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Systems Biology for Signaling Networks pp 111–137Cite as

Computational Procedures for Model Identification

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Part of the book series: Systems Biology ((SYSTBIOL))

Abstract

Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model’s response. These parameters can be usually estimated by fitting the model to experimental data. This chapter covers the parameter identification problem, its formulation and solution, and two closely related topics: identifiability and optimal experimental design.

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Acknowledgments

The authors acknowledge financial support from Spanish MICINN project “MultiSysBio,” ref. DPI2008-06880-C03-02.

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Authors and Affiliations

  1. (Bio)Process Engineering Group, IIM-CSIC (Spanish National Research Council), C/Eduardo Cabello, 6, 36208, Vigo, Spain

    Eva Balsa-Canto & Julio R. Banga

Authors
  1. Eva Balsa-Canto
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  2. Julio R. Banga
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Corresponding author

Correspondence to Eva Balsa-Canto .

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Editors and Affiliations

  1. College of Natural Science, Dept. Biological Science, Department of Molecular Science and Tech, Suwon, 442-749, Korea, Republic of (South Korea)

    Sangdun Choi

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Balsa-Canto, E., Banga, J.R. (2010). Computational Procedures for Model Identification. In: Choi, S. (eds) Systems Biology for Signaling Networks. Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5797-9_5

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