Computing optimal factories in metabolic networks with negative regulation

Author:

Krieger Spencer1,Kececioglu John1

Affiliation:

1. Department of Computer Science, The University of Arizona , Tucson, AZ 85721, USA

Abstract

Abstract Motivation 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.

Funder

US National Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference29 articles.

1. Algorithms and complexity of enumerating minimal precursor sets in genome-wide metabolic networks;Acuña;Bioinformatics,2012

2. Enumeration of minimal stoichiometric precursor sets in metabolic networks;Andrade;Algorithms Mol. Biol,2016

3. Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms;Carbonell;BMC Syst. Biol,2012

4. MetExplore: collaborative edition and exploration of metabolic networks;Cottret;Nucleic Acids Res,2018

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