State reduction for network intervention in probabilistic Boolean networks.

ABSTRACT:

Motivation

A key goal of studying biological systems is to design therapeutic intervention strategies. Probabilistic Boolean networks (PBNs) constitute a mathematical model which enables modeling, predicting and intervening in their long-run behavior using Markov chain theory. The long-run dynamics of a PBN, as represented by its steady-state distribution (SSD), can guide the design of effective intervention strategies for the modeled systems. A major obstacle for its application is the large state space of the underlying Markov chain, which poses a serious computational challenge. Hence, it is critical to reduce the model complexity of PBNs for practical applications.

Results

We propose a strategy to reduce the state space of the underlying Markov chain of a PBN based on a

SUBMITTER: Qian X 

PROVIDER: S-EPMC3025721 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature