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Stochastic Analysis of the SOS Response in Escherichia coli

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  • Yishai Shimoni
  • Shoshy Altuvia
  • Hanah Margalit
  • Ofer Biham

Abstract

Background: DNA damage in Escherichia coli evokes a response mechanism called the SOS response. The genetic circuit of this mechanism includes the genes recA and lexA, which regulate each other via a mixed feedback loop involving transcriptional regulation and protein-protein interaction. Under normal conditions, recA is transcriptionally repressed by LexA, which also functions as an auto-repressor. In presence of DNA damage, RecA proteins recognize stalled replication forks and participate in the DNA repair process. Under these conditions, RecA marks LexA for fast degradation. Generally, such mixed feedback loops are known to exhibit either bi-stability or a single steady state. However, when the dynamics of the SOS system following DNA damage was recently studied in single cells, ordered peaks were observed in the promoter activity of both genes (Friedman et al., 2005, PLoS Biol. 3(7):e238). This surprising phenomenon was masked in previous studies of cell populations. Previous attempts to explain these results harnessed additional genes to the system and deployed complex deterministic mathematical models that were only partially successful in explaining the results. Methodology/Principal Findings: Here we apply stochastic methods, which are better suited for dynamic simulations of single cells. We show that a simple model, involving only the basic components of the circuit, is sufficient to explain the peaks in the promoter activities of recA and lexA. Notably, deterministic simulations of the same model do not produce peaks in the promoter activities. Conclusion/Significance: We conclude that the double negative mixed feedback loop with auto-repression accounts for the experimentally observed peaks in the promoter activities. In addition to explaining the experimental results, this result shows that including additional regulations in a mixed feedback loop may dramatically change the dynamic functionality of this regulatory module. Furthermore, our results suggests that stochastic fluctuations strongly affect the qualitative behavior of important regulatory modules even under biologically relevant conditions, thus emphasizing the importance of stochastic analysis of regulatory circuits.

Suggested Citation

  • Yishai Shimoni & Shoshy Altuvia & Hanah Margalit & Ofer Biham, 2009. "Stochastic Analysis of the SOS Response in Escherichia coli," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-7, May.
  • Handle: RePEc:plo:pone00:0005363
    DOI: 10.1371/journal.pone.0005363
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    Cited by:

    1. Yishai Shimoni & Marc Y Fink & Soon-gang Choi & Stuart C Sealfon, 2010. "Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-13, June.
    2. Yishai Shimoni & German Nudelman & Fernand Hayot & Stuart C Sealfon, 2011. "Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-9, December.
    3. Rajesh Ramaswamy & Ivo F Sbalzarini & Nélido González-Segredo, 2011. "Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.

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