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.

翻译：在使用基因组尺度约束基代谢模型模拟代谢物生产时，基因删除策略是实现生长耦合生产所必需的，这意味着细胞生长与目标代谢物生产同时发生。由于为大型基因组尺度模型获取基因删除策略需要耗费大量计算时间，因此有必要开发方法来减轻这种计算负担。在本研究中，我们引入了一种计算基因删除策略的新框架。所提出的框架首先挖掘相关数据库，以提取关于生长耦合生产的基因删除先验信息。随后，它将提取的信息与下游算法集成，以缩小算法搜索空间，从而在基因组尺度模型上实现高效计算。计算实验结果表明，我们的框架能够为众多目标代谢物计算出化学计量可行的基因删除策略，并在计算效率上展现出显著的提升。具体而言，与现有方法相比，我们的框架在保持可观成功率的同时，实现了平均6.1倍的计算速度提升。DBgDel的源代码及示例可在 https://github.com/MetNetComp/DBgDel 获取。