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Statistical inference for stochastic dynamical systems in biology

   School of Mathematics and Statistics

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  Dr Giorgos Minas, Dr Jochen Kursawe  Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

There are two possible (and related) projects here, and students are welcome to select the one they are more interested in.

Statistical inference for stochastic dynamical systems in biology I

Stochastic dynamical systems can describe the interactions governing biological processes. In many applications, such as the circadian clock or embryonic development, researchers are collecting time-course data to gain insights to dynamic behaviours and regulation. Statistical inference can be applied to these data to identify parameters and properties that would otherwise not be experimentally measurable. Key challenges for statistical inference in large dynamical systems are parameter identifiability and computational speed. Which parameters can be inferred given a specific type of data? Can we optimise the experimental design to make it most informative? Can we generate faster algorithms for a specific inference problem?

This project will use theoretical approaches to answer these questions. The candidate will develop new methodology that can help practitioners decide on their data collection and analysis routines. While this project focusses on dynamical systems in general, the results will be applicable to many real-world applications, including embryonic development, circadian rhythms, and dynamic regulation of physiology.

Statistical inference for stochastic dynamical systems in biology II

This project will develop stochastic models describing the oscillatory dynamics of gene expression during embryonic stem cell differentiation. It will also develop statistical methodology and computational algorithms to estimate model parameters using live-cell imaging data provided by the lab of our collaborator Dr Cerys Manning at the University of Manchester. Live-cell imaging is a powerful technique for real-time observation of the expression of targeted genes in single cells. These observations are important in understanding cellular processes, such as stem cell differentiation, which strongly depend on dynamic gene expression. Stem cell differentiation is a critical biological process for embryonic development, regeneration, and regenerative therapy approaches. Dr Cerys Manning has previously shown that gene expression oscillations are observed in stem cells of the central nervous system, and these are important for regulating the differentiation process. We now wish to unravel the mechanisms driving these oscillations. We also wish to examine the role of stochasticity in stem cell differentiation and its interplay with oscillations. For this purpose, we will develop stochastic models that will be fitted to the highly variable live-cell imaging data. Bayesian statistical methodology will be employed to estimate model parameters, quantify model uncertainty, perform model comparisons, and derive predictions. The ideal candidate will be interested in Bayesian statistics, dynamical systems, and stem cell differentiation. Background in at least one of the above subjects will be beneficial, but candidates with other backgrounds will be considered.

Both projects

For more information, please see the School's Postgraduate Research page, and in particular the information about Statistics PhD opportunities.

Funding Notes

Full funding (fees, plus stipend of approx. £15,840) is available for well-qualified students; we encourage applications as soon as possible to maximize your chances of being funded. UK, EU and other overseas students are all encouraged to apply. New PhD students would typically start in September 2022, but this is flexible. More information is available School's Postgraduate Research web page -- please see the link at the bottom of the project description.

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