COBRAme: A Computational Framework for Building and Manipulating Models of Metabolism and Gene Expression

TitleCOBRAme: A Computational Framework for Building and Manipulating Models of Metabolism and Gene Expression
Publication TypeJournal Article
Year of Publication2017
AuthorsLloyd CJ, Ebrahim A, Yang L, King ZA, Catoiu E, O'Brien EJ, Liu JK, Palsson BO
JournalbioRxiv
PubMed Date02/2017
Abstract

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 new and exciting insights that are fundamental to understanding the basis 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 the construction and manipulation of ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iLE1678-ME. This new model gives virtually identical solutions to previous ME-models while using 1/4 the number of free variables and solving in ~10 minutes, a marked improvement over the ~6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be built and edited most efficiently using the software.

URLhttps://doi.org/10.1101/106559
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