Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America.
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
PLoS Comput Biol. 2018 Jul 5;14(7):e1006302. doi: 10.1371/journal.pcbi.1006302. eCollection 2018 Jul.
Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable a new fundamental understanding of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives functionally identical solutions to previous E. coli ME-models while using 1/6 the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. Errors in previous ME-models were also corrected leading to 52 additional genes that must be expressed in iJL1678b-ME to grow aerobically in glucose minimal in silico media. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be created, modified, and shared most efficiently using the new software framework.
基因组规模的代谢和大分子表达模型（ME 模型）明确计算了生长细胞的最佳蛋白质组组成。ME 模型扩展了成熟的基因组规模代谢模型（M 模型），并使人们对细胞生长有了新的基本理解。由于包含了生命机器的生物合成成本，ME 模型具有更高的预测能力和准确性，但模型大小和复杂性也显著增加。这一挑战导致模型既难以计算，又难以从概念上理解。因此，目前只有两种生物体（大肠杆菌和海洋栖热菌）拥有 ME 模型，而且相对较少的研究人员在使用它们。为了解决这些挑战，我们开发了一个名为 COBRAme 的新软件框架，用于构建和模拟 ME 模型。它是用 Python 编写的，并建立在 COBRApy 之上，COBRApy 是使用 M 模型的流行平台。COBRAme 简化了 ME 模型的计算和分析。它提供了简化构建和编辑 ME 模型的工具，使新生物体的 ME 模型重建成为可能。我们使用 COBRAme 重建了一个简化的大肠杆菌 ME 模型，称为 iJL1678b-ME。与之前的大肠杆菌 ME 模型相比，这个重新公式化的模型使用了六分之一数量的自由变量，求解时间不到 10 分钟，而之前的 ME 模型求解时间需要 6 小时，这是一个显著的改进。之前 ME 模型中的错误也得到了纠正，导致在 iJL1678b-ME 中必须表达 52 个额外的基因，才能在葡萄糖最小的计算机模拟培养基中进行好氧生长。本文概述了 COBRAme 的架构，并展示了如何使用新的软件框架最有效地创建、修改和共享 ME 模型。
