ISMB 2003 BioPathways SIG and 5th BioPathways Meeting

Abstract

The 5th BioPathways Consortium Meeting gathered 21 speakers, close to 100 registered participants and an undetermined number of visitors from neighbouring SIGs. The meeting featured two main scientific sessions, focusing respectively on ‘Regulation and Interactions on a Systems Scale’ and ‘Function and Evolution of Metabolic Networks’, an ‘Ontologies, Databases and Data Integration’ session, and a contributed session on software tools for pathways. Following the BioPathways tradition and to foster depth of exchange, scientific sessions were structured as a series of long presentations, concluded by an hour of open discussion on the session theme. The meeting started with a short assessment of the evolution of the field — computational biology of networks, or ‘systems biology’? — which has matured fast in the 3 years of existence of the BioPathways SIG. While some theoretical subfields, such as network reconstruction from experimental data, are acquiring technical depth and generating predictions of increasing biological relevance, there is a clear trend towards a stronger coupling between theoretical and experimental approaches, leading to new open questions on both sides. Another noticeable trend is the strong revival of fields that had been perceived as fairly well understood and stable, such as metabolism, thanks both to the ‘systems-wide’ perspective and to new theoretical tools. The ‘Regulation and Interactions’ session revolved around a few key ideas, each illustrated by several speakers. A first theme was the search for the right notion of ‘module’ in biological networks, at different levels of molecular organization (e.g. in regulatory networks, protein interaction networks), using diverse theoretical tools (e.g. graph theory, graphical probabilistic models). Segal illustrated this theme by presenting a method to infer module networks, i.e. sets of genes sharing a regulatory mechanism, from expression data. The original inference scheme learns a Bayesian Network from the data, but the Bayesian network models regulation of sets of genes — modules — rather than single genes. The learning algorithm optimizes on the structure of the network, but also on the partition of genes into modules and on the ‘regulation program’ of each module, an abstract representation of the regulatory mechanism as a decision tree. It was also shown that in order to ensure statistical robustness and, better yet, biological relevance,