The DOI matches its Crossref record; 144 of the 144 references it cites were found in a registry or an index.
Reconstructing organisms in silico: genome-scale models and their emerging applications
Open accessAbstract
Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
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“Furthermore, combinatorial multiplexing experiments remain prohibitively expensive and technically challenging to scale (7, 8).”
From A systematic comparison of single-cell perturbation response prediction models (2026) · cites this paper as [7]
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Muzahid, N. H., Patel, A., Raymond, M., Egidi, T., Nguyen, N., & Zúñiga, C. (2026) · Infection and Immunity · doi:10.1128/iai.00435-26
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Metadata
- Authors
- Fang, X., Lloyd, C. J., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- Venue
- Nature Reviews Microbiology
- Published
- 2020-09-21
- Volume
- 18
- Issue
- 12
- Pages
- 731-743
- ISSN
- 1740-1526 (ISSN-L)
- 1740-1534
- PMC
- PMC7981288
- OpenAlex
- W3087744872
- Cited by
- 292
- References
- 142
- Citations by year
- 2026: 34 · 2025: 49 · 2024: 66 · 2023: 60 · 2022: 35 · 2021: 47 · 2020: 1
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Microbiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- 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 models, Escherichia coli, metabolic network reconstruction, pangenome analysis, proteome allocation, phenotype prediction
- MeSH terms
- Firmicutes, Bacillota, Whole Genome Sequencing, Thermotoga, Cyanobacteria, Computer Simulation, Escherichia coli, Models, Genetic, Phenotype, Stress, Physiological, Genome, Bacterial, Proteobacteria, Genomics, Actinobacteria, Gene Regulatory Networks, Metabolic Networks and Pathways
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