Metabolic pathways variability and sequence/networks comparisons.
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.


CONCLUSIONS
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.
Tun
Kyaw
K
Systems Biology Group, Bioinformatics Institute, 30 Biopolis Way, 138671, Singapore. kyawtun@bii.a-star.edu.sg
Dhar
Pawan K
PK
Palumbo
Maria Concetta
MC
Giuliani
Alessandro
A
D000465:Algorithms; D018407:Bacterial Physiological Phenomena; D001426:Bacterial Proteins / Q000737:chemistry; D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D016680:Genome, Bacterial; D023281:Genomics; D005838:Genotype; D005943:Gluconeogenesis; D006019:Glycolysis; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D010641:Phenotype; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D012984:Software; D013045:Species Specificity
D001426:Bacterial Proteins