From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model
Abstract
Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Pythonbased open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe’s metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.
- Authors:
-
- San Diego State Univ., CA (United States). Computational Science Research Center
- Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
- Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
- San Diego State Univ., CA (United States). Dept. of Computer Science
- San Diego State Univ., CA (United States). Computational Science Research Center; San Diego State Univ., CA (United States). Dept. of Computer Science; San Diego State Univ., CA (United States). Biological and Medical Informatics Research Center; San Diego State Univ., CA (United States). Dept. of Biology
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1628146
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Microbiology
- Additional Journal Information:
- Journal Volume: 7; Journal ID: ISSN 1664-302X
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Microbiology; metabolic modeling; metabolic reconstruction; in silico modeling; flux-balance analysis; model SEED; genome annotation
Citation Formats
Cuevas, Daniel A., Edirisinghe, Janaka, Henry, Chris S., Overbeek, Ross, O’Connell, Taylor G., and Edwards, Robert A. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. United States: N. p., 2016.
Web. doi:10.3389/fmicb.2016.00907.
Cuevas, Daniel A., Edirisinghe, Janaka, Henry, Chris S., Overbeek, Ross, O’Connell, Taylor G., & Edwards, Robert A. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. United States. https://doi.org/10.3389/fmicb.2016.00907
Cuevas, Daniel A., Edirisinghe, Janaka, Henry, Chris S., Overbeek, Ross, O’Connell, Taylor G., and Edwards, Robert A. Fri .
"From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model". United States. https://doi.org/10.3389/fmicb.2016.00907. https://www.osti.gov/servlets/purl/1628146.
@article{osti_1628146,
title = {From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model},
author = {Cuevas, Daniel A. and Edirisinghe, Janaka and Henry, Chris S. and Overbeek, Ross and O’Connell, Taylor G. and Edwards, Robert A.},
abstractNote = {Microbiological studies are increasingly relying on in silico methods to perform exploration and rapid analysis of genomic data, and functional genomics studies are supplemented by the new perspectives that genome-scale metabolic models offer. A mathematical model consisting of a microbe’s entire metabolic map can be rapidly determined from whole-genome sequencing and annotating the genomic material encoded in its DNA. Flux-balance analysis (FBA), a linear programming technique that uses metabolic models to predict the phenotypic responses imposed by environmental elements and factors, is the leading method to simulate and manipulate cellular growth in silico. However, the process of creating an accurate model to use in FBA consists of a series of steps involving a multitude of connections between bioinformatics databases, enzyme resources, and metabolic pathways. We present the methodology and procedure to obtain a metabolic model using PyFBA, an extensible Pythonbased open-source software package aimed to provide a platform where functional annotations are used to build metabolic models (http://linsalrob.github.io/PyFBA). Backed by the Model SEED biochemistry database, PyFBA contains methods to reconstruct a microbe’s metabolic map, run FBA upon different media conditions, and gap-fill its metabolism. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models.},
doi = {10.3389/fmicb.2016.00907},
journal = {Frontiers in Microbiology},
number = ,
volume = 7,
place = {United States},
year = {Fri Jun 17 00:00:00 EDT 2016},
month = {Fri Jun 17 00:00:00 EDT 2016}
}
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