From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model
| Autor: | Janaka N. Edirisinghe, Ross Overbeek, Christopher S. Henry, Taylor O'Connell, Daniel A. Cuevas, Robert Edwards |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Microbiology (medical) genome annotation Process (engineering) In silico 0206 medical engineering 02 engineering and technology Computational biology Biology computer.software_genre Microbiology in silico modeling 03 medical and health sciences metabolic modeling metabolic reconstruction Methods computer.programming_language 2. Zero hunger Genome project Python (programming language) Flux balance analysis Metabolic network modelling Metabolic pathway flux-balance analysis 030104 developmental biology Data mining model SEED computer Functional genomics 020602 bioinformatics |
| Zdroj: | Frontiers in Microbiology |
| ISSN: | 1664-302X |
| DOI: | 10.3389/fmicb.2016.00907 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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