sybil – Efficient constraint-based modelling in R

Background Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automate...

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Bibliographic Details
Published in:BMC systems biology Vol. 7; no. 1; p. 125
Main Authors: Gelius-Dietrich, Gabriel, Desouki, Abdelmoneim Amer, Fritzemeier, Claus Jonathan, Lercher, Martin J
Format: Journal Article
Language:English
Published: London BioMed Central 13.11.2013
BioMed Central Ltd
Subjects:
Algorithms
Anopheles
Bioinformatics
Biomedical and Life Sciences
Cell metabolism
Cellular and Medical Topics
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Graphics
Computer industry
Computer Simulation
Escherichia coli - metabolism
Genes
Genomes
Internet software
Life Sciences
Mathematical optimization
Methods
Models, Biological
Physiological
Physiological aspects
Programming Languages
Public software
Simulation and Modeling
Software
software and technology
Systems Biology
United States
Germany
Flux-balance analysis
MOMA
FBA
ROOM
GNU R
Constraint-based modelling
ISSN:1752-0509, 1752-0509
Online Access:Get full text
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Summary:Background Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. Results Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model. Conclusions Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).
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ISSN:1752-0509
1752-0509
DOI:10.1186/1752-0509-7-125