , is the Hill state function of repressor Y + , lx is the inducer state of X + (either 0 or 1), IY is the inducer state of Y + (either 0 or 1), and a0, a1, a2, and ⁇ 3 are parameters determined from the SISO gates by a set of four equations (Fig. 17a).
  • ao is the minimum OUTPUT of the gate (or overall leakiness, i.e., EX or ⁇ Y)
  • ai is the OUTPUT increase (from the baseline ao) in response to lx
  • a2 is the OUTPUT increase (from the baseline ao) in response to IY
  • OUTPUT increase from the maximum OFF state to the ON state is provided below.
  • the AND gate model predicted the quantitative performances of experimental outcomes with a high degree of accuracy - with a mean error (measured output / predicted output) of 1.256, see Figs. 18a - 18b and Fig. 29a.
  • ao is the minimum OUTPUT of the gate (or overall leakiness, i.e., sx or ⁇ Y)
  • ai is the OUTPUT increase (from the baseline ao) in response to lx
  • a2 is the OUTPUT increase (from the baseline ao) in response to IY
  • a3 is the OUTPUT increase from the maximum OFF state to the ON state.
  • the initial selection (design) criteria employed the identification of said NOT SISO operations with statistically significant differences between the ON-state (without ligand) and the OFF- state (with ligand) - i.e., adequate dynamic ranges for a set of network capable antirepressors.
  • the second hierarchical design criteria require sufficient inequality between complementary ON and OFF states (Fig. 30a).
  • Equation 14 ⁇ NOR is the OUTPUT expression
  • Ay is the Hill state function of anti- repressor X A
  • lx is the inducer state of X A (either 0 or 1)
  • IY is the inducer state of Y A (either 0 or 1)
  • a0, a1, a2, and a3 are parameters determined by the set of four equations described previously - also see Fig. 17b. Further description of the NOR logic model is provided below.
  • PROXIMAL NOR gates (Fig. 20a) had a greater degree of spread in the standard deviation per data point relative to CORE NOR gates (Fig. 20b) - which we again attributed to a variable ‘5-UTR in the PROXIMAL operations.
  • Equation 14 NOR logic model.
  • Equations for a0, a1, a2, and a3 are derived from solving Equation 14 using similar assumptions described in the AND model but with antithetical input conditions (due to the antithetical phenotype of the anti-repressors from repressors).
  • ⁇ NOR the TF with the lowest SISO OFF state OUTPUT controls
  • any network of two transcription factors with divergent phenotypes i.e., repressor and anti- repressor
  • SEPA shared DNA operator
  • the complementary logical operation B NIMPLY A can be generated, see Figs. 31a - 31d .
  • the study can model nonimplication logic gates to better interpret quantitative performances. Namely, for 2-INPUT A NIMPLY B logic gates, the study modified the model shown in Equation 13 to include one repressor and one anti-repressor state function pertaining to both BUFFER and NOT SISO logic. The model for A NIMPLY B is provided in Equation 15 A.
  • Equation 15 A ⁇ A NIMPLY B is the OUTPUT expression, is the Hill state function of repressor X + , is the Hill state function of anti-repressor Y A , lx is the inducer state of X + (either 0 or 1), IY is the inducer state of Y A (either 0 or 1), and a0, a1, a2, and a3 are parameters determined by the set of four equations described previously - also see Figs. 22a - 22b. Further description of the A NIMPLY B logic model is provided below..
  • Equation 15B ⁇ B NIMPLY A is the OUTPUT expression, s the Hill state function of anti-repressor X A , is the Hill state function of repressor Y + , lx is the inducer state of XA (either 0 or 1), IY is the inducer state of Y + (either 0 or 1), and ao, ai, a2, and as are parameters determined by the set of four equations described - also see Fig. 22b. Further description of the B NIMPLY A logic model is provided below.
  • the model accurately predicted the qualitative performance in all cases and quantitatively predicted the performance of said nonimplication gates in -86% of the tested cases.
  • Equation 15 A A NIMPLY B logic model. For 2-INPUT B NIMPLY A logic gates, the model shown in Equation 13 can be modified to include one repressor and one anti -repressor state function pertaining to both BUFFER and NOT SISO logic.
  • the model for X NIMPLY Y is provided in Equation 15 A.
  • Equations for a0, a1, a2, and a3 are derived from solving Equation 15A using similar assumptions described in the AND and NOR model but with conditions reflecting the phenotype of each TF.
  • the anti-repressor Y A controls
