Timers, Switches and Sparse Bayesian Networks for Circadian Rhythm Analysis
We are seeking a computationally trained student with a background in Mathematics, Engineering or Physics to join our research group investigating new approaches to form mathematical descriptions of complex biological networks. The BBSRC DTP Programme will enable a student from a computational background to translate to biological research, train in biostatistics, control theory and dynamical modelling, and gain experience of experimentation with the potential for impact in food security. The student will be based at the University of Cambridge in the Department of Plant Science co-supervised by Professor Alex Webb and Professor Jorge Goncalves (University of Luxembourg). The student will have the opportunity to perform part of the training at the University of Luxembourg.
We have developed new tools for the analysis of circadian systems based on control theory. Our tools provide new insight when biological knowledge is limiting. We have used linear modelling to a global understanding of the circadian control of biological timing through an external coincidence with light signalling (Dalchau et al., (2010) PNAS 107, 13171-13176) and developed a linear models of the connections within the central circadian oscillator identifying previously unidentified links in the system providing insight in to ELF4 and LUX function (Herrero et al., 2012 Plant Cell 24, 428-443). More recently, we have developed a new tool from control theory called the Nu gap to identify systems changes in response to chemical and genetic perturbation of the circadian system (Carignano et al. (2015) Decision and Control, IEEE Conference Decision and Control. 3193 - 3198; Carignano et al., submitted to Molecular Systems Biology).
We will train a mathematically-trained student in Systems Biology. To ensure true multi-disciplinary training we will encourage the student to perform a rotation in a wet lab environment. Additionally, all computation focused students in the Webb laboratory are expected to perform some routine experimentation. The bulk of the training will be associated with the computational aspects of the project. The student will take the Control course in the Engineering Department and real analysis from the Mathematics Department. Moreover, training in specific aspects of the project will also take place in a placement at the laboratory of Jorge Goncalves in Luxembourg. Theoretically, this project aims to develop novel methods for network inference from time-series, low sampling data. These methods are based on inverting exponential matrices, while guaranteeing uniqueness of the models. Network sparsity will be the main focus of the research to deal with noisy data.
The position is open to residents of the EU including the UK.
Applications should in the first instance be made to Professor Alex Webb at: email@example.com Please include a cover letter and a CV.
Review of applicants will begin immediately and will continue until the position is filled.