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COBRAme: A computational framework for genome-scale models of metabolism and gene expression
- Source :
- PLoS computational biology, vol 14, iss 7, PLoS Computational Biology, Vol 14, Iss 7, p e1006302 (2018), Lloyd, C J, Ebrahim, A, Yang, L, King, Z A, Catoiu, E, O'Brien, E J, Liu, J K & Polsson, B O 2018, ' COBRAme: A computational framework for genome-scale models of metabolism and gene expression ', P L o S Computational Biology (Online), vol. 14, no. 7, e1006302 . https://doi.org/10.1371/journal.pcbi.1006302, PLoS Computational Biology
- Publication Year :
- 2018
- Publisher :
- eScholarship, University of California, 2018.
-
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 i JL1678b-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 i JL1678b-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.
- Subjects :
- Metabolic Processes
0301 basic medicine
Theoretical computer science
Computer science
Enzyme Metabolism
Cell
Gene Expression
02 engineering and technology
computer.software_genre
Biochemistry
Genome
Mathematical Sciences
0302 clinical medicine
Models
Software Design
Gene expression
Enzyme Chemistry
lcsh:QH301-705.5
computer.programming_language
0303 health sciences
Basis (linear algebra)
Ecology
Biological Sciences
Enzymes
Chemistry
medicine.anatomical_structure
Macromolecules
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Proteome
Free variables and bound variables
Software design
Algorithms
Research Article
Macromolecule
Biotechnology
Cell Physiology
Bioinformatics
Systems biology
Computation
In silico
DNA transcription
0206 medical engineering
Bioengineering
03 medical and health sciences
Cellular and Molecular Neuroscience
Genetic
Information and Computing Sciences
medicine
Genetics
Computer Simulation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Models, Genetic
Cell growth
Biology and Life Sciences
Proteins
Cell Biology
Metabolism
Python (programming language)
Polymer Chemistry
Expression (mathematics)
Cell Metabolism
Software framework
030104 developmental biology
lcsh:Biology (General)
Enzymology
Protein Translation
Generic health relevance
computer
030217 neurology & neurosurgery
020602 bioinformatics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- PLoS computational biology, vol 14, iss 7, PLoS Computational Biology, Vol 14, Iss 7, p e1006302 (2018), Lloyd, C J, Ebrahim, A, Yang, L, King, Z A, Catoiu, E, O'Brien, E J, Liu, J K & Polsson, B O 2018, ' COBRAme: A computational framework for genome-scale models of metabolism and gene expression ', P L o S Computational Biology (Online), vol. 14, no. 7, e1006302 . https://doi.org/10.1371/journal.pcbi.1006302, PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....0b4b967874284cbbb65fa6fa417a6980