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Biosynthetic potentials of metabolites and their hierarchical organization.

Matthäus F, Salazar C, Ebenhöh O - PLoS Comput. Biol. (2008)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.

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.

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Cluster radius and cluster separation.Maximum distance between compounds of a cluster to their corresponding consensus scope d0 (cluster radius), and the minimum distance of the compounds to the second nearest consensus scope d1 (cluster separation). The assignment to a cluster is good if d0≪d1.
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pcbi-1000049-g006: Cluster radius and cluster separation.Maximum distance between compounds of a cluster to their corresponding consensus scope d0 (cluster radius), and the minimum distance of the compounds to the second nearest consensus scope d1 (cluster separation). The assignment to a cluster is good if d0≪d1.

Mentions: Finally, we measure the quality of the clustering to assure that the elements within the same cluster are similar and the clusters well distinguishable. Generally, a clustering is considered good, if the distances of the elements within a cluster are small, and the distances between elements of distinct clusters large. To quantify the quality of the clustering we compute for every cluster (I-XIII) the distance between all scopes contained in the cluster to the consensus scope. The maximum of these distances can be regarded as a cluster radius, denoted by . Furthermore, we compute the distance between all scopes in the cluster to the second nearest consensus scope. The minimum of these, , provides a measure of the cluster separation. Since the distance is based on the Jaccard coefficient, and ≤1. A cluster is well defined if is small and large. Figure 6 shows that for all clusters is much smaller than , except for cluster II. This can be regarded as a consequence of the similarity between the clusters II and IX. Cluster II is fully embedded in cluster IX, while their consensus scopes differ only by about 25%.


Biosynthetic potentials of metabolites and their hierarchical organization.

Matthäus F, Salazar C, Ebenhöh O - PLoS Comput. Biol. (2008)

Cluster radius and cluster separation.Maximum distance between compounds of a cluster to their corresponding consensus scope d0 (cluster radius), and the minimum distance of the compounds to the second nearest consensus scope d1 (cluster separation). The assignment to a cluster is good if d0≪d1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2289774&req=5

pcbi-1000049-g006: Cluster radius and cluster separation.Maximum distance between compounds of a cluster to their corresponding consensus scope d0 (cluster radius), and the minimum distance of the compounds to the second nearest consensus scope d1 (cluster separation). The assignment to a cluster is good if d0≪d1.
Mentions: Finally, we measure the quality of the clustering to assure that the elements within the same cluster are similar and the clusters well distinguishable. Generally, a clustering is considered good, if the distances of the elements within a cluster are small, and the distances between elements of distinct clusters large. To quantify the quality of the clustering we compute for every cluster (I-XIII) the distance between all scopes contained in the cluster to the consensus scope. The maximum of these distances can be regarded as a cluster radius, denoted by . Furthermore, we compute the distance between all scopes in the cluster to the second nearest consensus scope. The minimum of these, , provides a measure of the cluster separation. Since the distance is based on the Jaccard coefficient, and ≤1. A cluster is well defined if is small and large. Figure 6 shows that for all clusters is much smaller than , except for cluster II. This can be regarded as a consequence of the similarity between the clusters II and IX. Cluster II is fully embedded in cluster IX, while their consensus scopes differ only by about 25%.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.

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.

Show MeSH
Related in: MedlinePlus