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A Simple Model to Control Growth Rate of Synthetic E. coli during the Exponential Phase: Model Analysis and Parameter Estimation

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7605))

Abstract

We develop and analyze a model of a minimal synthetic gene circuit, that describes part of the gene expression machinery in Escherichia coli, and enables the control of the growth rate of the cells during the exponential phase. This model is a piecewise non-linear system with two variables (the concentrations of two gene products) and an input (an inducer). We study the qualitative dynamics of the model and the bifurcation diagram with respect to the input. Moreover, an analytic expression of the growth rate during the exponential phase as function of the input is derived. A relevant problem is that of identifiability of the parameters of this expression supposing noisy measurements of exponential growth rate. We present such an identifiability study that we validate in silico with synthetic measurements.

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References

  1. Andrianantoandro, E., Basu, S., Karig, D., Weiss, R.: Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology 2(1) (2006)

    Google Scholar 

  2. Khalil, A., Collins, J.: Synthetic biology: applications come of age. Nature Reviews Genetics 11(5), 367–379 (2010)

    Article  Google Scholar 

  3. Mukherji, S., Van Oudenaarden, A.: Synthetic biology: understanding biological design from synthetic circuits. Nature Reviews Genetics 10(12), 859–871 (2009)

    Google Scholar 

  4. Elowitz, M., Leibler, S., et al.: A synthetic oscillatory network of transcriptional regulators. Nature 403(6767), 335–338 (2000)

    Article  Google Scholar 

  5. Gardner, T., Cantor, C., Collins, J.: Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000)

    Article  Google Scholar 

  6. Tigges, M., Marquez-Lago, T., Stelling, J., Fussenegger, M.: A tunable synthetic mammalian oscillator. Nature 457(7227), 309–312 (2009)

    Article  Google Scholar 

  7. Monod, J.: The growth of bacterial cultures. Annual Review of Microbiology 3(1), 371–394 (1949)

    Article  Google Scholar 

  8. Marr, A.G.: Growth rate of Escherichia coli. Microbiological Reviews 55(2), 316–333 (1991)

    Google Scholar 

  9. Kaern, M., Blake, W., Collins, J.: The engineering of gene regulatory networks. Annual Review of Biomedical Engineering 5(1), 179–206 (2003)

    Article  Google Scholar 

  10. Tan, C., Marguet, P., You, L.: Emergent bistability by a growth-modulating positive feedback circuit. Nature Chemical Biology 5(11), 842–848 (2009)

    Article  Google Scholar 

  11. Bettenbrock, K., Sauter, T., Jahreis, K., Kremling, A., Lengeler, J.W., Gilles, E.D.: Correlation between growth rates, EIIACrr phosphorylation, and Intracellular Cyclic AMP levels in Escherichia coli K-12. J. Bacteriol. 189(19), 6891–6900 (2007)

    Article  Google Scholar 

  12. Ropers, D., de Jong, H., Page, M., Schneider, D., Geiselmann, J.: Qualitative simulation of the carbon starvation response in Escherichia coli. Biosystems 84(2), 124–152 (2006)

    Article  Google Scholar 

  13. de Jong, H., Geiselmann, J., Hernandez, C., Page, M.: Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19(3), 336–344 (2003)

    Article  Google Scholar 

  14. Casey, R., Jong, H., Gouzé, J.: Piecewise-linear models of genetic regulatory networks: Equilibria and their stability. Journal of Mathematical Biology 52(1), 27–56 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Chaves, M., Gouzé, J.-L.: Piecewise Affine Models of Regulatory Genetic Networks: Review and Probabilistic Interpretation. In: Lévine, J., Müllhaupt, P. (eds.) Advances in the Theory of Control, Signals and Systems with Physical Modeling. LNCIS, vol. 407, pp. 241–253. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. De Jong, H., Gouzé, J., Hernandez, C., Page, M., Sari, T., Geiselmann, J.: Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bulletin of Mathematical Biology 66(2), 301–340 (2004)

