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Bioinformatics. 2015 Oct 1;31(19):3231-3. doi: 10.1093/bioinformatics/btv327. Epub 2015 May 29.

lpNet: a linear programming approach to reconstruct signal transduction networks.

Author information

1
Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and.
2
Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany and Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany.

Abstract

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.

AVAILABILITY AND IMPLEMENTATION:

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.

CONTACT:

bettina.knapp@helmholtz-muenchen.de

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26026168
DOI:
10.1093/bioinformatics/btv327
[Indexed for MEDLINE]

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