**PLEASE NOTE – the deadline for requesting a funding pack from Darwin Trust has now passed and completed funding applications must be submitted to Darwin Trust by 19th January. We can still accept applications for this project from self-funding students.
A gene regulatory network involves a set of genes interacting with each other to control cellular functions.
For example in autoregulation, a protein expressed from a gene activates or suppresses its own transcription, thereby regulating the number of proteins through negative or positive feedback [1].
Mathematical models of stochastic gene expression have provided insight into how intrinsic noise (due to transcriptional and translational processes) can be controlled via feedback mechanisms [1]. These models also have shown how noise can generate oscillations and multi-stable states. However these models ignore important sources of fluctuations such as those due to cell growth, cell division, DNA replication and cell size dependent transcription.
In this project, the student will build on recent advances [2] to construct a detailed model of stochastic model of gene regulation that includes these noise sources. A first aim is the precise quantification of how each different source of noise contributes to emergent phenomena observed at the single-cell level. A secondary aim is to obtain a reduced version of this detailed model by the modification of recently proposed AI techniques [3]. A final aim involves using the analytical solution of the detailed stochastic model to estimate the parameters of gene regulatory networks from single cell data.
The project will give the student a solid foundation in the basic molecular biology of transcription, and its modelling using stochastic simulations and the chemical master equation. No previous background on these topics is assumed, though experience in the analytical and numerical solution of ordinary differential equations and some experience in coding is necessary. The project is ideal for a student with a mathematics or physics bachelors degree who is interested in the quantitative modelling of living systems. The student will be based in the C. H. Waddington building which houses the Centre for Synthetic and Systems Biology at the University of Edinburgh.
For enquiries please contact Prof. Ramon Grima ([Email Address Removed])
https://grimagroup.bio.ed.ac.uk/home
The School of Biological Sciences is committed to Equality & Diversity: https://www.ed.ac.uk/biology/equality-and-diversity
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