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Computational Biology pp 283–295Cite as

Parameter Inference and Model Selection in Signaling Pathway Models

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 673))

Abstract

To support and guide an extensive experimental research into systems biology of signaling pathways, increasingly more mechanistic models are being developed with hopes of gaining further insight into biological processes. In order to analyze these models, computational and statistical techniques are needed to estimate the unknown kinetic parameters. This chapter reviews methods from frequentist and Bayesian statistics for estimation of parameters and for choosing which model is best for modeling the underlying system. Approximate Bayesian computation techniques are introduced and employed to explore different hypothesis about the JAK-STAT signaling pathway.

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Authors and Affiliations

  1. Division of Molecular Biosciences, Centre for Bioinformatics, Imperial College London, London, UK

    Tina Toni & Michael P. H. Stumpf

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  1. Tina Toni
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  2. Michael P. H. Stumpf
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Correspondence to Michael P. H. Stumpf .

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Editors and Affiliations

  1. The Rockefeller University, York Avenue 1230, New York, 10065, New York, USA

    David Fenyö

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Toni, T., Stumpf, M.P.H. (2010). Parameter Inference and Model Selection in Signaling Pathway Models. In: Fenyö, D. (eds) Computational Biology. Methods in Molecular Biology, vol 673. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-842-3_18

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  • DOI: https://doi.org/10.1007/978-1-60761-842-3_18

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  • Publisher Name: Humana Press, Totowa, NJ

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  • Online ISBN: 978-1-60761-842-3

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