PReach: Reachability in Probabilistic Signaling Networks

  • Authors:

  • Haitham Gabr;Andrei Todor;Helia Zandi;Alin Dobra;Tamer Kahveci

  • Affiliations:

  • University of Florida, CISE Department, Gainesville, Florida 32611;University of Florida, CISE Department, Gainesville, Florida 32611;University of Florida, CISE Department, Gainesville, Florida 32611;University of Florida, CISE Department, Gainesville, Florida 32611;University of Florida, CISE Department, Gainesville, Florida 32611

  • Venue:
  • Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
  • Year:
  • 2013

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Abstract

Extracellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources) which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extracellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bivariate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real datasets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods.