Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods

By Nathan E. Lewis, Harish Nagarajan, Bernhard O. Palsson. Nature Reviews Microbiology, 2012

804 citations recorded by the source corpus.

Reconstructed microbial metabolic networks facilitate a mechanistic description of the genotype–phenotype relationship through the deployment of constraint-based reconstruction and analysis (COBRA) methods. As reconstructed networks leverage genomic data for insight and phenotype prediction, the development of COBRA methods has accelerated following the advent of whole-genome sequencing. Here, we describe a phylogeny of COBRA methods that has rapidly evolved from the few early methods, such as flux balance analysis and elementary flux mode analysis, into a repertoire of more than 100 methods. These methods have enabled genome-scale analysis of microbial metabolism for numerous basic and applied uses, including antibiotic discovery, metabolic engineering and modelling of microbial community behaviour.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ab7d2563a86a1cb51a60b4b70df62ffcbc9d7351

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Citations in the corpus

Integrative approaches for finding modular structure in biological networks (2013, methodology): “We refer the reader to recent reviews 83,84 on integrative methods for modelling of metabolic networks through omics-based constraints.”
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models (2015, methodology): “GEMs can be used to predict cellular phenotypes (8), contextualize omics data (9–11), design cell factories (12,13) and understand evolutionary trajectories (14).”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, background): “Name to ID conversion Open [41] BiofOmics • Transcriptomics • Proteomics • Metabolomics Biofilm - Experiment library - Data depository Open [113] BioCyc/MetaCyc • Genomics • Proteomics • Metabolomics Unspecified - Online encyclopedia of metabolism - Predicted metabolic pathways in sequenced genomes ”
Using Genome-Scale Models to Predict Biological Capabilities (2015, positive): “Recapitulation Given its simplicity and utility, FBA has become one of the most widely employed computational techniques for the systems-level analysis of living organisms (Bordbar et al., 2014; Lewis et al., 2012).”
antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters (2015, background): “Also, hyperlinks to the MIBiG repository are available, where users can find additional information on each gene cluster. genome-scale models of metabolism (11,12).”
Gut microbiota functions: metabolism of nutrients and other food components (2017, background): “GENREs also provide a useful framework for the contextualization of meta-omic data, with a variety of constraint-based methods for the integration of such measurements already available [154].”
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0 (2019, methodology): “The local fork can be updated using procedure [5] of the contribute menu.”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “…is a constraint-based modeling package that is designed to accommodate the biological complexity of the next generation of COBRA models [10] and provides access to commonly used COBRA methods, such as flux balance analysis [35], flux variability analysis [18], and gene deletion analyses [43].”
Big Data Analytics in Healthcare (2015, methodology): “Constraint-based methods are widely applied to probe the genotype-phenotype relationship and attempt to overcome the limited availability of kinetic constants [168, 169].”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, background): “Integrating this information in a structured fashion has enabled its translation into computational models that can be used to calculate metabolic phenotypes (Palsson, 2009; Pfau et al , 2011; Lewis et al , 2012).”