Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs.

ABSTRACT: Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsist

SUBMITTER: Melas IN 

PROVIDER: S-EPMC3764019 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature