With the current explosion in biological data, from the sequencing of the human genome to the invention of DNA chips, the life sciences have been propelled into the quantitative era. There is a demand in academia, government, and industry for people who speak the languages of both the physical and the biological sciences. The CND summer school is precisely aimed at satisfying this demand by providing the knowledge needed to boost the move from one side of this divide to the other.

Applicants are encouraged from both the physical and the life sciences, though at least one year of calculus is needed. Each day will consist of a morning of lectures followed by computational lab work in the afternoon. The ultimate goal of the new biology, which has been loosely classified as Systems Biology, is to produce a computational model of a biological system which allows accurate, experimentally verifiable prediction at the molecular level. The summer school will provide a solid introduction to the fundamental science on which Systems Biology is based. Each lecture will explore the concepts and basic mathematical ideas of a different strand of Systems Biology and will be illustrated with examples from recently published research. Participants will develop a set of tools and a vocabulary which should allow them to discuss and become actively involved in any problem in quantitative biology. They will become familiar with the powerful Matlab programming language as well as the (free) xpp software package.

Topics to be covered will include linear and non-linear systems, chaos, bifurcation theory, stochastic systems, differential delay equations, Boolean models, cellular automata, and statistical analysis (clustering). Applications will range from the molecular (gene expression, neural signal propagation, DNA chips - microarrays, bacterial genetic regulation) to the systems (cardiology, hematology, neural control, sensory transduction) level. Each lecture will be delivered by an expert working in the field and at the end of the course participants will be familiar with the research directions available in quantitative Systems Biology.

9 a.m. - 12:30 p.m.		1:30 p.m. - 5:00 p.m.
DAY	LECTURES	LABORATORIES	PROFESSOR
McIntyre Building, Room 1034		McIntyre Building, Rooms 409, 1009, 1010
May 20	Nonlinear Dynamics of Biological Oscillations		Leon Glass McGill University Jacques Bélair Université de Montréal
	Periodic Forcing of Oscillators	Iterating Finite Difference Equations
May 21	Bifurcations and Excitable Systems		Michael Guevara McGill University
	1. Bifurcations in Physiological Systems Involving Steady States 2. Bifurcations in Physiological Systems Involving Limit Cycles	Analysis of the FitzHugh-Nagumo and the Hodgkin-Huxley Equations
May 22	Cardiac Dynamics		Michael Guevara McGill University Alain Vinet Université de Montréal
	1. Excitable Cells: From the Squid to the Hodgkin-Huxley Equations 2. Dynamics of Cardiac Arrhythmias	Cellular Automata Model of Electrical Propagation in Cardiac Excitable Tissue
May 23	Burst Firing and Sensory Processing		Maurice Chacron McGill University
	1. Bifurcations Leading to Burst Firing 2. Possible Functions of Burst Firing	Information Transmission by Action Potential Patterns
May 24	Weekend
May 25	Weekend
May 26	Modelling Cell Replication and Control		Michael C. Mackey McGill University
	1. Periodic Hematological Disease 2. Analysis of Differential Delay Equations and the Understanding of Cyclical Neutropenia	Delay Differential Equations and Control of Erythrocyte Production: Understanding through a Study of Periodic Anemia
May 27	Modelling Gene Networks in Prokaryotes		Moisés Santillán Centro de Investigación y de Estudios Avanzados del IPN, Mexico
	Dynamics of Bacterial Gene Regulation	Dynamics of the Lac and Tryptophan Operons
May 28	Constructing In Vivo Gene Networks		Mads Kaern University of Ottawa
	Synthetic Gene Circuits	Continuation and Bifurcations
May 29
	The Inverse Problem for Genetic Networks		Ted Perkins McGill University
	Discrete and Continuous Time Model-Fitting, Structure Search, and Integrating Multiple Sources of Evidence	Discovery and Refinement of Gene Networks
May 30	Stochastic Modelling of Biochemical Reactions		Peter Swain McGill University
	1. The Master Equation 2. Approximations: Langevin Theory, Computer Simulation	Simulating Noisy Gene Expression