Name
Abstract | ||
|---|---|---|
Background Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting
its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly
sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic
networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental
evidence or model-based likelihood.
Results Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network
as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs
and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system
uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets,
and enables further confirmation of modeling results.
Conclusions We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions
given the natural bipartivity of many biological networks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1186/1752-0509-5-76 | BMC systems biology |
Keywords | Field | DocType |
systems biology | Genome,Artificial intelligence,Natural language processing,Geography,Scoring system | Journal |
Volume | Issue | ISSN |
5 | 1 | 1752-0509 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M Ángeles Serrano | 1 | 257 | 17.84 |
| Francesc Sagués | 2 | 8 | 2.15 |