![]() ![]() Genome-scale metabolic networks have the potential to completely change our perspective of microbial genomics and of the meaning inferred from a genome sequence ( Oberhardt et al., 2011 Plata et al., 2015 Yurkovich and Palsson, 2016). The metabolic summary of a genome was limited to a few tables of higher metabolic categories. However, that information was usually presented in absentia the biochemical network that it purports to describe. Since the dawn of genomics, homology-based algorithms and annotation databases have been used to infer meaning from raw sequences ( Overbeek et al., 2000, 2003 Aziz et al., 2008), and papers describing microbial genomes have summarized the number of metabolic genes and breakdowns of their potential capacity. The extensibility of PyFBA facilitates novel techniques in creating accurate genome-scale metabolic models. 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. 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 ( ). 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. 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. 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. 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.
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