Physics Colloquium - Thursday, February 25th, 2010, 3:00 P.M.

Ryan Gutenkunst
Theoretical Biology and Biophysics & Center for Nonlinear Studies
Los Alamos National Laboratory

Nonlinear models with large numbers of difficult-to-measure parameters appear commonly in many fields of science, particularly in biology. Here I focus on modeling the dynamics of biochemical reaction networks and the spread of genetic variation among human populations. In both cases, estimating parameters and uncertainties is a major obstacle to developing useful models. I demonstrate that nonlinear models, particular of biochemical networks, exhibit a universal "sloppy" pattern of sensitivity to parameter variation; different directions in parameter space vary by orders of magnitude in their constraint. Consequently, predictions may be usefully constrained even when the available data only very poorly constrain individual parameter values. I also present a powerful method for inferring models of demographic history from population genetic data. With this method, we have developed the most complex statistically well-characterized model of human genetic history to date.