Mathematics: Fully Funded Joint PhD Scholarship: Computational and Mathematical Approaches to Understand Cancer Cells Metabolism Reprogramming and Consequences
Closing date: 20 July 2020
This scholarship is funded by Swansea University and Université Grenoble Alpes through Swansea University Strategic Partner Research Scholarship (SUSPRS) scheme.
Fully Funded Joint PhD Scholarship: Computational and Mathematical Approaches to Understand Cancer Cells Metabolism Reprogramming and Consequences for Therapeutic Optimization
Start date: October 2020
Subject areas: Applied Mathematics, Mathematical and Computational Biology
- Dr Angélique Stéphanou (TIMC, Université Grenoble Alpes)
- Dr Gibin Powathil (Mathematics, College of Science, Swansea University)
This work will be realized in collaboration with Dr Annabelle Ballesta (Leader of Equipe Avenir “Multiscale System Pharmacology Approach to Customizing Anticancer Chemotherapy”, INSERM / Université Paris 11)
Background and project description
Tumour metabolism is currently an emerging topic in cancer research with a view to explore effective treatment options. It is now accepted that cancer cells metabolism reprogramming is a central event in tumour emergence and progression.
The evolution of cell metabolism within a tumour is very heterogeneous since the cells are subjected to different environmental constraints – including oxygenation, access to glucose and acidity – depending on whether they are at the heart of the tumour or at its periphery. In addition, the tumour itself is a mosaic of cells with intracellular variations and different sensitivities. This makes the understanding of metabolic changes extremely complex.
The objective of the thesis is to address this issue through a theoretical modelling approach informed by experimental data. An integrated multiscale model will be developed to grasp the complexity of the problem by dynamically integrating the effects of the environment on cell metabolic networks.
The model will present a hybrid formulation that will couple continuous partial differential equations (PDEs) - to describe the concentration of the main energetic substrates (oxygen, glucose, lactate) in the tumour environment - to an agent-based formulation so as to assign to each tumour cell its own individual characteristics to account for cellular heterogeneity. The metabolic networks implemented within each cell will allow to describe the biochemical reactions and this will be modelled using ordinary differential equations (ODEs). The overall model implementation will be realized using the PhysiCells software (Ghaffarizadeh et al., 2018) which optimizes computational speeds and allows to simulate a large number of cells (of the order of the million) to correspond qualitatively and quantitatively to the experiments that will be carried out on tumour spheroids.
Advanced fluorescent microscopy techniques, currently under development in the Grenoble team, will be used to characterize the metabolism of different tumour cell lines – grown as tumour spheroids. Experimental data will therefore be available to calibrate and validate the model.
One main focus of the PhD project will be to use the metabolic model to test the potential of known metabolic drugs as anticancer agents by selecting those that interfere with targeted biochemical pathways. In this context, deep learning algorithms will be developed and exploited for both drug screening and therapeutic optimization.
This is a multidisciplinary project at the interface between mathematics, computing, biology and medicine. The objective is to gain some understanding and control on the cell metabolism – through a mathematical model – to identify new therapeutic strategies and make an impact on healthcare.
The Biomathematics Group in Swansea possesses a leading expertise in the development of integrated multiscale models that address tumour development and therapeutic issues.
The TIMC Laboratory in Grenoble gathers scientists and clinicians towards the use of computer science and applied mathematics for understanding and controlling normal and pathological processes in biology and healthcare.
Therapeutic optimization will be achieved by exploiting new deep learning approaches. In this context, Grenoble was recently selected as one of the four leading poles in France for artificial intelligence and the Multidisciplinary Institute for Artificial Intelligence (MIAI) was just launched. The TIMC Laboratory holds 3 of the 4 health-related MIAI chairs and will play a leading role in the development of AI approaches to health. Our project will both benefits and contributes to this.
As this is a joint degree, applicants must meet the entry/funder requirements of both universities: a recognised master’s degree in Civil/Mechanical/Aerospace/Medical Engineering or similar discipline, and an appropriate English language qualification, ELTS 6.5 overall (with at least 6.5 in each individual component) or Swansea University recognised equivalent.
Applicant must hold a Master’s Degree (M2 Research Master with distinction) or an Engineering Diploma in a relevant discipline that include Applied Mathematics, Biophysics, Bioinformatics, Computational Biology, Biomedical Engineering.
- Knowledge in cancer biology is not essential but the candidate will be required to learn appropriate biological and clinical knowledge.
- The candidate is expected to have a strong applied mathematical or related subject background such as numerical analysis, physics, etc. The project involves the use of PDEs, ODEs and discrete modelling (agent-based).
- Strong computational and programming skills are mandatory. The preferred programming languages are C++ and python, some knowledge in R and Matlab would be a plus.
- Strong interpersonal skills and the capacity to work and learn independently will be required.
Please see the links below for details regarding non-UK qualification comparisons and accepted English Language qualifications:
Due to funding restrictions, this scholarship is open to UK/EU candidates only.
This scholarship covers the full cost of UK/EU tuition fees (50% by Swansea University, 50% by Université Grenoble Alpes) and an annual stipend of £15,285 reviewed annually in line with UKRI rates.
Additional funding is available from Swansea University to assist with travel, accommodation and immersive training experiences.
How to Apply
To apply, please complete and submit the following documents to firstname.lastname@example.org:
- Joint-Collaborative Research Application Form
- Covering letter that addresses the following points:
- motivation for undertaking a PhD thesis
- demonstration that the candidate profile matches the position criteria
- preferred arrangements concerning time sharing between Grenoble and Swansea
- choice of the main university (Grenoble or Swansea) with justification
- Transcripts for bachelor's degree and transcript/details of grades to date for master’s degree
- Two references- if a referee does not wish to provide an open reference, the referee can send independently, NOTING SPECIFICALLY the name of the student AND the project being applied for to email@example.com
- English language qualification
- Copy of the French Baccalaureate or other EEA high school qualification if applicable - www.swansea.ac.uk/admissions/english-language-requirements/eu-eea-students/#d.en.19639
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