Dataset Information

Metabolic pathways variability and sequence/networks comparisons.

ABSTRACT:

Background

In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption.

Results

We demonstrate both the ability of the proposed method to build reliable biological classification of a set of microorganisms and the strong correlation between the metabolic network wiring and involved enzymes sequence space.

Conclusion

The method represents a valuable tool for the investigation of genotype/phenotype correlations allowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolic network space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process.

SUBMITTER: Tun K

PROVIDER: S-EPMC1360688 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature

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Publications

Metabolic pathways variability and sequence/networks comparisons.

Tun Kyaw K Dhar Pawan K PK Palumbo Maria Concetta MC Giuliani Alessandro A

BMC bioinformatics 20060118

<h4>Background</h4>In this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption.<h4>Results</h4>We demonstrate both the ...[more]

PMID: 16420696

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Project description:BackgroundA central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.ResultsIn this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/.ConclusionReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.

Dataset Information

Metabolic pathways variability and sequence/networks comparisons.

Background

Results

Conclusion

Publications

Metabolic pathways variability and sequence/networks comparisons.

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