April 9, 2020 at 9:59 am #5393fspillParticipant
PhD Projects in Mathematical Biology with Fabian Spill, University of Birmingham
PhD projects on mitochondria, modelling of fear during pandemics or other disasters, cell shape and tumour evolution and cancer cell migration are currently available for a start in autumn 2020. I am always supportive of students who have their own ideas, so please contact me to discuss potential alternative projects.
Funding is available through competition for EU/UK students. To be considered please contact me informally at firstname.lastname@example.org as soon as possible, or apply online:
Title: Dynamics of mitochondria in health and disease
Mitochondria are not only the energy factories of the cell, but also involved in regulating many other cell functions, including cell death. The production of energy in healthy cells depends on the availability of oxygen. However, in many diseases, e.g. heart attacks or cancer, oxygen availability is limited. This may lead to cell death through specific molecular pathways that also involve the mitochondria. Interestingly, mitochondria are also highly dynamic, moving around in the cell and undergo fission and fusion.
In this project, we will develop mathematical models to uncover the coupling of dynamic mitochondrial organisation, energy production and cell death. We will predict how mitochondrial organisation is altered in diseases, and how this alteration may contribute to cell death. Ultimately, we aim to uncover how we can target mitochondrial organisation with drugs to decrease cell death e.g. following a heart attack, or how we can increase cell death during cancer therapies.
To maximize the impact on experimental research, the student will be co-supervised by Dr Daniel Tennant from the Institute of Metabolism and Systems Research. The student will therefore gain access to cutting edge experimental data, and the predictions will be validated by experiments in the Tennant lab. Further collaborators for this project will involve Dr Vijay Rajagopal (University of Melbourne), Dr Melanie Madhani (Institute of Cardiovascular Sciences), Prof. