Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.

ABSTRACT:

Background

Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.

Results

In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without

SUBMITTER: Frohlich H 

PROVIDER: S-EPMC2770070 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature