Predicting dynamic signaling network response under unseen perturbations.

ABSTRACT:

Motivation

Predicting trajectories of signaling networks under complex perturbations is one of the most valuable, but challenging, tasks in systems biology. Signaling networks are involved in most of the biological pathways, and modeling their dynamics has wide applications including drug design and treatment outcome prediction.

Results

In this paper, we report a novel model for predicting the cell type-specific time course response of signaling proteins under unseen perturbations. This algorithm achieved the top performance in the 2013 8th Dialogue for Reverse Engineering Assessments and Methods (DREAM 8) subchallenge: time course prediction in breast cancer cell lines. We formulate the trajectory prediction problem into a standard regularization problem; the solution becom

SUBMITTER: Zhu F 

PROVIDER: S-EPMC4173019 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature