Analysis of complex metabolic behavior through pathway decomposition
File(s)
Author(s)
Ip, K
Colijn, C
Lun, DS
Type
Journal Article
Abstract
Background: Understanding complex systems through decomposition into simple interacting components is a
pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be
reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary
flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition
using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular
metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially
with its size.
Results: Here, we present a new method for decomposition of metabolic flux distributions into elementary flux
modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant
modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic
engineering of Escherichia coli and for understanding the survival of Mycobacterium tuberculosis (MTB) during
infection.
Conclusions: Our method can achieve computational time improvements exceeding 2000-fold and requires only
several seconds to generate elementary mode decompositions on genome-scale networks. These improvements
arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The
decompositions from our method are useful for understanding complex flux distributions and debugging genomescale
models.
pervasive paradigm throughout modern science and engineering. For cellular metabolism, complexity can be
reduced by decomposition into pathways with particular biochemical functions, and the concept of elementary
flux modes provides a systematic way for organizing metabolic networks into such pathways. While decomposition
using elementary flux modes has proven to be a powerful tool for understanding and manipulating cellular
metabolism, its utility, however, is severely limited since the number of modes in a network increases exponentially
with its size.
Results: Here, we present a new method for decomposition of metabolic flux distributions into elementary flux
modes. Our method can easily operate on large, genome-scale networks since it does not require all relevant
modes of the metabolic network to be generated. We illustrate the utility of our method for metabolic
engineering of Escherichia coli and for understanding the survival of Mycobacterium tuberculosis (MTB) during
infection.
Conclusions: Our method can achieve computational time improvements exceeding 2000-fold and requires only
several seconds to generate elementary mode decompositions on genome-scale networks. These improvements
arise from not having to generate all relevant elementary modes prior to initiating the decomposition. The
decompositions from our method are useful for understanding complex flux distributions and debugging genomescale
models.
Date Issued
2011-06-03
Date Acceptance
2011-06-03
Citation
BMC Systems Biology, 2011, 5 (1)
ISSN
1752-0509
Publisher
BioMed Central
Journal / Book Title
BMC Systems Biology
Volume
5
Issue
1
Copyright Statement
© 2011 Ip et al; licensee BioMed Central Ltd. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000292692900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Mathematical & Computational Biology
MATHEMATICAL & COMPUTATIONAL BIOLOGY
ELEMENTARY FLUX MODES
MYCOBACTERIUM-TUBERCULOSIS
ESCHERICHIA-COLI
DRUG TARGETS
NETWORKS
FRAMEWORK
OPTIMIZATION
COMPUTATION
STRAINS
RECONSTRUCTION
Acetates
Computational Biology
Escherichia coli
Gene Knockout Techniques
Genetic Engineering
Metabolic Networks and Pathways
Mycobacterium tuberculosis
1199 Other Medical And Health Sciences
Bioinformatics
Publication Status
Published
Article Number
ARTN 91