Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24932011
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Bioinformatics
2014 ; 30
(12
): i96-104
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Large scale analysis of signal reachability
#MMPMID24932011
Todor A
; Gabr H
; Dobra A
; Kahveci T
Bioinformatics
2014[Jun]; 30
(12
): i96-104
PMID24932011
show ga
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.
free
free
free
