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  • Review Article
  • Published:

Genome-scale models of microbial cells: evaluating the consequences of constraints

Nature Reviews Microbiology volume 2, pages 886–897 (2004)Cite this article

Key Points

  • The range of allowable biochemical network functions is constrained by natural law. These constraints have been classified as first, physico-chemical constraints; second, topobiological constraints; third, environmental constraints; and fourth, self-imposed regulatory constraints. These constraints can be represented mathematically, and a growing toolbox of computational analysis methods can be used to interrogate network properties.

  • The most common set of computational tools uses linear programming to identify network states that are optimal relative to a defined objective. Flux balance analysis is an example. Optimal states have been successfully used for applications such as predicting the endpoint of adaptive evolutions of Escherichia coli.

  • A more recently developed set of computational tools are used to quantify dependencies between fluxes in the network. For example, correlated reaction sets that form functional units, or unbiased modules, of a metabolic network can be determined.

  • All allowable functional states of a biochemical network can be characterized in one of two ways: first, by enumerating network-based pathways that serve as a basis set from which all possible steady-state flux distributions can be generated or second, by using Monte-Carlo sampling of all possible functional states.

  • The development of this field has matured to the point that phenotypes of altered strains can now be predicted. One such strategy, known as pOptKnock, uses gene deletions (or additions) to align the objective of an altered cell (optimal growth) with the necessary increase in the objective of the metabolic engineer (increased secretion of a particular compound). Adaptive evolution under selective pressure for growth rate is then used to select for strains that should produce the desired product at an increased rate.

  • Future directions in this field include studying the achievable concentration states of biochemical networks, the range of in vivo kinetic constants consistent with high-throughput data and the incorporation of non-linear constraints.

Abstract

Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.

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Figure 1: A growing toolbox for constraint-based analysis.
Figure 2: Determining optimal states.
Figure 3: Flux dependencies.
Figure 4: Characterizing the whole solution space.
Figure 5: Altered solution spaces.
Figure 6: Additional constraints.

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Acknowledgements

The authors would like to acknowledge funding from the United States National Institutes of Health and the National Science Foundation.

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Authors and Affiliations

  1. Department of Bioengineering, University of California, San Diego, La Jolla, 92093, California, USA

    Nathan D. Price, Jennifer L. Reed & Bernhard Ø. Palsson

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  1. Nathan D. Price
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  2. Jennifer L. Reed
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  3. Bernhard Ø. Palsson
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Correspondence to Bernhard Ø. Palsson.

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B.Ø.P. is on the Scientific Advisory Board of Genomatica, Inc.

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FURTHER INFORMATION

Bernhard Palsson's laboratory

GAMS

Matlab

Mathematica

SimPheny

Glossary

COBRA

(Constraint-based reconstruction and analysis). The overall philosophy and approach of applying constraints to limit the range of achievable functional (phenotypic) states of GENREs.

CONVEX SPACE

A convex space is one that satisfies the following condition: given any two points in the space, the line segment in between the points is completely contained in the space. Examples of convex objects include a square, triangle or circle.

CONCAVE SPACE

A space that is not convex. Examples of concave objects include a doughnut shape or a crescent.

GENRE

(Genome-scale network reconstruction). Applies to a particular organism, for example, GENRE of Escherichia coli. A GENRE contains a list of all the chemical transformations that take place in the particular network. These transformations can be represented stoichiometrically. These stoichiometric representations form a matrix, the rows of which represent the compounds, the columns of which represent the chemical transformations and the entries of which are the stoichiometric coefficients.

GEMS

Genome-scale models in silico of a particular organism, for example, GEMS of E. coli. The COBRA approach is used to analyse the properties of GENREs by assessing allowable functional states.

REDUCED COST

A mathematical programming term; it is the smallest change in the objective function coefficient needed for a zero variable to become a non-zero variable.

SHADOW PRICE

A mathematical programming term; it is the rate at which the objective value changes by increasing the supply of a particular resource (for example, a metabolite).

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Price, N., Reed, J. & Palsson, B. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2, 886–897 (2004). https://doi.org/10.1038/nrmicro1023

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