NYCCS/Computer Science Seminar
John Reinitz, SBU, Tuesday, October 14, 2008
"From Data to Dynamical Systems via Parallel Computing"
Abstract:
I will present a case study of how parallel computing can be used to
solve an important scientific problem. Animals develop from embryos
that self-organize their body patterns in the presence of noise, which
arises from molecular fluctuations and other sources. In order to
produce a viable organism, the variance associated with this noise
must be reduced, a process called "canalization" which was first
predicted in 1942 by C. H. Waddington. Using the early embryo of the
fruit fly Drosophila as an example, I will show how dynamical
interactions between genes reduce positional variance of gene
expression in the early embryo. The model takes the form of a
deterministic dynamical system with fluctuating maternal inputs, and
it is well supported by confirmed predictions of increased variance in
certain double mutants. Finally, I will show how the reduction in
variance can be understood in terms of trajectories in state space
which are governed by point attractors in the anterior of the embryo
and an attracting manifold in the posterior.
All of these biological results were obtained through high
performance parallel computing. Obtaining the dynamical model required
solving an inverse problem in which the 48 parameters of a system of
232 simultaneous nonlinear ODEs were determined from about 2100
observations of gene expression over time. This difficult numerical
problem requires about 50 CPU-days of computation and is best solved
in parallel. This is done by means of parallel simulated annealing,
using an algorithm devised in collaboration with Y. Deng and
K.-W. Chu. I will describe this algorithm as well as its strengths and
limitations an various parallel architectures, with emphasis on the
prospects for highly scalable speedup on BlueGene and other systems
with fast and balanced communication.