Speaker
Description
A common challenge in mathematical biology arises from data collected from a biological system for which we have a mechanistic model, such as an ODE model. Identifying the parameters of the model is often done through estimation schemes such as non-linear least squares, which presume errors in the amplitude of the data. Such estimation can be challenging, and other sources of error, such as deviations in phase, might also be considered. Relatedly, curve registration is a set of techniques in the statistical analysis of functions to address variations in phase across a collection of observed curves. In this talk, we will present techniques to apply methods of curve registration to estimation of parameters from ordinary differential equations models. We will show how particle filtering methods can facilitate such inference in a computationally tractable way and allow us to quantify the phase variability in such data. A brief discussion of vaccine trials will be included to illustrate how characterizing such phase variability can give new insights into inter-subject biological variation.
