Toward predictive engineering of gene circuits

• Brooks S.M.
• Alper H.S.

Applications, challenges, and needs for employing synthetic biology beyond the lab.

Nat. Commun. 2021; 12: 1390

• Lawson C.E.
• et al.

Common principles and best practices for engineering microbiomes.

Nat. Rev. Microbiol. 2019; 17: 725-741

• Keating K.W.
• Young E.M.

Synthetic biology for bio-derived structural materials.

Curr. Opin. Chem. Eng. 2019; 24: 107-114

• Nielsen A.A.K.
• et al.

Genetic circuit design automation.

Science. 2016; 352: aac7341

• Becskei A.
• Serrano L.

Engineering stability in gene networks by autoregulation.

Nature. 2000; 405: 590-593

• Riglar D.T.
• et al.

Engineered bacteria can function in the mammalian gut long-term as live diagnostics of inflammation.

Nat. Biotechnol. 2017; 35: 653-658

• Certain L.K.
• et al.

Using engineered bacteria to characterize infection dynamics and antibiotic effects in vivo.

Cell Host Microbe. 2017; 22: 263-268

• Gardner T.S.
• et al.

Construction of a genetic toggle switch in Escherichia coli.

Nature. 2000; 403: 339-342

• Elowitz M.B.
• Leibler S.

A synthetic oscillatory network of transcriptional regulators.

Nature. 2000; 403: 335-338

• Danino T.
• et al.

A synchronized quorum of genetic clocks.

Nature. 2010; 463: 326-330

• Lezia A.
• et al.

Design, mutate, screen: multiplexed creation and arrayed screening of synchronized genetic clocks.

Cell Syst. 2022; 13: 365-375

• Lu J.
• et al.

Advances and challenges in programming pattern formation using living cells.

Curr. Opin. Chem. Biol. 2022; 68102147

• Barbier I.
• et al.

Engineering synthetic spatial patterns in microbial populations and communities.

Curr. Opin. Microbiol. 2022; 67102149

• Tsoi R.
• et al.

Metabolic division of labor in microbial systems.

Proc. Natl. Acad. Sci. 2018; 115: 2526-2531

• Lindemann S.R.
• et al.

Engineering microbial consortia for controllable outputs.

ISME J. 2016; 10: 2077-2084

• Wan X.
• et al.

Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals.

Nat. Chem. Biol. 2019; 15: 540-548

• Chen Y.
• et al.

Emergent genetic oscillations in a synthetic microbial consortium.

Science. 2015; 349: 986-989

• Daeffler K.N.-M.
• et al.

Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation.

Mol. Syst. Biol. 2017; 13: 923

• Sexton J.T.
• Tabor J.J.

Multiplexing cell–cell communication.

Mol. Syst. Biol. 2020; 16e9618

• Dunkelmann D.L.
• et al.

A 68-codon genetic code to incorporate four distinct non-canonical amino acids enabled by automated orthogonal mRNA design.

Nat. Chem. 2021; 13: 1110-1117

• Tamsir A.
• et al.

Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’.

Nature. 2011; 469: 212-215

• Wang B.
• et al.

Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology.

Nat. Commun. 2011; 2: 508

• Xiang Y.
• et al.

Scaling up genetic circuit design for cellular computing: advances and prospects.

Nat. Comput. 2018; 17: 833-853

• Eling N.
• et al.

Challenges in measuring and understanding biological noise.

Nat. Rev. Genet. 2019; 20: 536-548

• Elowitz M.B.
• et al.

Stochastic gene expression in a single cell.

Science. 2002; 297: 1183-1186

• Barbier I.
• et al.

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch.

Mol. Syst. Biol. 2020; 16e9361

• Fernandez-Rodriguez J.
• et al.

Engineering RGB color vision into Escherichia coli.

Nat. Chem. Biol. 2017; 13: 706-708

• Gyorgy A.
• et al.

Isocost lines describe the cellular economy of genetic circuits.

Biophys. J. 2015; 109: 639-646

• Zhang R.
• et al.

Winner-takes-all resource competition redirects cascading cell fate transitions.

Nat. Commun. 2021; 12: 853

• Butzin N.C.
• Mather W.H.

Crosstalk between diverse synthetic protein degradation tags in Escherichia coli.

ACS Synth. Biol. 2018; 7: 54-62

• Contreras-Llano L.E.
• et al.

Holistic engineering of cell-free systems through proteome-reprogramming synthetic circuits.

Nat. Commun. 2020; 11: 3138

• Zhang R.
• et al.

Topology-dependent interference of synthetic gene circuit function by growth feedback.

Nat. Chem. Biol. 2020; 16: 695-701

• Tan C.
• et al.

Emergent bistability by a growth-modulating positive feedback circuit.

Nat. Chem. Biol. 2009; 5: 842-848

• Boo A.
• et al.

Host-aware synthetic biology.

