The use of in silico methods has become standard practice to correlate the structure of a biochemical network to the expression of a desired phenotype. Flux balance analysis (FBA) is one of the most prevalent techniques for modeling cellular metabolism. FBA models range from genome-scale reconstructions to simple minimal reaction sets, and have been successfully applied to obtain predictions of growth, theoretical product yields from heterologous pathways, and location of engineering targets to maximize product yield or design antibiotics. We take inspiration from high-throughput recombineering techniques, which show that combinatorial exploration can reveal optimal mutants, and apply the advantages of computational techniques to analyze these combinations. We introduce Constrictor, an in silico tool for flux balance analysis that allows gene mutations to be analyzed in a combinatorial fashion, by applying simulated constraints accounting for up- and down-regulation of gene expression. We demonstrate the ability to “scan” networks for valuable products, then apply Constrictor to study ethylene production in Escherichia coli through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as sets of finite numbers of reactions results in theoretical ethylene yields that are 25% greater than yields in the baseline strain during simulated exponential growth. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels, and troubleshoot metabolic networks.

Constrictor: Flux balance analysis constraint modification provides insight for design of biochemical networks

249th National Meeting (2015)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.