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Mathematical Modelling of the Regulation of Circadian Period

Supervisor: Alex Webb (Plant Sciences) with Professor Jorge Goncalves (University of Luxembourg)

The student will be trained in mathematical and experimental techniques to determine how plants adjust the speed of the circadian clock.

We discovered that circadian period is not fixed, but is instead plastic, and can be dynamically adjusted by metabolites such as NAD derivatives (Dodd et al, Science 2007) and sugars (Haydon et al., Nature 2013). We have identified an entirely new class of circadian mutants that are compromised specifically in the ability to dynamically alter circadian period in response to metabolites.

This is an interdisciplinary project that will appeal to a mathematically-trained undergraduate that wishes to transfer their skills to biological problems. The student will model the transcriptional networks in plants adjusting circadian period in response to stimulation, and in our mutant plants in which this response is compromised. RNA seq from wild-type and mutants across a circadian time course in the presence or absence of nicotinamide will be used to capture the dynamics of the system. The student will be trained in the analysis of the RNA seq data using tools such as BAYSEQ. The student will reconstruct the network using a new approach developed by my laboratory based on the use of linear models in a Sparse Baysean Network to describe the connections between the genes that comprise the circadian oscillator (Dalchau et al PNAS 2010).

To determine how the system changes in response to the metabolite nicotinamide, and the effect of the mutations on systems dynamics, the student will be trained in Control Theory approaches that we have translated to biology in which the Nu gap metric identifies region of system change. This will identify the network alterations that regulate circadian period. The biological purpose of changes in circadian period will be addressed through a combined modelling and experimental approach in which the student will be trained in the use of the BIODARE model repository to simulate the performance of the circadian system with and without dynamic period adjustment. Training in biological approaches will permit the student to test model predictions using our unique mutant population compromised in dynamic circadian period adjustment.

The training programme will enable a student from a computational background to translate to biological research, gain training in biostatistics, control theory and dynamical modelling, and gain experience of experimentation in the context of making use of a unique mutant population addressing a new fundamental question with the potential for impact in food security.

More studentships in the Webb lab and background information.