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Quantitative models for optimised cell-free gene expression

  • Full or part time
    Dr N Laohakunakorn
    Dr D Oyarzun
  • Application Deadline
    Sunday, January 05, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Synthetic biology is a rapidly growing field set to make profound impact in numerous industries including manufacturing, healthcare, agriculture, and sustainable energy, as well as our fundamental understanding of life itself. Progress has been hindered, however, by the difficulty in engineering biological systems reliably. One promising approach is to use cell-free expression systems [1]. These are in vitro systems that mimic the cellular environment and can be integrated with microfluidic technologies to rapidly prototype and screen synthetic gene circuits, prior to their deployment in vivo.

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 [2].

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.

Funding Notes

The “Visit Website” button on this page will take you to our Online Application checklist. Please complete each step and download the checklist which will provide a list of funding options and guide you through the application process.

If you would like us to consider you for one of our scholarships you must apply by 5 January 2020 at the latest.

References

1. Swank, Z., Laohakunakorn, N., and Maerkl, S. J. Cell-free gene-regulatory network engineering with synthetic transcription factors. PNAS 116:13, 5892-5901 (2019)
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)

How good is research at University of Edinburgh in Biological Sciences?

FTE Category A staff submitted: 109.70

Research output data provided by the Research Excellence Framework (REF)

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