OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions

• Published in: PLoS computational biology Vol. 6; no. 4; p. e1000744
• Main Authors: Ranganathan, Sridhar, Suthers, Patrick F, Maranas, Costas D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2010; Public Library of Science (PLoS)
• Subjects: Algorithms; Biochemistry/Bioinformatics; Biomass; Biotechnology/Bioengineering; Chemical properties; Computational Biology/Metabolic Networks; Computational Biology/Systems Biology; Computer Simulation; E coli; Escherichia coli; Escherichia coli - genetics; Escherichia coli - metabolism; Experiments; Gene expression; Gene Expression Regulation; Genetic aspects; Genetic engineering; Genetic Engineering - methods; Health aspects; Mathematical models; Mathematical optimization; Metabolic Networks and Pathways; Microbiology; Models, Genetic; Models, Statistical; Studies; Succinates; Succinic Acid - metabolism; Systems Biology - methods; United States; Algorithms; Genetic Engineering; Computer Simulation; Escherichia coli; Gene Expression Regulation; Metabolic Networks & Pathways; Models, Genetic; Succinic Acid; Models, Statistical; Systems Biology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000744

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis.