Parameter Analysis

  • Mario Andrea Marchisio
Part of the Learning Materials in Biosciences book series (LMB)


Models for synthetic gene circuits necessitate the knowledge of kinetic parameter values. However, only a fraction of them can be measured in the lab. The others are obtained by fitting the model to experimental data. This task is, in general, non-trivial and might demand to use so-called stochastic algorithms, whose theoretical foundations are discussed in this chapter. Beside that, you will see how to exploit the difference in reaction rates in order to carry out model reduction. This procedure permits to restrict the number of kinetic parameters and ordinary differential equations necessary to compute the circuit dynamics. Circuit robustness and sensitivity analysis are explained in this chapter as well. Sensitivity analysis, in particular, is a technique that permit to understand if species concentrations are affected by changes in parameter values. With this knowledge one can figure out which reactions (and corresponding rate constants) play a major role in determining the working of a synthetic gene circuit.


  1. 3.
    U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in bacterial chemotaxis. Nature 397(6715), 168–171 (1999)CrossRefPubMedPubMedCentralGoogle Scholar
  2. 6.
    N. Barkai, S. Leibler, Robustness in simple biochemical networks. Nature 387(6636), 913–917 (1997)CrossRefGoogle Scholar
  3. 9.
    A. Becskei, L. Serrano, Engineering stability in gene networks by autoregulation. Nature 405(6786), 593 (2000)CrossRefGoogle Scholar
  4. 17.
    X.-J. Feng, S. Hooshangi, D. Chen, G. Li, R. Weiss, H. Rabitz, Optimizing genetic circuits by global sensitivity analysis. Biophys. J. 87(4), 2195–2202 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 29.
    E. Klipp, W. Liebermeister, C. Wierling, A. Kowald, H. Lehrach, R. Herwig, System Biology (Wiley-VCH, Weinheim, 2009)Google Scholar
  6. 48.
    Z. Zi, Sensitivity analysis approaches applied to systems biology models. IET Syst. Biol. 5(6), 336–346 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Mario Andrea Marchisio
    • 1
  1. 1.School of Life Science and TechnologyHarbin Institute of TechnologyHarbinChina

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