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dc.contributor.authorHunt, Kristopher A.
dc.contributor.authorFolsom, James P.
dc.contributor.authorTaffs, Reed L.
dc.contributor.authorCarlson, Ross P.
dc.date.accessioned2016-12-05T18:06:04Z
dc.date.available2016-12-05T18:06:04Z
dc.date.issued2014-06
dc.identifier.citationHunt KA, Folsom JP, Taffs RL, Carlson RP, "Complete enumeration of elementary flux modes through scalable demand-based subnetwork definition," Bioinformatics. June 2014 30(11): 1569–78.en_US
dc.identifier.issn1367-4803
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/12306
dc.description.abstractMotivation: Elementary flux mode analysis (EFMA) decomposes complex metabolic network models into tractable biochemical pathways, which have been used for rational design and analysis of metabolic and regulatory networks. However, application of EFMA has often been limited to targeted or simplified metabolic network representations due to computational demands of the method. Results: Division of biological networks into subnetworks enables the complete enumeration of elementary flux modes (EFMs) for metabolic models of a broad range of complexities, including genome-scale. Here, subnetworks are defined using serial dichotomous suppression and enforcement of flux through model reactions. Rules for selecting appropriate reactions to generate subnetworks are proposed and tested; three test cases, including both prokaryotic and eukaryotic network models, verify the efficacy of these rules and demonstrate completeness and reproducibility of EFM enumeration. Division of models into subnetworks is demand-based and automated; computationally intractable subnetworks are further divided until the entire solution space is enumerated. To demonstrate the strategy’s scalability, the splitting algorithm was implemented using an EFMA software package (EFMTool) and Windows PowerShell on a 50 node Microsoft high performance computing cluster. Enumeration of the EFMs in a genome-scale metabolic model of a diatom, Phaeodactylum tricornutum, identified ~2 billion EFMs. The output represents an order of magnitude increase in EFMs computed compared with other published algorithms and demonstrates a scalable framework for EFMA of most systems.en_US
dc.titleComplete enumeration of elementary flux modes through scalable demand-based subnetwork definitionen_US
dc.typeArticleen_US
mus.citation.extentfirstpage1569en_US
mus.citation.extentlastpage1578en_US
mus.citation.issue11en_US
mus.citation.journaltitleBioinformaticsen_US
mus.citation.volume30en_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1093/bioinformatics/btu021en_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.collegeCollege of Letters & Scienceen_US
mus.relation.departmentCenter for Biofilm Engineering.en_US
mus.relation.departmentChemical & Biological Engineering.en_US
mus.relation.departmentChemical Engineering.en_US
mus.relation.departmentChemistry & Biochemistry.en_US
mus.relation.departmentCivil Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.relation.researchgroupCenter for Biofilm Engineering.en_US
mus.data.thumbpage4en_US
mus.contributor.orcidFolsom, James P.|0000-0002-4586-4086en_US


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