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 lessmore » 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:

Lloyd, Colton J. ;

• Search DOE PAGES for author "Lloyd, Colton J."
• Search DOE PAGES for ORCID "0000-0001-6556-6345"

Ebrahim, Ali; Yang, Laurence ;

• Search DOE PAGES for author "Yang, Laurence"
• Search DOE PAGES for ORCID "0000-0001-6663-7643"

King, Zachary A.; Catoiu, Edward; O’Brien, Edward J.; Liu, Joanne K. ;

• Search DOE PAGES for author "Liu, Joanne K."
• Search DOE PAGES for ORCID "0000-0001-9973-524X"

Palsson, Bernhard O. ;

• Search DOE PAGES for author "Palsson, Bernhard O."
• Search DOE PAGES for ORCID "0000-0003-2357-6785"

Darling, ed., Aaron E.

Publication Date:

Thu Jul 05 00:00:00 EDT 2018

Research Org.:

Lawrence Berkeley National Laboratory (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