Abstract:

Cyclic systems, such as the cell cycle, play an important role in many
biological processes. Recent advances in high-throughput experimental
methods in molecular biology are enabling researchers to obtain a global
view of the of the expression program of such systems. By developing
algorithms that combine diverse high throughput biological datasets we were
able to model some of these dynamic systems in yeast.  However, when moving
from model organisms to humans we face many new computational challenges.
Human systems are more complex, their temporal duration is longer and the
data is often noisier. In this talk I will present algorithms that combine
ideas from computer science, statistics and signal processing to address
these issues. Using these algorithms we were able to determine, for the
first time, the set of cycling human genes. This set can be used to
identify cancer related genes and to determine the core set of cell cycle
genes using a method we call comparative systems biology.