Research Article: Scalable Steady State Analysis of Boolean Biological Regulatory Networks

Date Published: December 1, 2009

Publisher: Public Library of Science

Author(s): Ferhat Ay, Fei Xu, Tamer Kahveci, Mark Isalan.

Abstract: Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem.

Partial Text: Analyzing biological networks is essential in understanding the machinery of living organisms which has been a main goal for scientists [1], [2]. Gene regulatory networks and signaling pathways are two important network types that play role in every process of living organisms [3]. In the last decade, significant amount of research has been done on reconstruction of these networks from experimental data [4]–[11]. The amount of regulatory data produced by these methods is sufficient enough to trigger the research on automated tools to analyze various aspects of these networks. We use the term biological regulatory networks (BRN) to combine gene regulatory networks and signal transduction pathways.

This section discusses our algorithm for identifying all the steady states of Boolean BRNs. First we describe the mathematical model for expressing the states and state transitions. Then, we discuss our method to segregate the state space into three subspaces. Finally, we present our randomized traversal method that extracts Type 1 steady states. We also give the formulation of a stopping criterion that terminates the traversal when sufficient amount of steady states are reported with high confidence.



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