Combining Novel Optofluidics Assays and Evolutionary Computation for Drug Treatment Optimisation - Mathematics - EPSRC DTP funded PhD Studentship Ref: 2961

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.

Dr Ozgur Akman
Dr Stefano Pagliara

Streatham Campus, Exeter

Project Description
This project will develop new evolutionary computing, microfluidics and microscopy approaches to optimise drug and stress treatments in bacteria, contributing towards the pressing need for new technologies to address bacterial phenotypic heterogeneity and antibiotic resistance mechanisms.

Aim: The aim of this project is to develop novel computational algorithms and microfluidic approaches with which to optimise combinatorial drug and stress treatments in bacterial populations. This will be part of a broader interdisciplinary research program aiming to develop automated drug pipelines.

Background: Clonal bacterial populations are characterised by inter-cell heterogeneity. Under drug treatment, three subpopulations are identified: bacteria susceptible to the drug (S); persisters (P) that resume growth when the drug is removed; viable but non-culturable (VBNC) cells that survive the treatment but only resume growth following specific treatment. P and VBNC cells have been implicated in the relapse of disease (e.g. cholera) and may comprise reservoirs for the development of antibiotic resistance. Recently, the Pagliara group at Exeter has developed a novel microfluidics-microscopy platform for performing single-cell assays of E. coli growth, enabling the relative fraction of S, P and VBNC cells within a population to be reliably measured. This microfluidics-microscopy platform enables the parallelism required for evolutionary algorithms (EAs) to be realised in vitro, thereby opening up the possibility of leveraging EAs to systematically explore the drug-stress response surface of bacterial populations.

Project: This PhD studentship will allow you to take advantage of the expertise available in Mathematics, Computer Science, Physics and Biosciences at Exeter to apply evolutionary computation methods to an important biological problem, using a state-of-the art technology platform.  This platform enables the parallel investigation of phenotypic subpopulations within a clonal population. Specifically, it enables the measurement of multiple physiological parameters, such as growth rate, antibiotic susceptibility and resistance to environmental stressors for each cell within each subpopulation.

By adapting and extending evolutionary optimisation algorithms developed by the Akman group to optimise gene regulatory network models to experimental timeseries data, you will be involved in performing the following proof-of-principle experiments:

(i) Determination of the drug dosage minimising bacterial growth under different stresses (e.g. pH, temperature) using self-adaptive evolutionary algorithms (e.g. CMA-ES);
(ii) Simultaneous minimisation of bacterial growth and drug dosage using a multi-objective evolutionary algorithm (MOEA), potentially generating novel ways of selecting treatments that reduce cost and damage to the host (and also mitigate antibiotic resistance);
(iii) Simultaneous minimisation of growth in S, P and VBNC cells under different drug-stress combinations, to obtain a complete quantification of the differential response of the clonal subpopulations. This will provide a potential platform for the development of more precisely tuned treatment regimens (e.g. regimens killing a greater relative fraction of P/VBNC cells).

These experiments will require you to operate (and potentially help design) microfluidic devices that allow thousands of cells to be confined and manipulated, each in a tiny separate compartment (a few picoliters in volume). You will then use a high-resolution microscope to image each cell during the different phases of the experiments and assist in improving existing detection and segmentation algorithms for image analysis.

Core techniques: evolutionary computation; mathematical modelling; software engineering; microfluidics; microscopy; single-cell analysis; big data analysis.

Entry Requirements
You should have, or expect to achieve, at least a 2:1 Honours degree, or equivalent, in mathematics, computer science, physics, engineering or natural sciences. Experience in microscopy, microfluidics and/or image processing 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.


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

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

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:
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.