Curr. Opin. Syst. Biol. 2019; 14: 66-72

• Ghatak S.
• et al.

The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function.

Nucleic Acids Res. 2019; 47: 2446-2454

• Shahab R.L.
• et al.

A heterogeneous microbial consortium producing short-chain fatty acids from lignocellulose.

Science. 2020; 369: eabb1214

• Lewis D.D.
• et al.

Frequency dependent growth of bacteria in living materials.

Front. Bioeng. Biotechnol. 2022; 10948483

• Shin J.
• et al.

Programming Escherichia coli to function as a digital display.

Mol. Syst. Biol. 2020; 16e9401

• Balagaddé F.K.
• et al.

A synthetic Escherichia coli predator-prey ecosystem.

Mol. Syst. Biol. 2008; 4: 187

• Endy D.

Foundations for engineering biology.

Nature. 2005; 438: 449-453

• Chan L.Y.
• et al.

Refactoring bacteriophage T7.

Mol. Syst. Biol. 2005; 1: 2005.0018

• Jayaraman P.
• et al.

Blue light-mediated transcriptional activation and repression of gene expression in bacteria.

Nucleic Acids Res. 2016; 44: 6994-7005

• Lajoie M.J.
• et al.

Genomically recoded organisms expand biological functions.

Science. 2013; 342: 357-360

• Rackham O.
• Chin J.W.

A network of orthogonal ribosome·mRNA pairs.

Nat. Chem. Biol. 2005; 1: 159-166

• Canton B.
• et al.

Refinement and standardization of synthetic biological parts and devices.

Nat. Biotechnol. 2008; 26: 787-793

• Park Y.
• et al.

Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads.

Mol. Syst. Biol. 2020; 16e9584

• Wu F.
• et al.

Modulation of microbial community dynamics by spatial partitioning.

Nat. Chem. Biol. 2022; 18: 394-402

• Lopatkin A.J.
• Collins J.J.

Predictive biology: modelling, understanding and harnessing microbial complexity.

Nat. Rev. Microbiol. 2020; 18: 507-520

• Del Vecchio D.
• et al.

Modular cell biology: retroactivity and insulation.

Mol. Syst. Biol. 2008; 4: 161

• Liao C.
• et al.

An integrative circuit–host modelling framework for predicting synthetic gene network behaviours.

Nat. Microbiol. 2017; 2: 1658-1666

• Peng W.
• et al.

Noise reduction facilitated by dosage compensation in gene networks.

Nat. Commun. 2016; 7: 12959

• Son H.-I.
• et al.

Design patterns for engineering genetic stability.

Curr. Opin. Biomed. Eng. 2021; 19100297

• Gillespie D.T.

A general method for numerically simulating the stochastic time evolution of coupled chemical reactions.

J. Comput. Phys. 1976; 22: 403-434

• Song R.
• et al.

A cell size- and cell cycle-aware stochastic model for predicting time-dynamic gene network activity in individual cells.

BMC Syst. Biol. 2015; 9: 91

• Aoki S.K.
• et al.

A universal biomolecular integral feedback controller for robust perfect adaptation.

Nature. 2019; 570: 533-537

• Wang S.
• et al.

Massive computational acceleration by using neural networks to emulate mechanism-based biological models.

Nat. Commun. 2019; 10: 4354

• Raissi M.
• et al.

Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

J. Comput. Phys. 2019; 378: 686-707

• Yazdani A.
• et al.

Systems biology informed deep learning for inferring parameters and hidden dynamics.

PLoS Comput. Biol. 2020; 16e1007575

• Tsopanoglou A.
• Jiménez del Val I.

Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses.

Curr. Opin. Chem. Eng. 2021; 32100691

• Chen J.
• et al.

High throughput flow cytometry based yeast two-hybrid array approach for large-scale analysis of protein-protein interactions.

Cytometry A. 2012; 81: 90-98

• Nolan J.P.
• Mandy F.

Multiplexed and microparticle-based analyses: quantitative tools for the large-scale analysis of biological systems.

Cytometry A. 2006; 69: 318-325

• Eason R.G.
• et al.

Characterization of synthetic DNA bar codes in Saccharomyces cerevisiae gene-deletion strains.

Proc. Natl. Acad. Sci. U. S. A. 2004; 101: 11046-11051

• Watanabe N.
• et al.

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

PLoS ONE. 2018; 13e0202049

• Johnson J.S.
• et al.

Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis.

Nat. Commun. 2019; 10: 5029

• Frieda K.L.
• et al.

Synthetic recording and in situ readout of lineage information in single cells.

Nature. 2017; 541: 107-111

• Schmidt F.
• et al.

Transcriptional recording by CRISPR spacer acquisition from RNA.

Nature. 2018; 562: 380-385

• Sheth R.U.
• et al.

Multiplex recording of cellular events over time on CRISPR biological tape.

Science. 2017; 358: 1457-1461