Bioinformatics Advance Access originally published online on September 26, 2008
Bioinformatics 2008 24(22):2579-2585; doi:10.1093/bioinformatics/btn503
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Phylogenetic distances are encoded in networks of interacting pathways
1Systems Biology Group, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France and 2Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284-2030, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange.
Results: Our analysis shows that a small set of descriptors of the structure and complexity of the NIPs combined into regression models reproduce very accurately reference phylogenetic distances derived from 16S rRNA sequences (10-fold cross-validation correlation coefficient higher than 0.9). Our method also showed better scores than previous work on metabolism-based phylogenetic reconstructions, as assessed by branch distances score, topological similarity and second cousins score. Thus, our metabolome representation as network of overlapping metabolic pathways captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny. It is important to note that precise knowledge of all of the reactions in these pathways is not required for these reconstructions. These observations underscore the potential for the use of abstract, modular representations of metabolic reactions as tools in studying the evolution of species.
Contact: aurelien.mazurie{at}pasteur.fr
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Martin Bishop
Received on May 19, 2008; revised on September 3, 2008; accepted on September 19, 2008
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