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  • Review Article
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Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

Nature Reviews Microbiology volume 10, pages 291–305 (2012)Cite this article

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Key Points

  • Our understanding of genotype–phenotype relationships has classically been qualitative, but recent advances are enabling us to overcome conceptual and technological barriers, leading to a quantitative understanding of these relationships. Within the framework of constraint-based modelling, the generation of quantitative relationships is facilitated by the realization that cell phenotypes are limited by physical and genetic constraints.

  • Physical laws, such as mass conservation and thermodynamics, constrain the possible metabolic and biosynthetic transformations that can occur in nature, and genetics specify which sets of biochemical reactions have been selected through evolution. Genome sequencing and annotation have allowed the comprehensive reconstruction of microbial metabolic networks, and constraint-based reconstruction and analysis (COBRA) modelling has emerged as a set of valuable tools that allows detailed analysis of the biochemical mechanisms underlying the metabolic genotype–phenotype relationship.

  • Network-based pathway analysis tools, such as elementary flux modes and extreme pathways analysis, delineate pathways that can perform a given metabolic function in an organism of interest. Although these methods have been difficult to use in larger metabolic networks, simplifications are now beginning to allow their use on genome-scale models.

  • As not all pathways are used in a cell at a given time, optimization algorithms are routinely used to identify pathway use that best reflects the in vivo metabolic state. Flux balance analysis, which uses linear programing to optimize a mathematical description of the cellular objective, has been widely used to understand microbial physiology and the effects of environmental and genetic perturbations.

  • The ability of COBRA methods to model genetic perturbations has allowed such methods to help predict antimicrobial targets and to aid in the design of strains for chemical production.

  • Reconstructed metabolic networks are often incomplete and can have a small fraction of incorrect reactions therein. However, the integration of phenotypic screens with model simulations can provide a systematic approach to refine the models and discover new metabolic functions in an organism.

  • COBRA methods are extending beyond metabolism, and approaches are beginning to incorporate transcription regulation, either implicitly, by constraining models with multiple 'omic data types, or explicitly, with detailed descriptions of regulatory mechanisms.

  • The diverse range of more than 100 COBRA methods is being deployed to address many questions in microbiology. For example, several recent studies have begun to explore the roles of metabolism in community interactions, including symbiosis, competition, parasitism and evolution.

Abstract

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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Figure 1: Fundamentals of the genome-scale metabolic genotype–phenotype relationship.
Figure 2: The 'phylogeny' of constraint-based modelling methods.
Figure 3: Flux balance analysis.
Figure 4: Principles of model-guided strain design.
Figure 5: Refining thermodynamic constraints.
Figure 6: Incorporating regulation.
Figure 7: Integrating constraint-based reconstruction and analysis methods to study community interactions.

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Acknowledgements

The authors thank M. Abrams, J. Monk, D. Taylor, E. O'Brien, A. Feist, J. Lerman, D. Zielinski, R. Chang, K. Zengler, A. Bordbar, R. Fleming, N. Price and E. Ruppin for discussions and comments on this Review, and numerous members of the COBRA community for help with compiling methods. This work was funded by the US National Institutes of Health (grant R01GM057089) and the Office of Science (Biological and Environmental Research) for the US Department of Energy (grant DE-SC0004485).

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Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093–0412, California, USA

    Nathan E. Lewis & Bernhard O. Palsson

  2. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093–0412, California, USA

    Harish Nagarajan

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  1. Nathan E. Lewis
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Correspondence to Bernhard O. Palsson.

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Supplementary information

Supplementary information s1 (table)

Description and classification of constaint-based methods. (XLS 151 kb)

Supplementary information s2 (table)

Software packages that have implemented multiple constraint-based methods. (XLS 34 kb)

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FURTHER INFORMATION

Bernhard O. Palsson's homepage

COBRA software

Glossary

Metabolic reconstruction

A carefully curated and biochemically validated knowledge base in which all known chemical reactions for an organism are detailed and catalogued.

Solution space

The feasible region satisfying a set of constraints. In constraint-based reconstruction and analysis (COBRA) models, this represents the feasible flux values for all of the reactions in the model.

Flux distributions

A set of steady-state fluxes for all of the reactions in a metabolic network.

Biomass function

A pseudo-reaction that is formed to aid in predicting the growth of a cell in constraint-based reconstruction and analysis (COBRA) models. It describes the rate at which and accurate proportions in which all of the biomass precursors are made.

Linear programing

A mathematical optimization technique that determines a way to maximize a particular linear objective under a given set of conditions (that is, when subjected to linear equalities and inequalities as constraints). Typically used in flux balance analysis, in which the objective is often the biomass function (growth) and the constraints represent the growth conditions.

Genome-scale model

(GEM). A condition-specific, mathematically described, computable derivative of a metabolic reconstruction, containing comprehensive knowledge of metabolism.

Mixed-integer linear programing

(MILP). Similar to linear programing, but some of the constraints are integer values. Used for applications such as enumerating alternative optimal solutions, strain design, eliminating loops, and so on.

Epistasis

The interaction between two genes such that the phenotypic effect of one gene is masked by that of the other. Usually identified by the phenotype of the double mutant relative to the phenotypes of the two single mutants.

Shadow prices

A mathematical term that refers to the dual aspects of the linear programing problem. It represents the rate at which the objective value (for example, growth rate) changes as the supply of a particular resource (for example, a metabolite) increases.

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Lewis, N., Nagarajan, H. & Palsson, B. Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10, 291–305 (2012). https://doi.org/10.1038/nrmicro2737

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