Causal network inference using biochemical kinetics.

ABSTRACT:

Motivation

Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems.

Results

We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. T

SUBMITTER: Oates CJ 

PROVIDER: S-EPMC4147905 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature