The data available from the Human genome project, and from the many other molecular high-throughput experiments, allow for the first time to build realistic models of genetic interactions in a cell. While there is still limited knowledge of the underlying biological mechanisms and functionalities, complex networked systems have emerged in the postgenomics era as the new paradigm in molecular biology.

Accordingly, data analysts are now challenged to develop corresponding complex large-scale models of stochastic systems with many interacting variables. Due to the "small n, large p" character of current molecular data, inference and modeling pose particularly hard problems and typically require advanced and novel statistical techniques.

This meeting aims to bring together state-of-the-art researchers working, e.g., on methods for

• graphical models and multiple time series,

• inference of high-dimensional models, and

• dynamics of complex systems

• genetic networks,

• gene expression analysis, and

• systems biology.