Optimization approaches for the in silico discovery of optimal targets for gene over/underexpression

摘要

Metabolic engineering (ME) efforts have been recently boosted by the increase in thenumber of annotated genomes and by the development of several genome-scale metabolicmodels for microbes of interest in industrial biotechnology. Based on these efforts, strainoptimization methods have been proposed to reach the best set of genetic changes to apply toselected host microbes, in order to create strains that are able to overproduce metabolites ofindustrial interest. Previous work in strain optimization has been mostly based in findingsets of gene (or reaction) deletions that lead to desired phenotypes in computational simulations.In this work, we focus on enlarging the set of possible genetic changes, consideringgene over and underexpression. A gene is considered under (over) expressed if its expressionvalue is constrained to be significantly lower (higher) than the one in the wild-type strain,used as a reference. A method is proposed to propagate relative gene expression valuesto flux constraints over related reactions, making use of the available transcriptional/translational information. The algorithms chosen for the optimization tasks are metaheuristicssuch as eolutionary agorithm (EA) and smulated anealing (SA), based on previoussuccessful work on gene knockout optimization. These methods were modified appropriatelyto accommodate the novel optimization tasks and were applied to study the optimizationof succinic and lactic acid production using Escherichia coli as the host. The results arecompared with previous ones obtained in gene knockout optimization, thus showing theusefulness of the approach. The methods proposed in this work were implemented in a novelplug-in for OptFlux, an open-source software framework for ME. Supplementary Materialis available at www.liebertonline.com/cmb.