Determination of Parameter Dependencies Across Diverse Populations of Neuronal and Excitable Cells - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2932

About the award

This project is one of a number funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2018. This project is in direct competition with others for funding; the projects which receive the best applicants will be awarded the funding.

The studentships will provide funding for a stipend which is currently £14,553 per annum for 2017-2018. It will provide research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.

Please note that of the total number of projects within the competition, up to 15 studentships will be filled.

Supervisors
Dr James Rankin, University of Exeter
Dr Joel Tabak
, University of Exeter

Location 
Streatham Campus, Exeter

Project Description
This interdisciplinary project will develop mathematical and computational methods for determining neuron model parameter dependencies from electrical activity recorded in highly heterogeneous cell populations.  The project will use methods from topological data analysis in conjunction with tools for studying differential equations to optimally constrain model parameters. The project will focus on data recorded from pituitary cells, but the methods developed will be applicable to neural recordings from any brain region.

The pituitary gland, attached to the base of the brain, interacts with the hypothalamus to regulate several physiological processes including growth, stress and reproduction. Specific cell types are associated with these processes, e.g., corticotrophs with stress responses and gonadotrophs with reproduction. Patch-clamp recordings have revealed a wide range of spiking activity patterns in pituitary cells, patterns that vary in features like frequency, amplitude, duration. These features determine how much hormone is released. The cell population as a whole is highly heterogeneous across cell types and, surprisingly, within cell types. Hodgkin-Huxley models (systems of differential equations) can capture this heterogeneity, with differing weightings given to the known ion channel types (e.g. calcium, potassium) in pituitary cells. Top-down modulation from the brain, drugs, and pollutants can all affect one or more channel weightings. This changes electrical activity, resulting in changes in hormone secretion. To understand how endocrine disruptors or drug side effects may cause hormone imbalances, and to determine how to counteract these effects, we must determine how variability in channel weightings determines the observed heterogeneity in electrical activity patterns.

Different models, corresponding to different combinations of channel weightings, may produce very similar electrical activity patterns (parameter estimation is non-unique). For real cells, it is hypothesised that the dimensionality of parameter space is lower than the number of channel weightings (e.g. because certain weightings co-vary). Topological data analysis allows for dimension reduction to be determined in a manner robust to the non-uniqueness of parameter estimation. This approach can reveal co-dependencies of channel regulation across cell populations that cannot be determined in experiments. By identifying the systematic changes to channel weightings across a highly heterogeneous population, this approach can pinpoint how population level regulation functions in response to e.g. drugs, pollutants. These methods will be applied to existing datasets of electrophysiological recordings from pituitary cells and other neural populations. 


The successful candidate will receive training in the analysis of Hodgkin-Huxley type neuron models with dynamical systems methods. The research will develop new methods based on topological data analysis to understand the role of heterogeneity in cell populations and the function of population-level modulation in regulating important physiological processes. This project provides a unique opportunity to receive training in mathematical modelling in close collaboration with experimentalists using cutting-edge methods that incorporate modelling and electrical recordings together.

Entry Requirements
You should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Mathematics, Physics, Engineering or Neuroscience. Experience in Dynamical Systems, Computational Neuroscience or Mathematical Biology is desirable.


If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information and a list of acceptable alternative tests.

The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs. To be eligible for fees-only funding you must be ordinarily resident in a member state of the EU.  For information on EPSRC residency criteria click here.

Applicants who are classed as International for tuition fee purposes are NOT eligible for funding. International students interested in studying at the University of Exeter should search our funding database for alternative options.

Summary

Application deadline:10th January 2018
Value:3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,553 per year.
Duration of award:per year
Contact: Doctoral Collegepgrenquiries@exeter.ac.uk

How to apply

You will be required to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to
        undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim
        transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current
        proficiency in English.  For further details of the University’s English language requirements please see
        http://www.exeter.ac.uk/postgraduate/apply/english/.

The closing date for applications is midnight (GMT) on Wednesday 10 January 2018.  Interviews will be held at the University of Exeter in late February 2018.

If you have any general enquiries about the application process please email: pgrenquiries@exeter.ac.uk.
Project-specific queries should be directed to the supervisor.

During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.