P.S. Swain (McGill)
B.P. Ingalls (Waterloo)
M.C. Mackey (McGill)


Eric Cytrynbaum (UBC)
Michael Ellison (Alberta)
Tim Elston (North Carolina)
Eldon Emberly (Simon Fraser)
James E. Ferrell, Jr. (Stanford)
Jeff Hasty (UC San Diego)
Martin Howard (Imperial College London)
Terry Hwa (UC San Diego)
Pablo Iglesias (Johns Hopkins)
Mads Kaern (Ottawa )
Edda Klipp (Max Plank)
Andre Levchenko (Johns Hopkins)
David McMillen (Toronto)
Nick Monk (Kroto Research Institute, Sheffield)
Felix Naef (ISREC)
Vincent Noireaux (Minnesota)
Sharad Ramanathan (Harvard)
Chris Rao (Illinois)
Andrew Rutenberg (Dalhousie)
Anirvan Sengupta (Rutgers)
Eric Siggia (Rockefeller)
Victor Sourjik (Heidelberg)
John Tyson (Virginia Tech)
Alex Van Oudenaarden (MIT)
Jose Vilar (Sloan-Kettering Institute)
Ron Weiss (Princeton)
Ned Wingreen (Princeton)

Thematic Semester on
Applied Dynamical Systems
June-December 2007

New technologies have led to a huge increase in molecular level data in biological systems. As well as fueling the transition of molecular biology to a quantitative science, this data is also revealing new complexity in biochemical networks. Proteins and genes were initially thought to exist in linear pathways, with an upstream component activating only its downstream counterpart. Now connections and crosstalk between pathways is the norm, and it appears that the nonlinear dynamical properties of these networks determine not only cell physiology but also organismal development and behaviour.

Biochemical networks are highly complex, containing positive, negative and mixed feedbacks in conjunction with stochastic components and time delays. Tools and concepts from mathematics are essential for developing a predictive understanding: stochastic processes and (delay) differential equations provide accurate models, bifurcation analysis describes model dynamics, numerical simulation gives prediction, and statistics and Markov chain Monte Carlo methods fit predictions to data.

This workshop will bring together both modellers and experimentalists, and will focus on network "design". Although reproducing cellular behaviour with simulation has invaluable predictive power, the simulation may become almost as complex as the network itself. An intuitive understanding of network properties will possibly only be reached by deconstructing networks into functional modules as, for example, an electrical system can be deconstructed into its component transistors and switches. Such an approach has been invaluable in the physical sciences and the multi-disciplinary character of this workshop will provide momentum to its application to biology.

This workshop will be preceded by the minicourse on Quantitative Biology (September 22-23, 2007).

Sponsored by: