Whole Cell Bayesian Parameter Inference

Due to advances in experimental techniques, it is now clear that cellular dynamics incorporate a vast array of heterogeneous components. While each component may be relatively simple, combining individual systems results in complex, temporal dynamics that can initially go against our intuition. The acknowledgement of this biological sophistication has highlighted that complex biological processes cannot be understood through the application of ever-more reductionist experimental programs. Instead, by formulating the system of interest into a mathematical framework, we can begin to combine disparate sources of knowledge.

Parameter inference for large models is non-trivial and requires both sophisticated computation and cutting edge statistics. The goal of this project is to perform Bayesian parameter inference for large stochastic models. Instead of treating the model as a single system, the network will be split into smaller sub-components. The impact of this approximation will be ameliorated using a delayed acceptance step in the parameter inference scheme.

Towards the end of the project, we apply the techniques developed to the whole cell parameter estimation DREAM challenge (https://www.synapse.org/#!Synapse:syn1876068/wiki/55673)

Successful applicants will also be given the opportunity to complete teaching and demonstrating duties within the school amounting to up to £1500 per annum.