Experimental advances in molecular biology are providing deeper understanding in the workings of living cells. High throughput functional genomic techniques are providing researchers with a reliable map of the complex networks underpinning the functioning of cells. Cellular processes often involve complex networking of several genes and transcription factors, and their temporal structure can often be accurately described in terms of pathways. A key problem in obtaining a computational understanding of these systems is the incomplete and noisy nature of most data: while certain relevant quantities, such as mRNA concentrations, can be measured accurately in a high throughput fashion, others, such as transcription factor concentrations, are difficult to measure quantitatively.

Probabilistic machine learning techniques such as Bayesian Networks have emerged in recent years as one of the main computational tools. Starting from the pioneering work of Friedman et al. (J. of Comp. Biol., 2000), probabilistic models of gene networks have received considerable attention (for some more recent works, see e.g. Nachman et al 2004, Beal et al 2005, Sanguinetti et al 2006, Sabatti and James 2006, etc). Despite the success of this approach, outstanding tasks remain to be addressed. For example, it is very hard to formulate tractable models that take into account the combinatorial nature of gene regulation, and generalising genome-wide models to incorporate dynamical effects such as pathways presents formidable computational challenges.

The main aim of this workshop is to bring together researchers working on the many facets of these problem, providing a forum for discussion and giving focus to the future directions of research. We aim to involve some experimental biologists in order to foster collaborations between computational and experimental researchers.

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