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Workshop 2: Regulatory Networks (November 8-12, 2004)

Organizers: Jeff Hasty, Ralf Bundschuh, and Fernand Hayot

It has been over 40 years since Monod and Jacob boldly predicted that such fundamental cellular processes as differentiation and protein regulation are accomplished through signaling pathways resident at the level of the gene. This prediction laid the foundation for the ensuing progress in describing the essential regulatory mechanisms in many specific genetic systems. With the development of the field of nonlinear dynamics and the concurrent advent of available computing, mathematical models describing gene regulation began to appear regularly in the 1970s. Implicit in these studies was the realization that the "wiring" of naturally-occurring gene regulatory networks would be too complex for qualitative descriptions devoid of mathematics. Though this realization proved to be ahead of its time, mainly due to the lack of experimentally deduced regulatory pathways in the "pre-genomic" era, recent experimental advances in both sequencing and genetic engineering have made the analysis and design of gene networks amenable to quantitative analysis. These advances have reignited interest in gene regulatory models that can be used to explain and predict behavior that emerges from gene regulatory networks.

In this workshop, we will focus on recent advances in utilizing mathematical models to describe gene regulatory networks. The workshop will begin by introducing mathematical modeling techniques, including boolean representations, classical kinetic theory, stochastic simulation methods and constraints-based models. This will be followed by descriptions of specific gene regulatory networks, such as those related to the lambda and T7 life cycles, drosophilia segmentation, and xenopus oocytes differentiation. The remainder of the workshop will be devoted to the construction and analysis of synthetic gene regulatory networks, including switches, oscillators, autoregulation, and noise analysis. The workshop will highlight the utility, which an accurate mathematical description of synthetic networks provides, in describing complex naturally-occurring networks.