My group works on how to learn models and make inferences given evidence from high-throughput biological datasets. The models that we develop range from mechanistic differential equation models of the cell to more abstract probabilistic latent variable models that can be used uncover interesting structure in high-dimensional data. We are particularly interested in hybrid models that combine aspects of mechanistic and probabilistic models.
Models encode our hypotheses about how biological systems work. We use probabilistic inference to learn the model parameters and to choose between competing models so as to identify the hypotheses best supported by the available experimental evidence. Bayesian inference and non-parametric modelling is a particular focus as this provides a principled framework for dealing with uncertainty in complex systems. We are applying our methods to infer gene regulatory networks from time-series mRNA expression and DNA-protein binding data, to uncover changes in the transcriptome from RNA-Seq datasets, and to develop novel inference algorithms for time-series data analysis and systems biology modelling.