Flux balance analysis of metabolic models: a review of recent advances and applications
| dc.contributor.author | Estep, Forrest Earnest, author | |
| dc.contributor.author | Peebles, Christie, advisor | |
| dc.contributor.author | Shipman, Patrick, committee member | |
| dc.contributor.author | Snow, Chris, committee member | |
| dc.date.accessioned | 2016-07-13T14:50:14Z | |
| dc.date.available | 2016-07-13T14:50:14Z | |
| dc.date.issued | 2016 | |
| dc.description.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. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Estep_colostate_0053N_13443.pdf | |
| dc.identifier.uri | http://hdl.handle.net/10217/173463 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | computational biology | |
| dc.subject | genome model | |
| dc.subject | metabolic flux | |
| dc.subject | flux balance analysis | |
| dc.subject | bioproduction | |
| dc.subject | metabolic engineering | |
| dc.title | Flux balance analysis of metabolic models: a review of recent advances and applications | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Chemical and Biological Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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