Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods
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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.
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Constraint-based modeling enables calculation of metabolic fluxes from reactions' stoichiometry and intracellular metabolites' mass balances.
“constraint‐based modeling (Lewis et al, 2012; Palsson, 2015), which enables calculation of metabolic fluxes from reactions’ stoichiometry and intracellular metabolites’ mass balances.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Lewis et al, 2012
Once this question is elucidated, a plethora of COBRA methods can be deployed to investigate genotype-phenotype relationships.
“Once this question is elucidated, a plethora of constraint-based reconstruction and analysis (COBRA) methods can be deployed to investigate genotype–phenotype relationships (Lewis et al., 2012).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Lewis et al., 2012
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- Authors
- Lewis, N. E., Nagarajan, H., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- ORCID
- Lewis, Nathan E.
- Venue
- Nature Reviews Microbiology
- Published
- 2012-02-27
- Volume
- 10
- Issue
- 4
- Pages
- 291-305
- ISSN
- 1740-1526 (ISSN-L)
- 1740-1534
- PMC
- PMC3536058
- OpenAlex
- W2082382448
- Cited by
- 779
- References
- 145
- Citations by year
- 2026: 23 · 2025: 30 · 2024: 42 · 2023: 39 · 2022: 41 · 2021: 46 · 2020: 53 · 2019: 51 · 2018: 56 · 2017: 77 · 2016: 71 · 2015: 81 · 2014: 70 · 2013: 67 · 2012: 32
- FWCI
- 7.14
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Microbiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM057089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- genome-scale metabolic modeling, genotype-phenotype correlation, microbial metabolism, flux balance analysis, metabolic networks, antibiotic discovery, metabolic engineering
- MeSH terms
- Computer Simulation, Energy Metabolism, Escherichia coli, Genotype, Models, Biological, Phenotype, Genome, Bacterial, Genome, Fungal, Buchnera, Metabolic Networks and Pathways, Metabolic Engineering
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