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Flux balance analysis of metabolic models: a review of recent advances and applications

Date

2016

Authors

Estep, Forrest Earnest, author
Peebles, Christie, advisor
Shipman, Patrick, committee member
Snow, Chris, committee member

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Abstract

Genome-level reconstructions of metabolic networks have provided new insight into the cellular functions of many organisms. These metabolic models are massive constructs, often including thousands of metabolic and transport reactions and metabolite species for even the most basic organisms. Construction of these models has typically involved an initial genomic analysis to identify known genes or genes with homologous structures for which the function may be inferred, followed by an intensive process of literature searching and experimental validation to refine the model. A number of automated algorithms have been developed to assist with this process. Once the model has been constructed, optimization techniques are applied to predict the distribution of fluxes through the reaction network. The systems then studied by FBA are generally static systems, assumed to be operating at a steady state, and thus constrained by the stoichiometries of the reactions rather than the kinetics. While these assumptions have shown to be valid under select laboratory conditions, evidence indicates that most organisms are not always at this steady state. A number of model improvements have been considered to bring predicted results more in line with experimental data, including the addition of regulatory controls, more detailed incorporation of thermodynamics, and the consideration of metabolite pool and flux data from metabolomics and labeled carbon studies, respectively. The improved predictive capabilities of these models readily find application in metabolic engineering in the custom strain design of organisms. Often this purpose is the production of some valuable bioproduct. This review seeks to give overview the advances made on both the model construction and application ends, with particular emphasis on model improvements via more complex constraints and the incorporation of experimental data.

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Subject

computational biology
genome model
metabolic flux
flux balance analysis
bioproduction
metabolic engineering

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