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To support and guide an extensive experimental research into systems biologyof signaling pathways, increasingly more mechanistic models are being developedwith hopes of gaining further insight into biological processes. In order toanalyse these models, computational and statistical techniques are needed toestimate the unknown kinetic parameters. This chapter reviews methods fromfrequentist and Bayesian statistics for estimation of parameters and forchoosing which model is best for modeling the underlying system. ApproximateBayesian Computation (ABC) techniques are introduced and employed to exploredifferent hypothesis about the JAK-STAT signaling pathway.

Parameter inference and model selection in signaling pathway models