CASOP GS: computing intervention strategies targeted at production improvement in genome-scale metabolic networks

Abstract

Metabolic engineering aims to improve the production of desired biochemicals and proteins in organisms and therefore, plays a central role in Biotechnology. However, the design of overproducing strains is not straightforward due to the complexity of metabolic and regulatory networks. Thus, theoretical tools supporting the design of such strains have been developed. One particular method, CASOP, uses the set of elementary flux modes (EFMs) of a reaction network to propose strategies for the overproduction of a target compound. The advantage of CASOP over other approaches is that it does not consider a single specific flux distribution within the network but the whole set of possible flux distributions represented by the EFMs of the network. Moreover, its application results not only in the identification of candidate loci that can be knocked out, but additionally proposes overexpression candidates. However, the utilization of CASOP was restricted to small and medium scale metabolic networks so far, since the entire set of EFMs cannot be enumerated in such networks. This work presents an approach that allows to use CASOP even in genome-scale networks. This approach is based on an estimation of the score utilized in CASOP through a sample of EFMs within a genome-scale network. Using EFMs from the genome-scale metabolic network gives a more reliable picture of the metabolic capabilities of an organism required for the design of overproducing strains. We applied our new method to identify strategies for the overproduction of succinate and histidine in Escherichia coli. The succinate case study, in particular, proposes engineering targets which resemble known strategies already applied in E. coli. Availability: Source code and an executable are available upon request.