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Institution: University of Southampton
United Kingdom
Retrieved : 2017-11-21 Expired
Description :

PhD Supervisors: Dr Salah Elias (Biological Sciences)

Dr Philip Greulich (Mathematical Sciences)


Application Deadline: 08 December 2017 

Interviews will be held mid December 2017


PhD Description

Adult stem cells of the mammary gland (in the female mammalian breast) drive its growth and remodelling during puberty and pregnancy. The functions and dynamics of these stem cells have considerable implications for the potential emergence of breast cancer. It remains poorly understood how these cells balance proliferation and differentiation programs during development and homeostasis, which is essential to maintain a healthy tissue and avoid cancer. 

The PhD project combines expertise in stem cell and mathematical/computational research, and aims to reveal whether the developmental control of oriented cell division in dividing mammary stem cells (MaSCs) represents a critical mechanism to balance cell fate choices, which influences epithelial differentiation and growth. To this end, data from cutting-edge experimental methodologies, including single-cell lineage tracing in vivo, and quantitative three-dimensional (3D) confocal imaging will be analyzed and used for mathematical/computational modelling, the latter to be undertaken by the PhD student. In order to test hypotheses about the influence of mitotic spindle dynamic orientation on cell fate outcomes, the PhD student will translate the hypotheses into stochastic models for cell fate dynamics, including spindle orientation, cell divisions (determined or not by spindle orientations) and stochastic cell fate choices. The mathematical models will be evaluated via mathematical techniques, such as solution approaches for the stochastic Master equation (coupled ordinary differential equations), and numerical techniques, such as Monte Carlo simulations. The results of this evaluation, which yields predicted clonal distributions, will be compared with the clonal data via Bayesian inference, whereby non-compliant hypotheses can be rejected and compliant ones can be scored for their certainty. The outcomes of this project are expected to provide important novel insight into the identity, dynamics and differentiation potential of adult MaSCs.

Training and Development Opportunities

This is a collaborative project between Biological and Mathematical Sciences, which are part of the Institute for Life Sciences (IfLS). IfLS is an interdisciplinary endeavour and offers a unique and exciting collaborative research environment and will provide the PhD student with support, mentoring, training and development opportunities. There is an international research seminar series and courses in mathematical modelling and biological systems, which will benefit for the PhD student during his/her training. The PhD student will be given opportunities to present the results of the project at major national and international conferences. The proximity of Biological and Mathematical Sciences and the specialized expertise that each contributes will provide the PhD student a unique environment to achieve the maximum impact of this project.

Knowledge, Skills and Experience Needed for the Position

Successful candidates are expected to be highly motivated, enthusiastic and have an excellent academic track record. Applicants must have, or are on track for, at least an upper second class degree or Master’s degree in a quantitative subject such as Quantitative Biological Sciences, Physical Sciences, or Computational/Mathematical Sciences.

A firm mathematical background is required. Experience in either dynamical systems or stochastic processes/models is preferred. Previous experience in computational modelling and/or bioinformatics tools is desirable. Experience/understanding of cell and/or stem cell biology is a plus but not essential. A strong interest for biological problems, the ability to acquire new quantitative skills, and to work independently and collaboratively, as part of a research team, is absolutely essential. The PhD student must be an effective communicator with an ability to present his/her work both within the laboratory and to others in the field.

Funding Notes

This is a 3-year project, currently partly funded by IfLS (Availability of full funding will be confirmed around January-February) and welcomes applicants from the UK and EU who have or expect to obtain at least an upper second class degree in Quantitative Biological Sciences, Computational/Mathematical Sciences or related fields. Funding will cover fees and a stipend at current research council rates of £ 14,533 per annum for 2017/18. 

Due to funding restrictions this position is only open to UK/EU applicants

If you wish to apply for this project, please contact Dr. Philip Greulich at P.S.Greulich@soton.ac.uk and/or Dr. Salah Elias at S.K.Elias@soton.ac.uk and submit your Application; CV, a transcript of grades and a short paragraph explaining your background and motivation relevant for this project via the online admissions weblink in Biological Sciences:

https://studentrecords.soton.ac.uk/BNNRPROD/bzsksrch.P_Login?pos=4973&majr=4973&term=201718Please place Dr Philip Greulich and Dr Salah Elias names in the field for proposed supervisor/s.

Any queries on the application process should be made to pgafnes@soton.ac.uk

Applications will be considered in the order that they are received, and the position will be considered filled when a suitable candidate has been identified.

The University of Southampton holds an Athena Swan Silver Award and Biological Sciences as well as Mathematical Sciences both hold an Athena Swan Bronze Award, respectively, demonstrating their commitment to provide equal opportunities and to advance the representation of women in STEM/M subjects: science, technology, engineering, mathematics and medicine. Due consideration will be given to applicants who have taken a career break.  University benefits include onsite childcare facilities, state-of-the-art on-campus sports, arts and culture facilities, a full programme of events and a range of staff discounts.

Closing Date: 08 Dec 2017
Post Type: PhD Studentship (Funded)





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