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Determination of parameter dependencies across diverse populations of neuro-endocrine cells

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.


Dr Joel Tabak, College of Medicine and Health
Dr James Rankin, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences

Project Description:

Neurons and endocrine cells generate pulses of electrical activity. The patterns of electrical activity vary from cell-to-cell, and characteristics of these patterns (pulse duration, amplitude, timing) are critical to the function performed by the cell. For instance, electric pulse duration and frequency determines the quantity of hormone released by endocrine cells. Electrical currents are produced by ion channels, which act as non-linear electric conductances. The interactions between these non-linear conductances generate the complex patterns of electrical activity. Thus, the exact activity pattern of a given cell depends in a complex way on the exact distribution, or combination of weightings, of the different ion channels.

To understand how different combinations of ion channel weightings result in a given pattern of electrical activity, we use mathematical models based on nonlinear differential equations that describe how the conductances vary with the cell electric potential and how they in turn change this potential. The problem with these models is that a lot of parameters are unknown, including the weightings of each channel. Electrophysiologists can record the electrical activity patterns from individual cells, but they cannot measure all the channel weightings from one cell. The objective of this funded PhD project is to extract information about the weightings from the measured electrical activity.

The successful candidate will generate a database of channel weighting combinations and compute the resulting electrical activity pattern associated with each combination. They will then apply machine learning and/or topological data analysis to this database. This will allow them to deduce the relationships between weightings across models that generate electrical activity patterns selected from a subset of the database. By identifying the relationships between channel weightings across a highly heterogeneous population we can pinpoint the rules that regulate electrical activity across heterogeneous populations of cells. These methods will then be applied to existing datasets of electrophysiological recordings from electrically active cells.

This project provides a unique opportunity to develop experience in machine learning and to receive training in mathematical modelling of neurons. This work will be done in close collaboration with experimentalists using cutting-edge methods that incorporate modelling and electrical recordings together. The student will therefore be exposed to multidisciplinary teamwork. If they so desire, they will also have the opportunity to learn electrophysiology and generate their own dataset of experimental electrical recordings.

Candidates with quantitative backgrounds (mathematics, physics, engineering, computer science) are encouraged to apply. Programming experience, knowledge of dynamical systems theory and experience in biological modelling are a plus. The successful candidate will also be expected to travel to conferences to present their work.

Funding Notes

For successful eligible applicants the studentship comprises:

- An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
- Payment of University tuition fees (UK/EU)
- Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

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