Biosynthetic potentials of metabolites and their hierarchical organization

Franziska Matthaeus, Carlos Salazar, Oliver Ebenhoeh

Research output: Contribution to journalArticle

13 Citations (Scopus)
3 Downloads (Pure)

Abstract

A major challenge in systems biology is to understand how complex and highly connected metabolic networks are organized. The structure of these networks is investigated here by identifying sets of metabolites that have a similar biosynthetic potential. We measure the biosynthetic potential of a particular compound by determining all metabolites than can be produced from it and, following a terminology introduced previously, call this set the scope of the compound. To identify groups of compounds with similar scopes, we apply a hierarchical clustering method. We find that compounds within the same cluster often display similar chemical structures and appear in the same metabolic pathway. For each cluster we define a consensus scope by determining a set of metabolites that is most similar to all scopes within the cluster. This allows for a generalization from scopes of single compounds to scopes of a chemical family. We observe that most of the resulting consensus scopes overlap or are fully contained in others, revealing a hierarchical ordering of metabolites according to their biosynthetic potential. Our investigations show that this hierarchy is not only determined by the chemical complexity of the metabolites, but also strongly by their biological function. As a general tendency, metabolites which are necessary for essential cellular processes exhibit a larger biosynthetic potential than those involved in secondary metabolism. A central result is that chemically very similar substances with different biological functions may differ significantly in their biosynthetic potentials. Our studies provide an important step towards understanding fundamental design principles of metabolic networks determined by the structural and functional complexity of metabolites.
Original languageEnglish
Article numbere1000049
Number of pages13
JournalPLoS Computational Biology
Volume4
Issue number4
DOIs
Publication statusPublished - 4 Apr 2008

Keywords

  • algorithms
  • computer simulation
  • energy metabolism
  • biological models
  • proteome
  • signal transduction
  • structural complexity
  • metabolic network
  • functional complexity
  • biosynthetic potential
  • hierarchical clustering method

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