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

2024-11-12

arXiv.org

AI Summary

Designing growth-coupled gene knockout strategies for genome-scale metabolic models (GEMs) entails prohibitively high computational complexity. Method: This paper proposes a database-driven hybrid optimization framework that integrates multi-source biological prior knowledge—such as KEGG and BioCyc—to construct constraint-based flux balance analysis (FBA) models, and synergistically combines heuristic pruning with mixed-integer linear programming (MILP). Contribution/Results: The approach pioneers the systematic embedding of structured biological knowledge directly into the optimization pipeline, overcoming the efficiency limitations of conventional purely algorithmic search methods. Evaluated across multiple target metabolites, it achieves an average 6.1× speedup while maintaining high solution success rates. Notably, it enables real-time generation of feasible, growth-coupled knockout strategies on full genome-scale models—a first in the field.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Gene Deletion
• Cell Growth
• Metabolic Engineering

Innovation

• DBgDel
• Computational Gene Deletion
• Efficient Bioproduction Optimization