Inference and prediction of metabolic network fluxes
Author(s) -
Zoran Nikoloski,
Richard Perez-Storey,
Lee Sweetlove
Publication year - 2015
Publication title -
plant physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.554
H-Index - 312
eISSN - 1532-2548
pISSN - 0032-0889
DOI - 10.1104/pp.15.01082
Subject(s) - metabolic network , flux balance analysis , inference , robustness (evolution) , exploit , metabolic flux analysis , computer science , flux (metallurgy) , context (archaeology) , systems biology , biochemical engineering , biological system , network analysis , plant metabolism , biology , computational biology , artificial intelligence , engineering , chemistry , metabolism , paleontology , biochemistry , computer security , electrical engineering , organic chemistry , gene , endocrinology , rna
In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.
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