Stochastic Models for Intracellular Reaction Networks

With the completion of numerous genome projects for bacteria, yeast, and humans, there is an increasing interest in understanding how molecules encoded within the genomes interact to define various functional networks of the cell. These intra-cellular networks may be classified as gene regulatory networks, protein interaction networks, or metabolic networks. Such networks of integrated molecular reactions tend to involve many different molecular species, thus posing complex analytical problems. For prediction and simulation purposes it is essential to reduce both the model and computational complexity of the problem, while still capturing all the essential characteristics and potential behavior of the network. This workshop will summarize some of the new and classical approaches to developing stochastic models for chemical reaction networks, beginning with Markov chain models and proceeding to some more recent ones which take into account the stepwise development of reactions involving RNA and DNA molecules. The workshop will focus on the following topics:

• Stochastic models of reaction networks.
• Structural properties of reaction networks and the relationship between structural properties and system behavior.
• Simulation of high dimensional systems.
• Parameter estimation and model validation for complex stochastic models.

The workshop will be proceeded by a tutorial that will help participants develop the common base of knowledge that will make the workshop presentations accessible.