Reconstruction of phyletic trees by global alignment of multiple metabolic networks

Abstract

BackgroundIn the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information.ResultsWe explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis.ConclusionsWe demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.

Optimizing a global alignment of protein interaction networks

“…Furthermore, Kuchaiev et al (2010) defined a distance metric between two species by using the EC ratio of pairwise metabolic network alignments and reconstructed phylogenetic trees. Global alignment of biological networks may reveal the evolutionary relationship from a systems-level perspective (Ma et al , 2013). It would be of great interest to have a better understanding of phylogeny by using our global alignment algorithm on biological networks.…”

Computational solutions for omics data

“…The Isorank algorithm is one such network alignment approach; it combines sequence similarity and network similarity constraints to construct and to solve an . It has been used to align and to analyse networks based on protein–protein interaction 134,135 , genetic interaction 140 and metabolic data 141 .…”

Solving the Tree Containment Problem for Reticulation-Visible Networks in Linear Time

“…As a result of their experiment, biologists often obtained a binary tree that best explain the evolution of the gene/protein [Delsuc et al, 2005, Ma et al, 2013. Tree containment problem (TCP) is a problem that arise from verifying a given phylogenetic network model with the experimentallyderived binary tree.…”