(288b) OptORF: Optimal Metabolic and Regulatory Gene Knockouts for Metabolic Engineering of Microbial Strains

Kim, J., University of Wisconsin-Madison
Reed, J. L., University of Wisconsin-Madison

Metabolic engineering has emerged as an important field aimed to improve cellular production of valuable biochemicals and biofuels. Conventional metabolic engineering approaches focus on metabolic branch points, and eliminate undesired reactions in competing branches and enhance flux through desired reactions using genetic modifications. However, these metabolic network modifications will affect not only local metabolic pathways, but will also have global effects on metabolic behavior due to changes in carbon, energy, and electron flows. Correspondingly, such conventional approaches may miss modifications in distant pathways that can potentially improve the cellular function of interest.

Currently available computational methods for metabolic engineering are often based on reaction deletions (not gene deletions), and do not account for the regulatory networks that control metabolism. Generally, there is not a one to one mapping between genes and reactions due to the presence of multi-functional enzymes, enzyme subunits, and/or isozymes. Thus, computational designs based on reaction deletions instead of gene deletions can sometimes result in strategies that are not genetically feasible, such as the deletion of essential genes, or result in more complicated strategies requiring multiple gene deletions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies, based on metabolic and transcription factor gene deletions, which couple biomass and biochemical production.

Here, we propose a systematic integration of GPR association and transcriptional regulatory constraints. This allows for the formulation of optimization problems that search for metabolic or regulatory gene deletions (rather than reaction deletions) that lead to the coupling of biomass and biochemical production, thus proposing adaptive evolutionary strain designs. Using genome-scale metabolic and regulatory models of Escherichia coli, we have implemented the OptORF algorithm (which considers gene deletions) and compared its metabolic engineering strategies to those found using OptKnock (which considers reaction deletions) for ethanol production. The developed OptORF approach can be used to design mutant strains, which after adaptive evolution should have enhanced biochemical production. While evaluated here for ethanol production in E. coli, the approach can easily be applied to the production of other compounds in other biological systems to propose metabolic and/or regulatory gene deletion strategies for biochemical production.