Abstract
BackgroundComputing 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.MethodologyIn this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence.ConclusionsThis method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs.AvailabilitySource code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse]
Highlights
Analyzing biological networks is essential in understanding the machinery of living organisms which has been a main goal for scientists [1,2]
Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles
Cell Cycles of Budding Yeast and Fission Yeast To evaluate the accuracy of the results reported by our algorithm, we compared the steady states that we found to the steady states that are reported in the literature
Summary
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]. We use the term biological regulatory networks (BRN) to combine gene regulatory networks and signal transduction pathways. 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
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