Design of Experiments to Investigate Dynamic Cell Signaling Models

  • Samuel BandaraEmail author
  • Tobias Meyer
Part of the Methods in Molecular Biology book series (MIMB, volume 880)


This chapter describes approaches to make use of dynamic models of cell signaling systems in order to optimize experiments in cell biology. We are particularly focusing on the question of how small molecule inhibitors or activators can best be used to get the most information out of a limited number of experiments when only a handful of molecular species can be measured. One goal addressed by this chapter is to find time course experiments to discriminate between rivaling molecular mechanisms. The other goal is to find experiments that are useful for inferring rate constants, binding affinities, concentrations, and other model parameters from time course data. Both are treated as optimal control problems in which rapid pharmacological perturbation schemes are identified in silico in order to close an experimental cycle from modeling back to the laboratory bench.

Key words

Parameter estimation Model discrimination Parameter uncertainty Experimental design Optimization Dynamical systems 



We would like to thank Johannes Schlöder, Sean Collins, Meredith Betterton, and Xuedong Liu for helpful comments on this chapter.


  1.  1.
    Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 3(10):1871–1878PubMedCrossRefGoogle Scholar
  2.  2.
    Bock HG (1987) Randwertproblemmethoden zur Parameteridentifizierung in Systemen nichtlinearer Differentialgleichungen. Bonner Mathematische Schriften, 183Google Scholar
  3.  3.
    Bock HG, Kostina E, Schlöder JP (2007) Numerical methods for parameter estimation in nonlinear differential algebraic equations. GAMM Mitt 30(2):352–375CrossRefGoogle Scholar
  4.  4.
    Chen BH, Asprey SP (2003) On the design of optimally informative dynamic experiments for model discrimination in multiresponse nonlinear situations. Ind Eng Chem Res 42(7):1379–1390CrossRefGoogle Scholar
  5.  5.
    Bauer I, Bock HG, Körkel S, Schlöder JP (2000) Numerical methods for optimum experimental design in DAE systems. J Comput Appl Math 120:1–25CrossRefGoogle Scholar
  6.  6.
    Bandara S, Schlöder JP, Eils R, Bock HG, Meyer T (2009) Optimal experimental design for parameter estimation of a cell signaling model. PLoS Comput Biol 5(11):e1000558PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Chemical and Systems BiologyStanford University Medical CenterStanfordUSA

Personalised recommendations