Paper Info
Title
COBRAme: A computational framework for genome-scale models of metabolism and gene expression.
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 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.
Year
DOI
Venue
2018
10.1371/journal.pcbi.1006302
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Genome,Biology,Free variables and bound variables,Theoretical computer science,Proteome,Genetics,Thermotoga maritima,Software framework,Python (programming language),In silico,Computation
Journal
14
Issue
ISSN
Citations 
7
1553-734X
4
PageRank 
References 
Authors
0.45
11
8
Name
Order
Citations
PageRank
Colton J Lloyd152.51
Ali Ebrahim2604.31
Laurence Yang392.75
Zachary A. King4604.97
Edward Catoiu541.46
Edward J. O'Brien640.79
Joanne K. Liu7121.38
Bernhard O. Palsson875167.99