Optimization Based Design of Synthetic Oscillators from Standard Biological Parts

  • Irene Otero-Muras
  • Julio R. Banga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8859)

Abstract

We consider the problem of optimal design of synthetic biological oscillators. Our aim is, given a set of standard biological parts and some pre-specified performance requirements, to automatically find the circuit configuration and its tuning so that self-sustained oscillations meeting the requirements are produced. To solve this design problem, we present a methodology based on mixed-integer nonlinear optimization. This method also takes into account the possibility of including more than one design objective and of handling both deterministic and stochastic descriptions of the dynamics. Further, it is capable of handling significant levels of circuit complexity. We illustrate the performance of this method with several challenging case studies.

Keywords

gene regulatory network synthetic biology multiobjective optimization synthetic oscillator optimization based design 

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References

  1. 1.
    Elowitz, M.B., Leibler, S.: A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000)CrossRefGoogle Scholar
  2. 2.
    Stricker, J., Cookson, S., Bennett, M.R., Mather, W.H., Tsimring, L.S., Hasty, J.: A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008)CrossRefGoogle Scholar
  3. 3.
    Tigges, M., Dénervaud, N., Greber, D., Stelling, J., Fussenegger, M.: A synthetic low-frequency mammalian oscillator. Nucleic Acids Res. 38, 2702–2711 (2010)CrossRefGoogle Scholar
  4. 4.
    Goodwin, B.C.: Oscillatory behaviour in enzymatic control processes. Adv. in Enzyme Regulation 3, 425–438 (1965)CrossRefGoogle Scholar
  5. 5.
    Higgings, J.: The theory of oscillating reactions. Ind. Eng. Chem. 59, 18–62 (1967)CrossRefGoogle Scholar
  6. 6.
    Tyson, J.J., Albert, R., Goldbeter, A., Ruoff, P., Sible, J.: Biological switches and clocks. J. R. Soc. Interface 6, S1–S8 (2008)Google Scholar
  7. 7.
    Purnick, P.E.M., Weiss, R.: The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cel. Biol. 10, 410–422 (2009)CrossRefGoogle Scholar
  8. 8.
    Lu, T.K., Khalil, A.S., Collins, J.J.: Next-generation synthetic gene networks. Nat. Biotechnol. 27, 1139–1150 (2009)CrossRefGoogle Scholar
  9. 9.
    Registry of Standard Biological Parts, http://partsregistry.org
  10. 10.
    Ham, T.S., Dmytriv, Z., Plahar, H., Chen, J., Hillson, N.J., Keasling, J.D.: Design, implementation and practice of JBEI-ICE: an open source biological part registry platform and tools. Nucleic Acids Res. 40, e141 (2012)Google Scholar
  11. 11.
    Cai, Y., Hartnett, B., Gustafsson, C., Peccoud, J.: A syntactic model to design and verify synthetic genetic constructs derived from standard biological parts. Bioinformatics 23, 2760–2767 (2007)CrossRefGoogle Scholar
  12. 12.
    Pedersen, M., Phillips, A.: Towards programming languages for genetic engineering of living cells. J. R. Soc. Interface 6(suppl. 4), S437–S450 (2009)Google Scholar
  13. 13.
    Bilitchenko, L., Liu, A., Densmore, D.: The Eugene Language for Synthetic Biology. In: Voigt, C. (ed.) Methods in Enzymology, vol. 498, pp. 153–172 (2011)Google Scholar
  14. 14.
    Rodrigo, G., Carrera, J., Landrain, T.E., Jaramillo, A.: Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Lett. 586, 2037–2042 (2012)CrossRefGoogle Scholar
  15. 15.
    Zomorrodi, A.R., Maranas, C.D.: Coarse-grained optimization-driven design and piecewise linear modeling of synthetic genetic circuits. Eur. J. Oper. Res. 237, 665–676 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008)CrossRefGoogle Scholar
  17. 17.
    Marchisio, M.A., Stelling, J.: Computational design of synthetic gene circuits with composable parts. Bioinformatics 24, 1903–1910 (2008)CrossRefGoogle Scholar
  18. 18.
    Stewart, D.: Modular modelling in Synthetic Biology: Light-Based Communication in E. coli. Electron. Notes Theor. Comput. Sci. 277, 77–87 (2011)CrossRefGoogle Scholar
  19. 19.
    Endy, D.: Foundations for engineering biology. Nature 438, 449–453 (2005)CrossRefGoogle Scholar
  20. 20.
    Galdzicki, M., et al.: The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nature Biotech. 32, 545–550 (2014)CrossRefGoogle Scholar
  21. 21.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)CrossRefGoogle Scholar
  22. 22.
    Gaspard, P.: The correlation time of mesoscopic chemical clocks. J. Chem. Phys. 117, 8905–8916 (2002)CrossRefGoogle Scholar
  23. 23.
    Gray, C.M., Konig, P., Engel, A.K., Singer, W.: Oscillatory responses in cat visual cortex exhibit inter columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989)CrossRefGoogle Scholar
  24. 24.
    d’Eysmond, T., De Simone, A., Naef, F.: Analysis of precision in chemical oscillators: implications for circadian clocks. Phys. Biol. 10, 056005 (2013)Google Scholar
  25. 25.
    Gaspard, P.: Trace formula for noisy flows. J. Stat. Phys. 106, 57–96 (2002)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Min, B., Goh, K.I., Kim, I.M.: Noise characteristics of molecular oscillations in simple genetic oscillatory systems. J. Korean Phys. Soc. 56(3), 911–917 (2010)CrossRefGoogle Scholar
  27. 27.
    Loinger, A., Biham, O.: Stochastic simulations of the repressilator circuit. Phys. Rev. E 76, 051917 (2007)Google Scholar
  28. 28.
    Otero-Muras, I., Banga, J.R.: Multicriteria global optimization for biocircuit design. arXiv:1402.7323 (2014)Google Scholar
  29. 29.
    Tsai, T.Y., Choi, Y.S., Ma, W., Pomerening, J.R., Tang, C., Ferrell, J.E.: Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science 321, 126–129 (2008)CrossRefGoogle Scholar
  30. 30.
    Egea, J.A., Marti, R., Banga, J.R.: An evolutionary method for complex-process optimization. Comput. Oper. Res. 37, 315–324 (2010)CrossRefMATHGoogle Scholar
  31. 31.
    Exler, O., Antelo, L.T., Egea, J.A., Alonso, A.A., Banga, J.R.: A tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design. Comput. Chem. Eng. 32, 1877–1891 (2008)CrossRefGoogle Scholar
  32. 32.
    Schlueter, M., Egea, J.A., Banga, J.R.: Extended ant colony optimization for non-convex mixed integer nonlinear programming. Comput. Oper. Res. 36, 2217–2229 (2009)MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Chickarmane, V., Paladugu, S.R., Bergmann, F., Sauro, H.M.: Bifurcation discovery tool. Bioinformatics 21, 3688–3690 (2005)CrossRefGoogle Scholar
  34. 34.
    Levering, J., Kummer, U., Becker, K., Sahle, S.: Glycolytic oscillations in a model of a lactic acid bacterium metabolism. Biophys. Chem. 172, 53–60 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Irene Otero-Muras
    • 1
  • Julio R. Banga
    • 1
  1. 1.BioProcess Engineering Group, IIM-CSICSpanish Council for Scientific ResearchVigoSpain

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