Text S 1 : supplementary information for Combinatorial gene regulation using autoregulation

As many as 59% of the transcription factors in Escherichia coli regulate the transcription rate of their own genes. This suggests that auto-regulation has one or more important functions. Here, one possible function is studied. Often the transcription rate of an auto-regulator is also controlled by additional transcription factors. In these cases, the way the expression of the auto-regulator responds to changes in the concentrations of the “input” regulators (the response function) is obviously affected by the auto-regulation. We suggest that, conversely, auto-regulation may be used to optimize this response function. To test this hypothesis, we use an evolutionary algorithm and a chemical–physical model of transcription regulation to design model cis-regulatory constructs with predefined response functions. In these simulations, auto-regulation can evolve if this provides a functional benefit. When selecting for a series of elementary response functions—Boolean logic gates and linear responses—the cis-regulatory regions resulting from the simulations indeed often exploit auto-regulation. Surprisingly, the resulting constructs use auto-activation rather than auto-repression. Several design principles show up repeatedly in the simulation results. They demonstrate how auto-activation can be used to generate sharp, switch-like activation and repression circuits and how linearly decreasing response functions can be obtained. Auto-repression, on the other hand, resulted only when a high response speed or a suppression of intrinsic noise was also selected for. The results suggest that, while auto-repression may primarily be valuable to improve the dynamical properties of regulatory circuits, auto-activation is likely to evolve even when selection acts on the shape of response function only.

[1]  U. Alon,et al.  Negative autoregulation speeds the response times of transcription networks. , 2002, Journal of molecular biology.

[2]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[3]  P. V. von Hippel,et al.  Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. , 1987, Journal of molecular biology.

[4]  Araceli M. Huerta,et al.  From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. , 1998, BioEssays : news and reviews in molecular, cellular and developmental biology.

[5]  W. Ebeling Stochastic Processes in Physics and Chemistry , 1995 .

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

[7]  M. A. Shea,et al.  The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation. , 1985, Journal of molecular biology.

[8]  J. Collado-Vides,et al.  Control site location and transcriptional regulation in Escherichia coli , 1991, Microbiological reviews.

[9]  M. Savageau Comparison of classical and autogenous systems of regulation in inducible operons , 1974, Nature.

[10]  Julio Collado-Vides,et al.  RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12 , 2001, Nucleic Acids Res..

[11]  O. Berg,et al.  Selection of DNA binding sites by regulatory proteins. Functional specificity and pseudosite competition. , 1988, Journal of biomolecular structure & dynamics.

[12]  Peter D. Karp,et al.  EcoCyc: a comprehensive database resource for Escherichia coli , 2004, Nucleic Acids Res..

[13]  Pieter Rein ten Wolde,et al.  Regulatory Control and the Costs and Benefits of Biochemical Noise , 2007, PLoS Comput. Biol..

[14]  H. Margalit,et al.  Quantitative parameters for amino acid-base interaction: implications for prediction of protein-DNA binding sites. , 1998, Nucleic acids research.

[15]  S. Shen-Orr,et al.  Network motifs in the transcriptional regulation network of Escherichia coli , 2002, Nature Genetics.

[16]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[17]  M. Ptashne,et al.  Transcriptional activation by recruitment , 1997, Nature.

[18]  P. V. von Hippel,et al.  Selection of DNA binding sites by regulatory proteins. II. The binding specificity of cyclic AMP receptor protein to recognition sites. , 1988, Journal of molecular biology.

[19]  S. Leibler,et al.  Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments , 2005, Science.

[20]  H. Margalit,et al.  Compilation of E. coli mRNA promoter sequences. , 1993, Nucleic acids research.

[21]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Naama Barkai,et al.  Noise Propagation and Signaling Sensitivity in Biological Networks: A Role for Positive Feedback , 2007, PLoS Comput. Biol..

[23]  B. Séraphin,et al.  Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion , 2001, The EMBO journal.

[24]  Dov J. Stekel,et al.  Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression , 2008, BMC Systems Biology.

[25]  M. Levine,et al.  Computational Models for Neurogenic Gene Expression in the Drosophila Embryo , 2006, Current Biology.

[26]  Pieter Rein ten Wolde,et al.  Transcriptional Regulation by Competing Transcription Factor Modules , 2006, PLoS Comput. Biol..

[27]  G. Balázsi,et al.  Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression , 2009, Proceedings of the National Academy of Sciences.

[28]  S. Scherrer,et al.  Synergy of repression and silencing gradients along the chromosome. , 2009, Journal of molecular biology.

[29]  Nicolas E. Buchler,et al.  On schemes of combinatorial transcription logic , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  U. Alon,et al.  Diverse two-dimensional input functions control bacterial sugar genes. , 2008, Molecular cell.

[31]  T. Mizuno,et al.  Evidence for multiple OmpR-binding sites in the upstream activation sequence of the ompC promoter in Escherichia coli: a single OmpR-binding site is capable of activating the promoter , 1990, Journal of bacteriology.

[32]  K. Vahala Handbook of stochastic methods for physics, chemistry and the natural sciences , 1986, IEEE Journal of Quantum Electronics.

[33]  M. Freeman Feedback control of intercellular signalling in development , 2000, Nature.

[34]  Terence Hwa,et al.  Transcriptional regulation by the numbers: models. , 2005, Current opinion in genetics & development.

[35]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , .

[36]  S. Swain Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences , 1984 .

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