Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.

ABSTRACT:

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system's response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, res

SUBMITTER: Kiani NA 

PROVIDER: S-EPMC4133630 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature