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Thursday 30 Oct 2014Statistical Science Seminar: Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins

Prof. Richard Boys - Newcastle University

H101 15:00-16:00

The presence of protein aggregates in cells is a known
feature of many human age-related diseases, such as Huntington's
disease. Simulations using fixed parameter values in a model of the
dynamic evolution of expanded polyglutaime (PolyQ) proteins in cells
have been used to gain a better understanding of the biological
system, how to focus drug development and how to construct more
efficient designs of future laboratory-based in vitro experiments.
However, there is considerable uncertainty about the values of some of
the parameters governing the system. Currently, appropriate values are
chosen by ad hoc attempts to tune the parameters so that the model
output matches experimental data. The problem is further complicated
by the fact that the data only offer a very partial insight into the
underlying biological process. The data consist only of the
proportions of cell death and of cells with inclusion bodies at a few
time points, corrupted by measurement error.

Developing inference procedures to estimate the model parameters is a
significant task. Ideally the chemical species in the model would be
observed continuously in time, or at least some of them observed at a
reasonable number of time points. The model probabilities
corresponding to the observed proportions cannot be evaluated exactly
and so they are estimated within the inference algorithm by repeatedly
simulating realisations from the model. In general such an approach is
computationally very expensive and therefore we construct Gaussian
process emulators for the key quantities and reformulate our algorithm
around these fast stochastic approximations. We conclude by examining
the fit of our model and highlight new insights into appropriate
values of the model parameters.

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