A Graph Theory Augmented Math ProgrammingApproach to Identify Genetic Targets for StrainImprovement

摘要

Improvement of biological strains through targeted modification of metabolism isessential for successful development of bioprocesses. The computational complexity ofoptimization procedures routinely used for identifying genetic targets limits theirapplication to genome-scale metabolic networks. In this study, we combined graphtheoretic approaches with mixed-integer liner programming (MILP) to reduce the searchspace and thus reducing computational time. Specifically, we used cut-sets (minimal setof reactions that cuts metabolic networks) as additional constraints to reduce the searchspace. The efficacy of proposed approach is illustrated by identifying minimal reactionset for Saccharomyces Cerevisiae.