C Oates and S Mukherjee
Structural inference using nonlinear dynamics
Date: March 2012
Abstract: Network inference methods are widely used to study regulatory interplay in biological systems. Such methods are usually based on simple, often linear, approximations to underlying dynamics. We present a network inference methodology that is rooted in nonlinear biochemical kinetics. This is done by considering a dynamical system that depends on a reaction graph, summarizing all biochemical reactions and associated parameters. We assume that neither graph nor parameters are known; inference regarding the graph is carried out within a Bayesian framework, using an efficient Monte Carlo approach to integrate out parameters. In this way, we take account of model complexity as well as fit-to-data. Focusing on protein signaling networks, we show results on data simulated from a recent dynamical model of MAPK signaling. We find that the method is able to effectively recover regulatory relationships. Furthermore, the approach facilitates modeling of interventional data, since it respects the roles of individual variables.