Abstract
Motivation: Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge.Results: We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors) when the topology of the underlying signaling network is uncertain. Our method tackles this problem for large networks while providing a provably accurate result. Our method follows a divide-and-conquer strategy. We break down the given network into a sequence of non-overlapping subnetworks such that reachability can be computed autonomously and sequentially on each subnetwork. We represent each interaction using a small polynomial. The product of these polynomials express different scenarios when a signal can or cannot reach to target genes from the source genes. We introduce polynomial collapsing operators for each subnetwork. These operators reduce the size of the resulting polynomial and thus the computational complexity dramatically. We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. We demonstrate that our method can successfully characterize key reachability characteristics of the entire transcriptions regulatory networks of patients affected by eight different subtypes of leukemia, as well as those from healthy control samples.Availability: All the datasets and code used in this article are available at bioinformatics.cise.ufl.edu/PReach/scalable.htm.Contact: atodor@cise.ufl.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Major disorders, such as cancer, have been shown to alter the transcription of a large number of genes and affect the mechanism that governs cells functions (Krivtsov, 2009; Valk et al, 2004)
We address the problem of characterizing the signaling reachability in transcription regulatory networks (TRNs)
We show that the reachability profile can help us understand how different disorders that alter the cellular functions based on the signaling patterns of the gene regulatory networks
Summary
Major disorders, such as cancer, have been shown to alter the transcription of a large number of genes and affect the mechanism that governs cells functions (Krivtsov, 2009; Valk et al, 2004). While the pattern of this mechanism is similar for all organisms, important variations in its quantitative aspects such as gene expressions result from external perturbations, differentiation stage of the cell, timing of DNA replication and various epigenetic mutations (Los et al, 2009; Mattick et al, 2009). Detecting these quantitative variations is an important source of information for assessing the fitness of the organism and for diagnosis and prognosis
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