Computing optimal factories in metabolic networks with negative regulation
AffiliationDepartment of Computer Science, University of Arizona
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CitationKrieger, S., & Kececioglu, J. (2022). Computing optimal factories in metabolic networks with negative regulation. Bioinformatics (Oxford, England), 38(1), i369–i377.
JournalBioinformatics (Oxford, England)
RightsCopyright © The Author(s) 2022. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
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AbstractMOTIVATION: A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation. METHODS: We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories. RESULTS: This optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases. AVAILABILITY AND IMPLEMENTATION: Source code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu. © The Author(s) 2022. Published by Oxford University Press.
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VersionFinal published version
Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2022. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).