A Gene Ranking Framework Enhances the Design Efficiency of Genome-Scale Constraint-Based Metabolic Networks under Time Limits

TL;DR

This paper introduces a gene ranking framework that significantly improves the success rate and efficiency of designing genome-scale constraint-based metabolic networks within time constraints.

Contribution

The study proposes a novel gene importance ranking method that accelerates MILP solutions and enhances success rates in metabolic network design.

Findings

Achieved a 37% to 186% increase in success rate within the same time limits.

Reduced subproblem sizes and branch-and-bound nodes in MILP solutions.

Recovered most successful cases of the original approach.

Abstract

The design of genome-scale constraint-based metabolic networks has steadily advanced, with an increasing number of successful cases achieving growth-coupled production, in which the biosynthesis of key metabolites is linked to cell growth. However, a major cause of design failures is the inability to find solutions within realistic time limits. Therefore, it is essential to develop methods that achieve a high success rate within the specified computation time. In this study, we propose a framework for ranking the importance of individual genes to accelerate the solution of the original mixed-integer linear programming (MILP) problems in the design of constraint-based models. In the proposed method, after pre-assigning values to highly important genes, the MILPs are solved in parallel as a series of mutually exclusive subproblems. It is found that our framework was able to recover most of the successful cases identified by the original approach and achieved a 37% to 186% increase in success rate compared to the original method within the same time limits. Analysis of the MILP solution process revealed that the proposed method reduced the sizes of subproblems and decreased the number of nodes in the branch-and-bound tree. This framework for ranking gene importance can be directly applicable to a range of MILP-based algorithms for the design of constraint-based metabolic networks. The developed scripts are available on \href{https://github.com/MetNetComp/Gene-Ranked-RatGene}{https://github.com/MetNetComp/Gene-Ranked-RatGene}.