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  • Review Article
  • Published:

Reconstruction of biochemical networks in microorganisms

Nature Reviews Microbiology volume 7, pages 129–143 (2009)Cite this article

Key Points

  • Our ability to reconstruct genome-scale metabolic networks in microbial cells from genomic and high-throughput data has grown substantially in recent years. There are currently more than 25 genome-scale metabolic reconstructions of microbial cells, and 6–10 more are being produced each year.

  • An increasing number of research groups around the world are working on genome-scale reconstructions of metabolism in their target organism.

  • There is no single source that practitioners can access to learn about and understand the reconstruction process.

  • This Review details the data flows and work flows that underlie the reconstruction process and thus provides a basis for newcomers in the field.

  • Biological network reconstructions continue to grow in scope and are expected to include transcriptional regulation and protein synthesis over the next few years. Expansion in scope will probably also include small RNAs and two-component signalling networks.

  • Genome-scale reconstructions are a common denominator in systems biology of microorganisms and are reaching an advanced stage of development, which indicates that systems analysis of microbial functions and phenotypes will progress in the years to come.

Abstract

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This Review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

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Figure 1: Phases and data used to generate a metabolic reconstruction.
Figure 2: Network integration: the interface between different types of reconstruction.

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Acknowledgements

The authors thank A. Osterman and N. Jamshidi for their insights. A.M.F. and I.T. were supported by National Institutes of Health (NIH) grant R01 GM057089 and M.J.H. was supported by NIH grant R01 GM071808. B.O.P. serves on the scientific advisory board of Genomatica.

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Authors and Affiliations

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

    Adam M. Feist, Markus J. Herrgård, Ines Thiele & Bernhard Ø. Palsson

  2. Synthetic Genomics, 11149 N. Torrey Pines Road, La Jolla, 92037, California, USA

    Markus J. Herrgård

  3. Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, 53706, Wisconsin, USA

    Jennie L. Reed

  4. Centre for Systems Biology, University of Iceland, Vatnsmyrarvegi 16, Reykjavik, IS-101, Iceland

    Bernhard Ø. Palsson

Authors
  1. Adam M. Feist
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  2. Markus J. Herrgård
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  3. Ines Thiele
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Correspondence to Bernhard Ø. Palsson.

Supplementary information

Supplementary information S1 (table)

Available predictive genome-scale metabolic network reconstructions (PDF 216 kb)

Supplementary information S2 (table)

Common issues encountered during metabolic network reconstructions (PDF 112 kb)

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DATABASES

Entrez Genome Project

Bacillus subtilis

Clostridium acetobutylicum

Escherichia coli

Halobacterium salinarum

Saccharomyces cerevisiae

Staphylococcus aureus

Plasmodium falciparum

FURTHER INFORMATION

Bernhard Ø. Palsson's homepage

BioCyc

Biolog

BRENDA

CMR

CYGD

EBI

EcoCyc

EntrezGene

Genome Reviews

IMG

KEGG

MetaCyc

RegulonDB

SBML

SEED

SGD

Transport DB

Glossary

BiGG knowledge base

The collection of established biochemical, genetic and genomic data (BiGG) represented by a network reconstruction.

Genome-scale network reconstruction

(GENRE). A two-dimensional genome annotation (for example, a metabolic reconstruction) that contains a list of all the chemical transformations known to take place in a particular network (usually the entire metabolic network of a particular organism; for example, a GENRE of E. coli). These transformations can be represented by a stoichiometric matrix. A genre is updated as the BiGG knowledge base expands.

Genome-scale model

A network reconstruction in a mathematical format that can be computationally interrogated and can be subsequently used for experimental design.

Bibliomic data

Legacy data that are contained in peer-reviewed scientific publications. The 'omic designation represents a comprehensive assessment of legacy data for a target organism.

Stoichiometric matrix

A matrix that contains the stoichiometric coefficients for the reactions that constitute a network. The rows represent the compounds, the columns represent the chemical transformations and the entries represent the stoichiometric coefficients.

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Feist, A., Herrgård, M., Thiele, I. et al. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol 7, 129–143 (2009). https://doi.org/10.1038/nrmicro1949

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