  • B NIMPLY A logic model.
  • the model for 2-INPUT B NIMPLY A logic gates follows that described above for A NIMPLY B gates, with the modification that TFs X and Y phenotypes are switched, so that this system contains an anti-repressor X A and repressor Y + .
  • the model is provided in Equation 15B.
  • Equations for a0, a1, a2, and a3 are derived from solving Equation 15B using similar assumptions described in the AND model but with input conditions reflecting the phenotypes of each TF.
  • the anti-repressor X A controls ⁇ x; a0
  • This SE-PA network form resulted in 7 orthogonal (binned) DNA binding networks, with inter-bin communication facilitated via the INPUT signals.
  • the description is provided in the supplemental discussion below.
  • 7 nonsynonymous ADR and 5 RCDs each with the capacity to interact with one of 5 non-synonymous INPUTS, the result was a putative network space of 70 operations (i.e., restricted to said AND gates and NOR gates), with 10 3 signal coupled operations.
  • mixed unit operations i.e., said A NIMPLY B gates, and B NIMPLY A gates
  • the network space is represented by 350 putative operations, with signal coupling three times larger than AND gate (or NOR gate) coupled operations.
  • the DNA binding network in transcriptional programming can be expanded via the SERI architecture.
  • SERI SERI genetic architecture
  • two nonsynonymous DNA operators can be paired in tandem - i.e., one located at the CORE position and the other at the PROXIMAL position (Fig. 27e).
  • the putative SERI networked DNA space results in 10 3 operations, with a signal coupling on the order of 10 5 .
  • the description is provided in the supplemental discussion below.
  • 1 ADR can be selected from the set of repressors and 1 ADR from the set of anti-repressors, and one DBD. In general, this would lead to possible nonsynonymous NIMPLY designs per position for a total of 350 possible designs. However, four of the five signals are the same for both the repressor and anti-repressor (Cellobiose and Adenine being the two different ones, respectively). This means that except for these two, when the signal is selected for the first operator, the second one can only be selected from the remaining four. This leads to (5+4x4)x8 or (5+4x4)x7 NIMPLY designs for each position, 168 or 147, respectively.
  • Computer-executable instructions such as program modules, being executed by a computer may be used.
  • program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types.
  • the controller includes at least one processing unit and memory.
  • memory may be volatile (such as random-access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two.
  • RAM random-access memory
  • ROM read-only memory
  • flash memory etc.
  • the controller of Fig. 12 may have additional features/functionality.
  • the computing device may include additional storage (removable and/or non-removable), including, but not limited to, magnetic or optical disks or tape. Such additional storage may include removable storage and/or non-removable storage.
  • FPGAs Field-programmable Gate Arrays
  • ASICs Application-specific Integrated Circuits
  • ASSPs Application-specific Standard Products
  • SOCs System-on-a-chip systems
  • CPLDs Complex Programmable Logic Devices
  • the methods and apparatus of the presently disclosed subject matter may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium where, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the presently disclosed subject matter.
  • program code i.e., instructions
  • tangible media such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium
  • Bold nucleotides/amino acids represent super-repressor mutation.

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Abstract

La présente invention concerne des constructions et des compositions cellulaires pour la reprogrammation de cellules. La présente invention concerne également des procédés faisant appel à des constructions et à des compositions cellulaires pour modifier et/ou surveiller des cellules. La présente invention concerne une programmation transcriptionnelle, et une modélisation de celle-ci, à trois entrées qui peuvent fournir jusqu'à 256 opérations logiques, par exemple, pour former une technologie de plateforme de prise de décision complète et évolutive de Turing pour bioinformatique et intelligence biologique.
PCT/US2024/014269 2023-02-02 2024-02-02 Prédiction de performance de programmes transcriptionnels fondamentaux Ceased WO2024163909A2 (fr)

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