Parameter inference and model selection in signaling pathway models

TL;DR

This paper reviews statistical methods, including ABC techniques, for estimating parameters and selecting models in signaling pathway models, aiding biological research.

Contribution

It introduces and applies ABC methods to analyze signaling pathways, providing a comparative review of frequentist and Bayesian approaches.

Findings

ABC techniques effectively explore pathway hypotheses

Bayesian methods assist in model selection

Enhanced understanding of JAK-STAT pathway dynamics

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 analyse 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 (ABC) techniques are introduced and employed to explore different hypothesis about the JAK-STAT signaling pathway.