Trajectory Tracking for Genetic Networks Using Control Theory

  • Natalja StrelkowaEmail author
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)


Synthetic biology has impressively progressed during the last decades making it possible to rationally design and implement genetic networks with new functionalities in living microorganisms. With these new technologies the expression of genes can be observed using fluorescent markers and influenced using light flashes and photo-active expression inducers. In this contribution, we suggest the implementation of external feedback control for dynamic trajectory tracking of a synthetic genetic network. The feedback control can be implemented in living microorganisms using fluorescent markers for system readout and photo-active gene expression inducers for external control signals. In particular we show that hierarchical or sequential design for synthetic gene networks makes controlled trajectory tracking possible using the readout and control actions on few instead of all genes. Optimised trajectory tracking opens the possibility to interact and influence genetic networks in a very precise manner in terms of time and location with minimal cell burden.


synthetic gene networks feedback control generalised repressilator trajectory tracking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Csete, M.E., Doyle, J.C.: Reverse engineering of biological complexity. Science 295, 1664–1669 (2002)CrossRefGoogle Scholar
  2. 2.
    Lin, F., Muthuraman, K., Lawley, M.: An optimal control theory approach to non-pharmaceutical interventions. BMC Infectious Diseases 10, 32 (2010)CrossRefGoogle Scholar
  3. 3.
    Stan, G.B., Belmudes, F., Fonteneau, R., Zeggwagh, F., Lefebvre, M.A., et al.: Modelling the influence of activation-induced apoptosis of cd4[sup + ] and cd8[sup + ] t-cells on the immune system response of a hiv-infected patient. IET Systems Biology 2, 94–102 (2008)CrossRefGoogle Scholar
  4. 4.
    Schiff, S.J., Sauer, T.: Kalman filter control of a model of spatiotemporal cortical dynamics. J. Neural Eng. 5, 1–8 (2008)CrossRefGoogle Scholar
  5. 5.
    Khalil, A.S., Collins, J.J.: Synthetic biology: applications come of age. Nat. Rev. Genet. 11, 367–379 (2010)CrossRefGoogle Scholar
  6. 6.
    Cameron, D.E., Bashor, C.J., Collins, J.J.: A brief history of synthetic biology. Nat. Rev. Micro. 12, 381–390 (2014)CrossRefGoogle Scholar
  7. 7.
    Cai, L., Friedman, N., Xie, X.S.: Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358–362 (2006)CrossRefGoogle Scholar
  8. 8.
    Bennett, M.R., Hasty, J.: Microfluidic devices for measuring gene network dynamics in single cells. Nat. Rev. Genet. 10, 628–638 (2009)CrossRefGoogle Scholar
  9. 9.
    Shimizu-Sato, S., Huq, E., Tepperman, J.M., Quail, P.H.: A light-switchable gene promoter system. Nat. Biotech. 20, 1041–1044 (2002)CrossRefGoogle Scholar
  10. 10.
    Levskaya, A., Weiner, O.D., Lim, W.A., Voigt, C.A.: Spatiotemporal control of cell signalling using a light-switchable protein interaction. Nature 461, 997–1001 (2009)CrossRefGoogle Scholar
  11. 11.
    Slotine, J.J.E., Li, W.: Applied nonlinear control. Prentice-Hall, New Jersey (1991)zbMATHGoogle Scholar
  12. 12.
    Ernst, D., Geurts, P., Wehenkel, L.: Tree-based batch mode reinforcement learning. Journal of Machine Learning Research 6, 503–556 (2005)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Strelkowa, N.: Inference of Optimized Control Strategies for Genetic Networks. In: ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol. 8, pp. 265–270. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Bertsekas, D.: Dynamic Programming and Optimal Control, 2nd edn., vol. I. Athena Scientific, Belmont (2000)Google Scholar
  15. 15.
    van Kampen, N.G.: Stochastic Processes in Physics and Chemistry, 3rd edn. Elsevier, Amsterdam (2007)Google Scholar
  16. 16.
    Strelkowa, N., Barahona, M.: Switchable genetic oscillator operating in quasi-stable mode. Journal of the Royal Society Interface (2010)Google Scholar
  17. 17.
    Strelkowa, N., Barahona, M.: Transient dynamics around unstable periodic orbits in the generalized repressilator model. Chaos: An Interdisciplinary Journal of Nonlinear Science 21, 023104 (2011)Google Scholar
  18. 18.
    El-Samad, H., Khammash, M.: Modelling and analysis of gene regulatory networks using feedback control theory. International Journal of Systems Science 41, 17–33 (2010)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Boehringer Ingelheim Pharma GmbH and Co. KGRhineland-PalatinateGermany

Personalised recommendations