Network Motifs Provide Signatures That Characterize Metabolism and Produce Novel Insights into the Evolutionary History of the Eukaryotic Cell.

[摘要] A motif is a small, repeated pattern that is over-represented in a network compared to its abundance in a collection of random graphs.Motifs are of chief interest in network theory and systems biology because their over-expression may determine the topological properties that give rise to dynamic behaviors in biological systems.Motifs also provide novel functional evidence that can help unravel mechanisms of molecular evolution.In this work, we analyze metabolic network motifs, where metabolites are represented by nodes and biochemical associations are represented by edges.We find that metabolic network motifs can be characterized by their enzymatic associations and therefore, their biochemical functionality.Further, we demonstrate that cellular organelles display motif distributions that are distinct from one another and likely reflect their distinct metabolic roles in the cell.We follow this analysis by assessing the relationship between motif participation and the property of tolerance to random component failure in the E. coli metabolic network. We find that the metabolic network displays higher levels of failure tolerance than seen in Erdos-Renyi random graphs, and that some motifs have unique structural properties in metabolism.Finally, we apply the methodology of motif mining and analysis to assess specific hypotheses of Eukaryotic organelle evolution.Specifically, we present novel evidence suggesting that an alpha-proteobacterium may not have been the ancestor of modern mitochondria.We independently validate this result using phylogenetic analysis and find that mitochondrial genomes tend to fall within the same clades as delta- and epsilon-proteobacteria.Based on this validation we propose a new hypothesis that modern mitochondria are not derived from alpha-proteobacteria, but are instead derived from a member of the delta- or epsilon-proteobacterial families.

[发布机构] University of Michigan

[效力级别] Network Motif

[关键词] Analysis of Large-scale Metabolic Networks;Network Motif;Metabolism;Molecular;Cellular and Developmental Biology;Science;Bioinformatics