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Reconstructing organisms in silico: genome-scale models and their emerging applications

Nature Reviews Microbiology volume 18, pages 731–743 (2020)Cite this article

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Abstract

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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Fig. 1: Basic principles of constraint-based modelling of cellular functions.
Fig. 2: The increasing number of genome sequences and the development of genome-scale models.
Fig. 3: Historical development of E. coli genome-scale models.
Fig. 4: Generation of strain-specific E. coli genome-scale models and their application in multistrain studies.
Fig. 5: General formulation of a ME model and its application to the study of stress response.

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Acknowledgements

This work was supported by the US National Institutes of General Medical Sciences of the National Institutes of Health (grant R01GM057089) and the Novo Nordisk Foundation (grant NNF10CC1016517). Some figures were adapted from previously published figures, and the authors acknowledge the original authors of these figures and thank them for their contributions: J. M. Monk (Fig. 3), L. Yang (Fig. 5), K. Chen (Fig. 5), B. Du (Fig. 5) and A. Bordbar (Fig. 1).

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Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Xin Fang, Colton J. Lloyd & Bernhard O. Palsson

  2. Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA

    Bernhard O. Palsson

  3. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

    Bernhard O. Palsson

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  1. Xin Fang
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Contributions

X.F. researched data for the article. X.F. and B.O.P. substantially contributed to discussion of the content. All authors wrote the article, and reviewed or edited the manuscript before submission.

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Correspondence to Bernhard O. Palsson.

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Nature Reviews Microbiology thanks María Suárez Diez, Sara Moreno Paz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Python

An interpreted, general-purpose programming language that is widely used in computational biology.

Reactomes

All the reactions involved in genome-scale models (or a certain model of interest). Each base unit is a reaction, and the entities are metabolites involved in the reactions, such as proteins, nucleic acids and small molecules.

Proteome allocation

The partition of proteomics resources into different functions to fulfil the organism’s need under the given condition.

Sensome

The components (such as genes and proteins) in an organism or cell that are involved in sensing the changes in the environment.

Proximal causation

Explains traits/events (such as change in proteome allocation) in terms of immediate physiological or environmental factors.

Distal causation

Explains traits/events (such as change in proteome allocation) in terms of evolutionary forces acting on them.

Expression matrix

(E matrix). A matrix that describes all components (including DNA, mRNA, proteins and metabolites) and reactions that are involved in the transcriptional and translational machinery in the organism of interest.

Overflow metabolism

When cells incompletely oxidize their substrate (which yields less energy) instead of using the more energetically efficient respiratory pathways to completely oxidize their substrates even when oxygen is available.

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Fang, X., Lloyd, C.J. & Palsson, B.O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 18, 731–743 (2020). https://doi.org/10.1038/s41579-020-00440-4

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