Workshop 1: Network Biology: Understanding metabolic and protein interactions (September 14-18, 2009)
Organizers: Eivind Almaas and Laszlo Barabasi
As network approaches have become an important tool to study a wide range of complex systems for which traditional reductionist approaches have enjoyed limited success, maybe the biggest enthusiasm and triumphs have been noted in biology. In particular within the cell, the variety of interactions between genes, proteins and metabolites are well captured by network representations. The dramatic availability of quantitative data from large-scale genomic experiments has begged for systemic approaches with the ability to simultaneously integrate information from multiple sources. In response, the advent of "systems biology" methods has been heavily influenced by network methods. Although recent network analyses have shed light on organizational principles of the proteome as well as the metabolome, there is, however, an increasing need for developing even more sophisticated, integrative approaches as higher quality data is becoming available. These challenges include developing systematic methods for integrating proteomic and metabolic information, thus coupling their mostly separated analyses; incorporating spatial localization of cellular constituents, and developing new tools to include stochastic and time varying measurements. Noteworthy, most network oriented workshops and conferences have an interdisciplinary and broad focus, as network approaches flourish in many fields. However, there is a need to bring biologists together with network scientists to discuss sharply defined topics within network biology. The goal of this workshop is to facilitate information exchange between biologists (experimental as theoretical) and network scientists, making them aware of each others capabilities and methodologies, as well as fostering collaborative interactions. Analysis and modeling of metabolic and protein interaction networks typically involves graph theory, optimization, and statistics.
