title: Parameter inference and model selection in signaling pathway models.
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.
authors.affiliation: Division of Molecular Biosciences, Centre for Bioinformatics, Imperial College London, London, UK.
authors.forename: Tina
authors.initials: T
authors.lastname: Toni
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D053612:Janus Kinases / Q000378:metabolism; D008954:Models, Biological; D050791:STAT Transcription Factors / Q000378:metabolism; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
chemical_list: D050791:STAT Transcription Factors; D053612:Janus Kinases