Using Genome-Scale Models to Predict Biological Capabilities

By Edward J. O'Brien, Jonathan M. Monk, Bernhard O. Palsson. Cell, 2015

684 citations recorded by the source corpus.

Constraint-based reconstruction and analysis (COBRA) methods at the genome-scale have been under development since the first whole genome sequences appeared in the mid-1990s. A few years ago this approach began to demonstrate the ability to predict a range of cellular functions including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome-scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This primer will get you started.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/62d097edb954a17c561cccfab95b185745d084ed

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Citations in the corpus

Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism (2018, neutral): “Genome-scale network reconstructions can be converted into computational models that enable predictive biology 2 .”
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0 (2019, methodology): “These branches can be accessed by following procedure [2] in Step 99.”
Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017, methodology): “An alternative framework to account for enzyme limitation is a genome-scale model of both metabolism and gene expression (ME model) (O’Brien & Palsson, 2015), which includes metabolic reactions and all processes required for the synthesis of functional proteins starting from the transcription rates…”
Metabolomics and isotope tracing (2018, background): “At steady state, the quantitative inflows and effluxes from eachmetabolite must balance (O’Brien et al., 2015).”
RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor (2018, background): “GEMs can be used to understand and predict how organisms react to variations on genetic and environmental parameters [2].”
Engineering Cellular Metabolism (2008, methodology): “Here, genome-scale metabolic models (GEMs) have shown to be particularly useful (O’Brien et al., 2015; Lee and Kim, 2015), and GEMs have been developed for most industrially relevant microorganisms (Kim et al., 2012; Garcia-Albornoz and Nielsen, 2013).”
The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions (2017, methodology): “Potential synergies include designing hypothesis-driven experiments based on the results of modeled scenarios, or using modeling as a tool to mechanistically interpret the results of high-throughput experiments (O’Brien et al., 2015).”