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Cuevas DA, Edirisinghe J, Henry CS, et al. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. Front Microbiol. 2016;7:907doi: 10.3389/fmicb.2016.00907.
Cuevas, D. A., Edirisinghe, J., Henry, C. S., Overbeek, R., O'Connell, T. G., & Edwards, R. A. (2016). From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. Frontiers in microbiology, 7907. https://doi.org/10.3389/fmicb.2016.00907
Cuevas, Daniel A, et al. "From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model." Frontiers in microbiology vol. 7 (2016): 907. doi: https://doi.org/10.3389/fmicb.2016.00907
Cuevas DA, Edirisinghe J, Henry CS, Overbeek R, O'Connell TG, Edwards RA. From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model. Front Microbiol. 2016 Jun 17;7:907. doi: 10.3389/fmicb.2016.00907. eCollection 2016. PMID: 27379044; PMCID: PMC4911401.
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Front Microbiol. 2016 Jun 17;7:907. doi: 10.3389/fmicb.2016.00907. eCollection 2016.

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.

Frontiers in microbiology

Daniel A Cuevas, Janaka Edirisinghe, Chris S Henry, Ross Overbeek, Taylor G O'Connell, Robert A Edwards

Affiliations

  1. Computational Science Research Center, San Diego State University, San Diego CA, USA.
  2. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne IL, USA.
  3. Fellowship for Interpretation of Genomes, Burr Ridge IL, USA.
  4. Biological and Medical Informatics Research Center, San Diego State University, San Diego CA, USA.
  5. Computational Science Research Center, San Diego State University, San DiegoCA, USA; Biological and Medical Informatics Research Center, San Diego State University, San DiegoCA, USA; Department of Computer Science, San Diego State University, San DiegoCA, USA; Department of Biology, San Diego State University, San DiegoCA, USA.

PMID: 27379044 PMCID: PMC4911401 DOI: 10.3389/fmicb.2016.00907

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 Python-based 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.

Keywords: flux-balance analysis; genome annotation; in silico modeling; metabolic modeling; metabolic reconstruction; model SEED

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