lpNet: a linear programming approach to reconstruct signal transduction networks

Marta R A MatosLars Kaderali


With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease's mechanisms of action. We have implemented the approach as an R package available through bioconductor. This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org (http://bioconductor.org/packages/release/bioc/html/lpNet.html) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures. bettina.knapp@helmholtz-muenchen.de Supplementary data are available at Bioinformatics ...Continue Reading


Dec 7, 2000·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·N FriedmanDana Pe'er
Jul 25, 2007·Bioinformatics·Florian MarkowetzRainer Spang
Aug 1, 2013·American Journal of Physical Anthropology·Renato Bender, Nicole Bender


Aug 11, 2015·Frontiers in Microbiology·Gungor BudakNurcan Tuncbag

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Computer Graphics
Programming, Linear
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