Mechanistic Hypothesis Exploration of Signaling Network Processes via Bayesian Inference Methods

Abstract

Characterization of signal execution dynamics within complex biochemical networks is highly challenging but necessary to understand how cells process signals and commit to a biological phenotype. Mechanistic interpretation of experimental results can be inaccurate due to limited data or the need for an unrealistic number of measurements. Exploration of network dynamics via mathematical simulation, while useful, may be limited by a dearth of knowledge of reaction rate parameters or the data needed to calibrate them. To address this challenge, we take a probabilistic approach to the analysis of network-driven biochemical processes using a Bayesian inference formalism to explore network dynamics when data is limited and identify the roles of various subnetworks in the overall mechanism of a biochemical signaling network. We applied the approach to the well-studied signal execution pathways of mammalian extrinsic apoptosis and produced results concurrent with experimental evidence, as well as several additional hypotheses, regarding mechanistic specifics of mitochondrial signal amplification.