Genome-scale microbial in silico models: the constraints-based approach

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Abstract

Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70–90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.

Section snippets

The constraints-based modeling approach

The challenges of genome-scale model building are being met, in part, by constraints-based models 19, 24. This modeling process involves a multi-step procedure (Fig. 1). The first step is to reconstruct the underlying network 25, 26, 27, 28, 29. For metabolism, this is a well-established procedure [25], whereas methods for the reconstruction of the associated regulatory networks are being developed 30, 31. The second step involves the statement of the constraints under which the reconstructed

Current genome-scale metabolic models

Genome-scale constraints-based models of metabolism have been built for several organisms, and some have appeared in the literature, including for Escherichia coli 45, 46, Haemophilus influenzae [47], Helicobacter pylori [48], and Saccharomyces cerevisiae [49]. Others, including Bacillus subtilis, Pseudomonas aeruginosa, and Pseudomonas putida have been fully built but not yet published (Sung Park and Jeremy Edwards, unpublished results).

‘Genome-scale’ is used to describe these models because

Simulating the results of manipulating gene content and function

There are many experimental methods for changing the gene content of an organism. Genes can be added or deleted, or their functions impaired or enhanced. Reliable computational models that link genotype to phenotype would thus allow for directed manipulation of the gene content of an organism to obtain a desired phenotype. Genome-scale constraints-based models can provide such a link. The ability to predict phenotypic outcomes from genetic inputs would provide a basis for the rational selection

Predictions of optimal growth rates

Experimental validation of in silico predictions has provided increasing evidence that a primary function of the E. coli metabolic network is to maximize growth 42, 44. E. coli was grown on several substrates, including acetate, succinate, malate or glucose minimal media, and the corresponding uptake rates, secretion rates and growth rates were experimentally measured. Good correlation was obtained between the growth rates, uptake rates and secretion rates that were experimentally observed and

Computing minimal reaction sets

A recent study used the E. coli metabolic model to enumerate the number of reactions necessary to maintain the capacity of the metabolic network to produce all of the biomass components necessary for growth [40]. It was discovered that the E. coli metabolic network was able to support growth using only 31% of its known metabolic reactions for growth on glucose and only 17% of its metabolic reactions on a rich medium. Thus, E. coli's metabolic network contains many redundant reactions, making E.

Accounting for regulation

The genome-scale models discussed above are based on network topology and the analysis assumes their unfettered use to achieve assumed optimal performance. Cells use complex regulatory networks to achieve their goals that might or might not be consistent with assumptions of optimal performance. Thus, significant need exists to account for regulation in genome-scale models and initial progress in this regard is being made [59].

Beyond metabolism: constraints-based modeling as means to integrate ‘omics’ data

Genome-scale models provide a framework in which high-throughput biological data can be integrated and thus broadens our capacity to predict phenotypes. In silico methods are being developed that allow for the integration of heterogeneous data sets, including genomics, proteomics, transcriptomics and metabolomics 17, 61 (Fig. 2). In the case of E. coli, the inclusion of metabolism, transcription, translation and regulation will lead to models that account for ∼2000 open reading frames. Such a

In closing

Genome-scale models of metabolism have been developed for several microbial cells. Many useful results have already been derived from these models and as they grow in scope and validation, an even broader set of uses can be anticipated. Perhaps most important is the integration of diverse omics data and the ability to account for the management of the genome and associated regulatory processes. Eventually, such models are expected to become routinely used for a variety of biotechnological

Acknowledgments

We acknowledge the support of the National Science Foundation (BES 01–20363), the National Institutes of Health (GM 57089), and the Whitaker Foundation (Graduate Research Fellowship to JP).

References (61)

  • N.D Price

    Extreme pathways and Kirchhoff's second law

    Biophys. J.

    (2002)
  • C.H Schilling

    Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective

    J. Theor. Biol.

    (2000)
  • H.P.J Bonarius

    Flux analysis of underdetermined metabolic networks: the quest for the missing constraints

    Trends Biotechnol.

    (1997)
  • J.S Edwards et al.

    Systems properties of the Haemophilus influenzae Rd metabolic genotype

    J. Biol. Chem.

    (1999)
  • R Mahadevan

    Dynamic flux balance analysis of diauxic growth in Escherichia coli

    Biophys. J.

    (2002)
  • S Flores

    Analysis of carbon metabolism in Escherichia coli strains with an inactive phosphotransferase system by (13)C labeling and NMR spectroscopy

    Metab. Eng.

    (2002)
  • J.A Papin

    The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy

    J. Theor. Biol.

    (2002)
  • M.W Covert

    Regulation of gene expression in flux balance models of metabolism

    J. Theor. Biol.

    (2001)
  • C.H Schilling et al.

    Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis

    J. Theor. Biol.

    (2000)
  • T.E Allen et al.

    Sequenced-based analysis of metabolic demands for protein synthesis in prokaryotes

    J. Theor. Biol.

    (2003)
  • B Chance

    Analogue and digital computer representations of biochemical processes

    Fed. Proc.

    (1962)
  • D Garfinkel

    Computer applications to biochemical kinetics

    Annu. Rev. Biochem.

    (1970)
  • J.G Reich et al.

    Energy Metabolism of the Cell: A Theoretical Treatise

    (1981)
  • P.J Mulquiney

    Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: in vivo kinetic characterization of 2,3-bisphosphoglycerate synthase/phosphatase using 13C and 31P NMR

    Biochem. J.

    (1999)
  • P.J Mulquiney et al.

    Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: equations and parameter refinement

    Biochem. J.

    (1999)
  • P.J Mulquiney et al.

    Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations: computer simulation and metabolic control analysis

    Biochem. J.

    (1999)
  • N Jamshidi

    Dynamic simulation of the human red blood cell metabolic network

    Bioinformatics

    (2001)
  • M Tomita

    E-CELL: software environment for whole-cell simulation

    Bioinformatics

    (1999)
  • R.D Fleischmann

    Whole-genome random sequencing and assembly of Haemophilus influenzae Rd

    Science

    (1995)
  • D Drell

    The department of energy microbial cell project: a 180° paradigm shift for biology

    Omics

    (2002)
  • Cited by (0)

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