Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

By D. McCloskey, Bernhard O. Palsson, Adam M. Feist. Molecular Systems Biology, 2013

359 citations recorded by the source corpus.

The genome‐scale model (GEM) of metabolism in the bacterium Escherichia coli K‐12 has been in development for over a decade and is now in wide use. GEM‐enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model‐driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome‐scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large‐scale network models with sufficient accuracy.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9ca4eaa3d7557ff5e87740cb390b2be2c31e2f1

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Citations in the corpus

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, background): “These reconstructions range from microbes to higher organisms, and have been used for myriad applications, including the prediction of cellular phenotypes, design of microbial cell factories, studies of evolution, and model-driven discovery of novel drug targets [2,6].”
Insights from 20 years of bacterial genome sequencing (2015, background): “This has enabled several developments on large-scale network analysis (McCloskey et al. 2013) that can have several applications (Bordbar et al. 2014).”
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models (2015, background): “GEMs can be used to predict cellular phenotypes (8), contextualize omics data (9–11), design cell factories (12,13) and understand evolutionary trajectories (14).”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “It has been successfully applied to a multitude of species for modeling their cellular metabolisms (Feist and Pals-son, 2008; McCloskey et al., 2013; Oberhardt et al., 2009) and therefore enabled a variety of applications such as metabolic engineering for the over-production of biochemicals (Yim et…”