RatGene: Gene deletion-addition algorithms using growth to production ratio for growth-coupled production in constraint-based metabolic networks

TL;DR

RatGene is a novel algorithm that efficiently identifies gene deletion and addition strategies to enhance growth-coupled production in metabolic networks, addressing computational challenges in genome-scale models.

Contribution

It introduces a growth-to-production ratio-based method, proves NP-hardness, and automates the integration of multiple networks for metabolic design.

Findings

Significantly improves success rate of identifying gene strategies

Automates integration of multiple constraint-based networks

Reduces redundancy in gene modification strategies

Abstract

In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled production, where cell growth forces target metabolite production. However, in genome-scale models, finding strategies to simultaneously delete and add genes to induce growth-coupled production is challenging. This is particularly true when heavy computation is necessary due to numerous gene deletions and additions. In this study, we mathematically defined related problems, proved NP-hardness and/or NP-completeness, and developed an algorithm named RatGene that (1) automatically integrates multiple constraint-based metabolic networks, (2) identifies gene deletion-addition strategies by a growth-to-production ratio-based approach, and (3) eliminates redundant gene additions and deletions. The results of computational experiments demonstrated that the RatGene-based approach can significantly improve the success ratio for identifying the strategies for growth-coupled production. RatGene can facilitate a more rational approach to computational metabolic design for the production of useful substances using microorganisms by concurrently considering both gene deletions and additions.