Unknown

Dataset Information

0

Metabolic PathFinding: inferring relevant pathways in biochemical networks.


ABSTRACT: Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

SUBMITTER: Croes D 

PROVIDER: S-EPMC1160198 | biostudies-literature | 2005 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

Metabolic PathFinding: inferring relevant pathways in biochemical networks.

Croes Didier D   Couche Fabian F   Wodak Shoshana J SJ   van Helden Jacques J  

Nucleic acids research 20050701 Web Server issue


Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present  ...[more]

Similar Datasets

| S-EPMC2791103 | biostudies-literature
| S-EPMC3521384 | biostudies-literature
| S-EPMC6954563 | biostudies-literature
| S-EPMC6997860 | biostudies-literature
| S-EPMC7273199 | biostudies-literature
| S-EPMC7657559 | biostudies-literature
| S-EPMC3539929 | biostudies-literature
| S-EPMC4331720 | biostudies-literature
| S-EPMC1360688 | biostudies-literature