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Basic concepts and principles of stoichiometric modeling of metabolic networks
Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic pla...
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Published in: | Biotechnology journal 2013-09, Vol.8 (9), p.997-1008 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. Constraint‐based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint‐based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.
Metabolism is responsible for the supply of energy and building blocks for cell growth and maintenance. One way to explore metabolism is by using computational modeling approaches. In this review, the authors use simple metabolic networks to explain the state‐of‐the‐art computational modeling techniques. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces are explained with simple, illustrative examples. This review will provide rigorous and quantitative hypotheses and fundamental understanding for metabolic networks studies. |
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ISSN: | 1860-6768 1860-7314 |
DOI: | 10.1002/biot.201200291 |