COBRAme: A computational framework for genome-scale models of metabolism and gene expression

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.基因组规模代谢与大分子表达模型（ME-models）可显式计算增殖细胞的最优蛋白质组组成。ME-models是在成熟的基因组规模代谢模型（M-models）基础上拓展而来，能够为细胞生长机制带来全新的基础性认知。由于纳入了生命运作系统的生物合成成本，ME-models的预测能力与精度均得到提升，但同时也导致模型规模与复杂度显著提升。这一问题使得此类模型不仅计算难度高，还难以从概念层面理解。因此，目前仅存在两种生物的ME-models，即大肠杆菌（Escherichia coli）与海栖热袍菌（Thermotoga maritima），且相关研究的使用者仍相对较少。为解决上述挑战，我们开发了一款名为COBRAme的全新软件框架，用于构建与模拟ME-models。该框架基于主流基因组规模代谢模型平台COBRApy开发，采用Python语言编写。COBRAme可简化ME-models的计算与分析流程，提供了便捷构建与编辑ME-models的工具，从而支持针对新生物的ME-model重构工作。我们借助COBRAme重构了一款精简版大肠杆菌ME-model，命名为iJL1678b-ME。相较于此前的大肠杆菌ME-models，该重构模型可输出功能一致的求解结果，但其自由变量数量仅为前者的1/6，求解时长也缩短至10分钟以内，相较于此前ME-models需6小时的求解时长，实现了显著优化。此外，本研究还修正了此前ME-models中存在的错误，使得iJL1678b-ME在有氧条件下于葡萄糖最小化硅基模拟（in silico）培养基中生长时，需额外表达52个基因。本文阐述了COBRAme的架构，并展示了如何借助该全新软件框架高效创建、修改与共享ME-models。