  Reliability on complex biological networks reconstructions remains a concern.
Although observations are getting more and more precise, the data collection
process is yet error prone and the proofs display uneven certitude. In the case
of metabolic networks, the currently employed confidence scoring system rates
reactions according to a discretized small set of labels denoting different
levels of experimental evidence or model-based likelihood. Here, we propose a
computational network-based system of reaction scoring that exploits the
complex hierarchical structure and the statistical regularities of the
metabolic network as a bipartite graph. We use the example of Escherichia coli
metabolism to illustrate our methodology. Our model is adjusted to the
observations in order to derive connection probabilities between individual
metabolite-reaction pairs and, after validation, we integrate individual link
information to assess the reliability of each reaction in probabilistic terms.
This network-based scoring system breaks the degeneracy of currently employed
scores, enables further confirmation of modeling results, uncovers very
specific reactions that could be functionally or evolutionary important, and
identifies prominent experimental targets for further verification. We foresee
a wide range of potential applications of our approach given the natural
network bipartivity of many biological interactions.
