About the Project
The quantitative nature of data from cell-free experiments, as well as the amount of control over perturbations and composition of the system allow access to internal parameters otherwise hidden in living cells. This enables precise mathematical modelling and inference of the system’s behaviour, and opens up possibilities for truly rational approaches to gene circuit design .
The student will combine high-throughput microfluidics with model-building, parameter inference, and optimal experimental design strategies to develop a complete quantitative description of cell-free gene expression. In addition to developing deeper understanding of gene expression biophysics, the models will provide a quantitative platform for the optimisation of cell-free systems, with important implications for cell-free bioproduction of high-value chemicals.
The ideal candidate will be highly motivated, and possess a strong quantitative background in physics/engineering/computer science or related disciplines. Depending on the candidate’s interests, it will be possible to work on both computational and experimental aspects of the project. Experience in any of the following areas is highly desirable: numerical modelling, Bayesian inference, optimal experimental design, molecular biology, microfluidics.
The project will be jointly supervised by Dr Nadanai Laohakunakorn (https://nadanai263.github.io/) from the School of Biological Sciences, and Dr Diego Oyarún (http://homepages.inf.ed.ac.uk/doyarzun/) from the Schools of Biological Sciences and Informatics.
If you would like us to consider you for one of our scholarships you must apply by 5 January 2020 at the latest.
2. Halter, W., Allgöwer, F., Murray, R. M., and Gyorgy, A. Optimal experiment design and leveraging competition for shared resources in cell-free extracts, IEEE CDC (2018)
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.