Organization of Biological Networks

Research towards understanding biological systems is movingfrom a focus ofidentifying components parts (cells, molecules, sequences) tothe study of how these components function together. Networks attainfunctional characteristics that the individual components do not possessin isolation.To reveal and further explore such emergent properties ofnetworks, over avariety of biological scales and complexities, mathematicaltools play increasingly important roles in furthering our understanding ofbiological systems.

Goals:

(1) The goal of this workshop is to bring together researchersfrom thebiological and mathematical sciences to compare and contrastemergingsystems properties over diverse scales of biological networks.

(2) The goal of each session is to first discuss progress andchallenges atthe biological scale relevant to that session. The sessionleader will thenorganize subsequent talks and discussion to focus on specificnetworks and biological questions at that scale.

(3) The goal for each presenter is to give sufficient, but notoverwhelmingdetails on their biological system so that key biologicalquestionsinvolving design principles, models, and conjectures can beunderstood and compared across sessions by a diverse audience. Thisworkshop will have limited focus on tool building for data analysis.

Session Descriptions:

The workshop will be organized into five sessions, presented inincreasing order of biological scale. The first session will be onnetworks at thesingle molecule scale, with emphasis on stochastic effects. Thesecond will be in design principles of small networks. Here, syntheticnetworks andnetworks involving polarity decisions will be showcased. Thethird andfourth sessions will delve into intra- and intercellularnetworks. Emphasis will be placed on biological examples in which collaborations between theory and experiment are nascent, but showing promise. Our final session will cover design principles of large networks, including results from interpreting high-throughput and large-scale datasets.