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

One fundamental challenge in synthetic biology is the lack of quantitative tools that accurately describe and predict the behaviours of engineered gene circuits. This challenge arises from multiple factors, among which the complex interdependence of circuits and their host is a leading cause. Here we present a gene circuit modelling framework that explicitly integrates circuit behaviours with host physiology through bidirectional circuit–host coupling. The framework consists of a coarse-grained but mechanistic description of host physiology that involves dynamic resource partitioning, multilayered circuit–host coupling including both generic and system-specific interactions, and a detailed kinetic module of exogenous circuits. We showed that, following training, the framework was able to capture and predict a large set of experimental data concerning the host and its foreign gene overexpression. To demonstrate its utility, we applied the framework to examine a growth-modulating feedback circuit whose dynamics is qualitatively altered by circuit–host interactions. Using an extended version of the framework, we further systematically revealed the behaviours of a toggle switch across scales from single-cell dynamics to population structure and to spatial ecology. This work advances our quantitative understanding of gene circuit behaviours and also benefits the rational design of synthetic gene networks.An integrative modelling framework is developed to predict the behaviours of synthetic circuits and theirimpacts on host physiology.

[1]  Tetsuya J. Kobayashi,et al.  Reconstructing the single‐cell‐level behavior of a toggle switch from population‐level measurements , 2008, FEBS letters.

[2]  J. Keasling,et al.  Microbial engineering for the production of advanced biofuels , 2012, Nature.

[3]  K. Dill,et al.  Bacterial growth laws reflect the evolutionary importance of energy efficiency , 2014, Proceedings of the National Academy of Sciences.

[4]  T. Hwa,et al.  Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.

[5]  A. G. Marr,et al.  Growth rate of Escherichia coli. , 1991, Microbiological reviews.

[6]  N. Philippe,et al.  ppGpp is the major source of growth rate control in E. coli. , 2011, Environmental microbiology.

[7]  Lingchong You,et al.  Addressing biological uncertainties in engineering gene circuits. , 2016, Integrative biology : quantitative biosciences from nano to macro.

[8]  R. Gourse,et al.  Mechanism of regulation of transcription initiation by ppGpp. I. Effects of ppGpp on transcription initiation in vivo and in vitro. , 2001, Journal of molecular biology.

[9]  K. von Meyenburg,et al.  Synthesis and turnover of basal level guanosine tetraphosphate in Escherichia coli. , 1975, The Journal of biological chemistry.

[10]  L. You,et al.  Emergent bistability by a growth-modulating positive feedback circuit. , 2009, Nature chemical biology.

[11]  U. Sauer,et al.  Dissecting specific and global transcriptional regulation of bacterial gene expression , 2013, Molecular systems biology.

[12]  P. Swain,et al.  Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.

[13]  O. Maaløe,et al.  Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium. , 1958, Journal of general microbiology.

[14]  A. Arkin,et al.  Contextualizing context for synthetic biology – identifying causes of failure of synthetic biological systems , 2012, Biotechnology journal.

[15]  John W. Foster,et al.  DksA A Critical Component of the Transcription Initiation Machinery that Potentiates the Regulation of rRNA Promoters by ppGpp and the Initiating NTP , 2004, Cell.

[16]  R. Kishony,et al.  Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions , 2009, Cell.

[17]  R. Gourse,et al.  Control of rRNA expression by small molecules is dynamic and nonredundant. , 2003, Molecular cell.

[18]  Andreas Bracher,et al.  Molecular chaperones in protein folding and proteostasis , 2011, Nature.

[19]  T. Hwa,et al.  Growth-rate-dependent partitioning of RNA polymerases in bacteria , 2008, Proceedings of the National Academy of Sciences.

[20]  Christopher A. Voigt,et al.  Principles of genetic circuit design , 2014, Nature Methods.

[21]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[22]  T. Hwa,et al.  Growth Rate-Dependent Global Effects on Gene Expression in Bacteria , 2009, Cell.

[23]  D. J. Greenwood,et al.  RNA:protein ratio of the unicellular organism as a characteristic of phosphorous and nitrogen stoichiometry and of the cellular requirement of ribosomes for protein synthesis , 2006, BMC Biology.

[24]  M. Elowitz,et al.  Build life to understand it , 2010, Nature.

[25]  James J. Collins,et al.  Next-Generation Synthetic Gene Networks , 2009, Nature Biotechnology.

[26]  S. Basu,et al.  A synthetic multicellular system for programmed pattern formation , 2005, Nature.

[27]  Ting Lu,et al.  Bacterial social interactions drive the emergence of differential spatial colony structures , 2015, BMC Systems Biology.

[28]  S. Molin,et al.  Control of Protein Synthesis in Escherichia coli: Analysis of an Energy Source Shift-Down , 1977, Journal of bacteriology.

[29]  I. Chopra,et al.  Sensitive biological detection method for tetracyclines using a tetA-lacZ fusion system , 1990, Antimicrobial Agents and Chemotherapy.

[30]  R. Gourse,et al.  Control of Ribosome Synthesis in Escherichia coli , 1986 .

[31]  Ahmad S. Khalil,et al.  Synthetic biology: applications come of age , 2010, Nature Reviews Genetics.

[32]  K. Potrykus,et al.  (p)ppGpp: still magical? , 2008, Annual review of microbiology.

[33]  R. Kwok Five hard truths for synthetic biology , 2010, Nature.

[34]  P. Dennis,et al.  Modulation of Chemical Composition and Other Parameters of the Cell at Different Exponential Growth Rates , 2008, EcoSal Plus.

[35]  David Vanderbilt,et al.  Origins and Consequences of Surface Stress , 1996 .

[36]  Christopher A. Voigt,et al.  A Synthetic Genetic Edge Detection Program , 2009, Cell.

[37]  Christopher A. Voigt,et al.  Environmental signal integration by a modular AND gate , 2007, Molecular systems biology.

[38]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[39]  Adam Paul Arkin,et al.  A wise consistency: engineering biology for conformity, reliability, predictability. , 2013, Current opinion in chemical biology.

[40]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[41]  B. Glick Metabolic load and heterologous gene expression. , 1995, Biotechnology advances.

[42]  R. Cox,et al.  Quantitative relationships for specific growth rates and macromolecular compositions of Mycobacterium tuberculosis, Streptomyces coelicolor A3(2) and Escherichia coli B/r: an integrative theoretical approach. , 2004, Microbiology.

[43]  Terence Hwa,et al.  The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria , 2013, Science.

[44]  Joel T. Smith,et al.  The global, ppGpp‐mediated stringent response to amino acid starvation in Escherichia coli , 2008, Molecular microbiology.

[45]  David H Burkhardt,et al.  Quantifying Absolute Protein Synthesis Rates Reveals Principles Underlying Allocation of Cellular Resources , 2014, Cell.

[46]  J. Collins,et al.  Synthetic Biology Moving into the Clinic , 2011, Science.

[47]  C. Kurland,et al.  Gratuitous overexpression of genes in Escherichia coli leads to growth inhibition and ribosome destruction , 1995, Journal of bacteriology.

[48]  Adam P. Arkin,et al.  Programming mRNA decay to modulate synthetic circuit resource allocation , 2016 .

[49]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[50]  Jeff Hasty,et al.  Phenotypic variability of growing cellular populations , 2007, Proceedings of the National Academy of Sciences.

[51]  A. L. Koch,et al.  In vivo assay of protein synthesizing capacity of Escherichia coli from slowly growing chemostat cultures. , 1971, Journal of molecular biology.