Metabolic modeling of microbial strains in silico

doi:10.1016/S0968-0004(00)01754-0

Trends in Biochemical Sciences

Volume 26, Issue 3, 1 March 2001, Pages 179-186

https://doi.org/10.1016/S0968-0004(00)01754-0 Get rights and content

Abstract

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

Section snippets

Genome annotation

Reconstruction of metabolic reaction networks in an organism begins with the thorough examination of the genome. The first step in functional annotation of a genome sequence is to identify the coding regions or open reading frames (ORFs) on the sequence. Each ORF is searched initially against databases with the goal of assigning a putative function to it. Established algorithms (e.g. the BLAST and FASTA family of programs) can be used to determine the similarity between a given sequence and

Model construction and analysis

Mathematical models and their computer simulation allow us to examine the integrated function of the reconstructed metabolic network. Awell-defined network by itself is not sufficient to describe the behavior of a system quantitatively, as shown in Fig. 2. Here, an analogy is drawn between simulating traffic conditions in a typical city and simulating the behavior of a microbial metabolic network. The first step for both situations is to generate a list of the functional components for the

Model characteristics

A comparison of the genomic characteristics and insilico metabolic model characteristics for three bacterial strains is shown in Table 1 (14, 29) (C.H.Schilling, PhD Thesis, University of California, 2000). These in silico models represent between 25% and 40% of the known ORFs in their in vivo counterparts. Fig. 3a shows the reaction complement of the gastric pathogen Helicobacter pylori 26695 in greater detail. The Venn diagram is used to categorize the inclusion of reactions in the

Modeling issues

There are two primary issues regarding the construction of microbial metabolic models. First, not all of the reactions suggested by these models are found directly in the databases or the biochemical literature, and second, not all of the metabolic genes present in the genotype are accounted for – or even noted – in the model, because their functions are as yet undiscovered (Fig. 3b).

However, a ‘real metabolic network’ exists for the example given in Fig. 3a, that is the actual set of all the

Future challenges

In silico models of metabolic networks will be subjected to an ongoing iterative model-building process just as complex systems in other branches of science and engineering have in the past. This process is illustrated in Fig. 4. Here, the traditional scientific method is depicted in the context of biology in the post-genomic era. Hypotheses based on the metabolic analysis of microbial strains are examined both in terms of an experimental study and using bioinformatics techniques.

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Trends in Biochemical Sciences

ReviewMetabolic modeling of microbial strains in silico

Abstract

Section snippets

Genome annotation

Model construction and analysis

Model characteristics

Modeling issues

Future challenges

Nucl. Acids Res.

Sci.

Nucl. Acids Res.

Proc. Natl. Acad. Sci. USA

Nature

Nucl. Acids Res.

Curr. Opin. Biotechnol.

Nat. Biotechnol.

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

Nucl. Acids Res.

Comparison of DNA sequences with protein sequences

Genomics

Protein function in the post-genomic era

Nature

Integrated pathway-genome databases and their role in drug discovery

Trends Biotechnol.

MPW: the Metabolic Pathways Database

Nucl. Acids Res.

Reconstruction of amino acid biosynthesis pathways from the complete genome sequence

Genome Res.

KEGG: Kyoto Encyclopedia of Genes and Genomes

Nucl. Acids Res.

Review
Metabolic modeling of microbial strains in silico