    Article  MathSciNet  Google Scholar 

  17. Gouzé, J., Sari, T.: A class of piecewise linear differential equations arising in biological models. Dynamical Systems 17(4), 299–316 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  18. Grognard, F., De Jong, H., Gouzé, J.: Piecewise-linear models of genetic regulatory networks: theory and example. Biology and Control Theory: Current Challenges, 137–159 (2007)

    Google Scholar 

  19. Yagil, G., Yagil, E.: On the relation between effector concentration and the rate of induced enzyme synthesis. Biophysical Journal 11(1), 11–27 (1971)

    Article  Google Scholar 

  20. Filippov, A., Arscott, F.: Differential equations with discontinuous righthand sides. In: Mathematics and its Applications Series. Kluwer Academic Publishers (1988)

    Google Scholar 

  21. Klumpp, S., Zhang, Z., Hwa, T.: Growth rate-dependent global effects on gene expression in bacteria. Cell 139(7), 1366–1375 (2010)

    Article  Google Scholar 

  22. Scott, M., Gunderson, C.W., Mateescu, E.M., Zhang, Z., Hwa, T.: Interdependence of cell growth and gene expression: Origins and consequences. Science 330(6007), 1099–1102 (2010)

    Article  Google Scholar 

  23. Eden, E., Geva-Zatorsky, N., Issaeva, I., Cohen, A., Dekel, E., Danon, T., Cohen, L., Mayo, A., Alon, U.: Proteome half-life dynamics in living human cells. Science 331(6018), 764–768 (2011)

    Article  Google Scholar 

  24. Krin, E., Sismeiro, O., Danchin, A., Bertin, P.N.: The regulation of Enzyme IIAGlc expression controls adenylate cyclase activity in Escherichia coli. Microbiology 148(5), 1553–1559 (2002)

    Article  Google Scholar 

  25. Notley-McRobb, L., Death, A., Ferenci, T.: The relationship between external glucose concentration and cAMP levels inside Escherichia coli: implications for models of phosphotransferase-mediated regulation of adenylate cyclase. Microbiology 143(6), 1909–1918 (1997)

    Article  Google Scholar 

  26. Vajda, S., Rabitz, H., Walter, E., Lecourtier, Y.: Qualitative and quantitative identifiability analysis of nonlinear chemical kinetic models. Chemical Engineering Communications 83(1), 191–219 (1989)

    Article  Google Scholar 

  27. Chis, O., Banga, J., Balsa-Canto, E.: Structural identifiability of systems biology models: A critical comparison of methods. PloS one 6(11), e27755 (2011)

    Google Scholar 

  28. Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., Timmer, J.: Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25(15), 1923–1929 (2009)

    Article  Google Scholar 

  29. Walter, É., Pronzato, L.: Identification of parametric models from experimental data. Communications and Control Engineering, Springer (1997)

    Google Scholar 

  30. Dochain, D., Vanrolleghem, P.: Dynamical Modelling and Estimation in Wastewater Treatment Processes. IWA Publishing (2001)

    Google Scholar 

  31. Seber, G., Wild, C.: Nonlinear regression, vol. 503. Libre Digital (2003)

    Google Scholar 

  32. Gallant, A.: Nonlinear regression. The American Statistician 29(2), 73–81 (1975)

    MathSciNet  MATH  Google Scholar 

  33. Bremer, H., Dennis, P., et al.: Modulation of chemical composition and other parameters of the cell by growth rate. Escherichia Coli and Salmonella: Cellular and Molecular Biology 2, 1553–1569 (1996)

    Google Scholar 

  34. Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley (1989)

    Google Scholar 

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Carta, A., Chaves, M., Gouzé, JL. (2012). A Simple Model to Control Growth Rate of Synthetic E. coli during the Exponential Phase: Model Analysis and Parameter Estimation. In: Gilbert, D., Heiner, M. (eds) Computational Methods in Systems Biology. CMSB 2012. Lecture Notes in Computer Science(), vol 7605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33636-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-33636-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33635-5

  • Online ISBN: 978-3-642-33636-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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