Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
Department of Bioengineering, University of California, San Diego, La Jolla, San Diego, CA 92093, USA.
Cell Syst. 2021 Sep 22;12(9):842-859. doi: 10.1016/j.cels.2021.06.005.
Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.
基因组规模的代谢模型（GEMs）是代谢网络系统水平研究的关键计算工具。在这里，我们描述了“GEM 生命周期”，我们将其细分为四个阶段：初始阶段、成熟阶段、专业化阶段和合并阶段。我们展示了不同类型的 GEM 重建工作流程如何适应每个阶段，并进一步强调了提高 GEM 质量的两个基本瓶颈：GEM 成熟和内容去除。我们从过去独立的 GEM 成熟工作中汲取经验，确定了有助于提高成熟 GEM 质量的共同特征。然后，我们揭示了内容去除这一潜在且未被认识但普遍存在的问题，展示了模型修剪对其解决方案空间的重大影响。最后，我们提出了一种新的内容去除框架及其相关置信度分配，这将有助于指导未来的 GEM 开发工作，减少不同小组之间的重复工作，可能有助于自动化重建平台，并提高模型开发的可重复性。
