Encyclopedia of Systems Biology

2013 Edition
| Editors: Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota

Optimal Experiment Design, Multimodality

  • Maria Rodriguez-Fernandez
  • Francis J. DoyleIII
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-9863-7_1224



Mathematical models of complex biological systems are typically represented in the form of nonlinear ordinary differential equations. This raises three important problems for optimal experimental design based on the Fisher information matrix (FIM) ( Optimal Experiment Design, Fisher Information). The first is that, due to the linear nature of the FIM, it may be a poor measure of the size of the uncertainty region. The second is that this matrix depends on the value assumed for the parameters in the case of nonlinear models. And the third, that applies to any  optimal experimental design, either for accurate estimation of the parameters ( Designing Experiments for Sound Statistical Inference), model discrimination ( Optimal Experimental Design, Model Discrimination), or maximization of a model response, is that the resultant nonlinear optimization problem is generally nonconvex. Therefore, optimal experimental design for nonlinear models...

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  1. Balsa-Canto E, Alonso AA, Banga JR (2008) Computational procedures for optimal experimental design in biological systems. IET Syst Biol 2(4):163–172PubMedGoogle Scholar
  2. 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):e1000558PubMedGoogle Scholar
  3. Chaloner K, Verdinelli I (1995) Bayesian experimental design: a review. Stat Sci 10:273–304Google Scholar
  4. Chu Y, Hahn J (2010) Quantitative optimal experimental design using global sensitivity analysis via quasi-linearization. Ind Eng Chem Res 49:7782–7794Google Scholar
  5. Gadkar KG, Gunawan R, Doyle FJ III (2005) Iterative approach to model identification of biological networks. BMC Bioinformatics 6:155PubMedGoogle Scholar
  6. Hamilton DC, Walls DG (1985) A quadratic design criterion for precise estimation in nonlinear regression models. Technometrics 27:241–250Google Scholar
  7. He F, Brown M, Yue H (2010) Maximin and Bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems. Int J Robust Nonlinear Control 20:1059–1078Google Scholar
  8. Kreutz C, Timmer J (2009) Systems biology: experimental design. FEBS J 276(4):923–942PubMedGoogle Scholar
  9. Rodriguez-Fernandez M, Kucherenko S, Pantelides C, Shah N (2007) Optimal experimental design based on global sensitivity analysis. In: Computer-aided chemical engineering, vol 24, Elsevier, Bucharest, pp 63–68Google Scholar
  10. Walter E, Pronzato L (1997) Identification of parametric models from experimental data. Springer, BerlinGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Maria Rodriguez-Fernandez
    • 1
  • Francis J. DoyleIII
    • 1
  1. 1.Department of Chemical EngineeringInstitute for Collaborative Biotechnologies, University of CaliforniaSanta BarbaraUSA