Prof Darren Wilkinson Professor of Stochastic Modelling
School of Mathematics, Statistics and Physics
Newcastle University

Research / Interests

Darren Wilkinson working at his computers


Although my background and particular expertise are both in Bayesian statistical inference, I have a broad range of research interests, cutting across mathematics, statistics, probability theory, modelling, computing science, and molecular and systems biology.

Most of my current research interests involve applications of Bayesian statistics to a variety of challenging problems in Molecular biology, Bioinformatics and Systems Biology. I am especially interested in parameter inference for dynamic biological models, and the use of approximate models and (stochastic) model emulators for rendering computationally prohibitive algorithms for "big data" more tractable. I'm also interested in the way that HPC and Cloud computing technology can help, guided by principles of Functional Programming, especially in the context of streaming data modelling. My research blog give some insight into my current interests.


Bayesian analysis, scalable Bayesian computation, and Bayesian software development; Dynamic and graphical models, and Markov processes; Statistical bioinformatics and stochastic models in computational systems biology; Functional programming, Cloud computing and e-Science technology for computational statistics and big data; Efficient Bayesian algorithms and Bayes linear methods.


I held a BBSRC Research Development Fellowship to investigate Integrative modelling of stochasticity, noise, heterogeneity and measurement error in the study of model biological systems, and this topic remains a major focus of my research. The project involved a combination of statistical modelling and experimental lab work exploiting model organisms Bacillus subtilis (a gram-positive bacterium) and Saccharomyces cerevisiae (budding yeast). I am still working closely with the Bacillus lab of Leendert Hamoen and the yeast lab of David Lydall.

Budding yeast
Yeast SGA robots In the Lydall lab we are using robotic genetic screens to identify mechanisms involved in DNA maintenance and the cellular response to telomere uncapping. A typical experiment can generate up to 40,000 high resolution images - this is "big data". Each image contains information on 384 different yeast mutants. Our image analysis pipeline therefore leads to around 15 million colony growth measurements, corresponding to around 400,000 time series. Analysis of this data poses considerable computational, as well as statistical, challenges. We have developed a computational analysis and modelling pipeline that we call Quantitative Fitness Analysis (QFA), allowing us to discover thousands of previously unknown genetic interactions. The slides of a recent talk I gave on the statistical analysis of the data generated by this experimental technology are available. Above are some photos relating to the yeast robotic genetics, and some more are available on flickr.

Bacillus subtilis
Bacillus subtilis data In the Hamoen lab we used live cell imaging and flow cytometry to study control and regulation of key phenotypic decision processes such as competence development, sporulation and cell division. We are currently focusing experimental work on the regulation of cellular motility. Above are some images showing heterogeneity in gene expression by using a GFP reporter, and some more are available on flickr.

The rationale for undertaking this project can be found in my recent Nature Reviews Genetics article: Stochastic modelling for quantitative description of heterogeneous biological systems.

Darren J Wilkinson Book: Stochastic Modelling for
	      Systems Biology

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