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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 inmore » 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.« less

Authors:
ORCiD logo; ; ORCiD logo; ; ; ; ORCiD logo; ORCiD logo;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1460606
Alternate Identifier(s):
OSTI ID: 1459050; OSTI ID: 1544029
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online) Journal Volume: 14 Journal Issue: 7; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science (PLoS)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Mathematical & Computational Biology

Citation Formats

Lloyd, Colton J., Ebrahim, Ali, Yang, Laurence, King, Zachary A., Catoiu, Edward, O’Brien, Edward J., Liu, Joanne K., Palsson, Bernhard O., and Darling, ed., Aaron E. COBRAme: A computational framework for genome-scale models of metabolism and gene expression. United States: N. p., 2018. Web. doi:10.1371/journal.pcbi.1006302.
Lloyd, Colton J., Ebrahim, Ali, Yang, Laurence, King, Zachary A., Catoiu, Edward, O’Brien, Edward J., Liu, Joanne K., Palsson, Bernhard O., & Darling, ed., Aaron E. COBRAme: A computational framework for genome-scale models of metabolism and gene expression. United States. doi:10.1371/journal.pcbi.1006302.
Lloyd, Colton J., Ebrahim, Ali, Yang, Laurence, King, Zachary A., Catoiu, Edward, O’Brien, Edward J., Liu, Joanne K., Palsson, Bernhard O., and Darling, ed., Aaron E. Thu . "COBRAme: A computational framework for genome-scale models of metabolism and gene expression". United States. doi:10.1371/journal.pcbi.1006302.
@article{osti_1460606,
title = {COBRAme: A computational framework for genome-scale models of metabolism and gene expression},
author = {Lloyd, Colton J. and Ebrahim, Ali and Yang, Laurence and King, Zachary A. and Catoiu, Edward and O’Brien, Edward J. and Liu, Joanne K. and Palsson, Bernhard O. and Darling, ed., Aaron E.},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1006302},
journal = {PLoS Computational Biology (Online)},
number = 7,
volume = 14,
place = {United States},
year = {2018},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1371/journal.pcbi.1006302

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