Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale metabolic models. We generate an ensemble of models, each equally consistent with experimental data, then perform simulations with them. We apply machine learning to the simulation output to identify model structure variation that maximally influences simulations. These variants are high-priority candidates for curation through removal, addition, or reannotation in the model. We apply this approach, automated metabolic model ensemble-driven elimination of uncertainty with statistical learning (AMMEDEUS), to 29 bacterial species to improve gene essentiality predictions. We explore targets for individual species and compile pan-species targets to improve the database used during model construction. AMMEDEUS is an automated and performance-driven recommendation system that complements intuition during curation of biochemical knowledgebases.

机制模型明确地代表了假设的生物学知识。因此，它们比数据驱动模型具有更强的通用性。然而，确定提高机械模型性能的模型管理工作并非易事。在这里，我们为基因组规模的代谢模型开发了解决这个问题的方法。我们生成一组模型，每个模型都与实验数据一致，然后用它们进行模拟。我们将机器学习应用于仿真输出，以识别对仿真影响最大的模型结构变化。这些变体是通过在模型中删除、添加或重新注释进行管理的高优先级候选者。我们将这种方法（自动化代谢模型集成驱动的通过统计学习消除不确定性 (AMMEDEUS)）应用于 29 个细菌物种，以改进基因重要性预测。我们探索单个物种的目标并编制泛物种目标，以改进模型构建过程中使用的数据库。 AMMEDEUS 是一种自动化且性能驱动的推荐系统，可在生化知识库的管理过程中补充直觉。