About the Project
This project is aiming to develop adequate data-driven and model-based approaches and tools by utilising a combination of computational methods, including Machine Learning, Stochastic Simulations, Formal Verification, High Performance Computing and Multi-agent Systems. The project will allow designing, analysing and visualising the dynamics of biological cells, large bacterial populations, colonies and organisms. Several biological systems will be investigated using the case studies provided by the Faculty of Life Sciences, whilst a special attention will be paid to synthetic biological systems.
The project offers the candidate new opportunities to gain invaluable experience in bioinformatics, computational biology and synthetic biology. The successful candidate will join an interdisciplinary research team, collaboration between computer scientists and biologists. He/she will have the opportunity to work within a dynamic, effective and multi-disciplinary team, working closely partners both from academia and industry.
Candidates are expected to hold (or be about to obtain) a minimum 2:1 honours degree (or equivalent) in a related area / subject, e.g. Computer Science, Bioinformatics, Systems Biology, Synthetic Biology, Biological Sciences, etc. MSc, MA or relevant experience in a related discipline is highly desirable.
Candidates who have a good computer science background should be willing to learn more about modelling biological systems. Candidates who have less computing background should be willing to improve the programming skills and learn computational (e.g. data and model-based) approaches.
J. Fisher, T.A. Henzinger. Executable cell biology. Nature Biotechnology 25 (100), 1239, 2007.
S. Konur, H. Fellermann, L. M. Mierla, D. Sanassy, C. Ladroue, S. Kalvala, M. Gheorghe, N. Krasnogor. An integrated in silico simulation and biomatter compilation approach to cellular computation. Advances in Unconventional Computing, Springer, pp. 655-676, 2017. M. Gheorghe, S. Konur, F. Ipate. Kernel P Systems and Stochastic P Systems for Modelling and Formal Verification of Genetic Logic Gates. Advances in Unconventional Computing, Springer, pp. 661-675, 2017.
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