An empirical Bayesian approach for model-based inference of cellular signaling networks.

ABSTRACT:

Background

A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways.

Results

As an illustrative example, a kinetic model f

SUBMITTER: Klinke DJ 

PROVIDER: S-EPMC2781012 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature