DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic Models

TL;DR

DBgDel is a new computational framework that efficiently identifies gene deletion strategies for growth-coupled metabolite production in genome-scale models, significantly reducing computation time while maintaining success rates.

Contribution

It introduces a database-informed, integrated approach that accelerates gene deletion strategy computation in large metabolic models.

Findings

Achieves an average 6.1-fold speedup over existing methods.

Successfully computes gene deletions for numerous target metabolites.

Maintains high success rate despite increased efficiency.

Abstract

When simulating metabolite productions with genome-scale constraint-based metabolic models, gene deletion strategies are necessary to achieve growth-coupled production, which means cell growth and target metabolite production occur simultaneously. Since obtaining gene deletion strategies for large genome-scale models suffers from significant computational time, it is necessary to develop methods to mitigate this computational burden. In this study, we introduce a novel framework for computing gene deletion strategies. The proposed framework first mines related databases to extract prior information about gene deletions for growth-coupled production. It then integrates the extracted information with downstream algorithms to narrow down the algorithmic search space, resulting in highly efficient calculations on genome-scale models. Computational experiment results demonstrated that our framework can compute stoichiometrically feasible gene deletion strategies for numerous target metabolites, showcasing a noteworthy improvement in computational efficiency. Specifically, our framework achieves an average 6.1-fold acceleration in computational speed compared to existing methods while maintaining a respectable success rate. The source code of DBgDel with examples are available on https://github.com/MetNetComp/DBgDel.