A metabolic network in the evolutionary context: Multiscale structure and modularity

Victor Spirin, Mikhail S. Gelfand, Andrey A. Mironov, Leonid A. Mirny

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)

Abstract

The enormous complexity of biological networks has led to the suggestion that networks are built of modules that perform particular functions and are "reused" in evolution in a manner similar to reusable domains in protein structures or modules of electronic circuits. Analysis of known biological networks has revealed several modules, many of which have transparent biological functions. However, it remains to be shown that identified structural modules constitute evolutionary building blocks, independent and easily interchangeable units. An alternative possibility is that evolutionary modules do not match structural modules. To investigate the structure of evolutionary modules and their relationship to functional ones, we integrated a metabolic network with evolutionary associations between genes inferred from comparative genomics. The resulting metabolic-genomic network places metabolic pathways into evolutionary and genomic context, thereby revealing previously unknown components and modules. We analyzed the integrated metabolic-genomic network on three levels: macro-, meso-, and microscale. The macroscale level demonstrates strong associations between neighboring enzymes and between enzymes that are distant on the network but belong to the same linear pathway. At the mesoscale level, we identified evolutionary metabolic modules and compared them with traditional metabolic pathways. Although, in some cases, there is almost exact correspondence, some pathways are split into independent modules. On the microscale level, we observed high association of enzyme subunits and weak association of isoenzymes independently catalyzing the same reaction. This study shows that evolutionary modules, rather than pathways, may be thought of as regulatory and functional units in bacterial genomes.

Original languageEnglish
Pages (from-to)8774-8779
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume103
Issue number23
DOIs
Publication statusPublished - 6 Jun 2006
Externally publishedYes

Keywords

  • Clustering
  • Evolution
  • Modules

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