Stats4Grads: Emulation and Inference for Stochastic Systems Biology Models
8 June 2011 14:15 in CM221
Due to recent experimental advances in the area of systems biology,
the inference of rate parameters that feature in both genetic and
biochemical networks has become increasingly important. Here we
present a novel methodology for inference of such parameters in the
case of stochastic networks, based on concepts from the area of
computer models (emulation and history matching) combined with Bayes
Linear variance learning methodology. We apply these techniques to a
simple, analytically tractable Birth-Death process model, followed by
a more complex stochastic Prokaryotic Auto-regulatory Gene Network.
This talk will be a hopefully light introduction to stochastic models
and the above techniques, and will feature lots of pictures and
possibly some movies (if I have time).
Contact thomai.tsiftsi(@)durham.ac.uk for more information
See the Stats4Grads page for more details about this series.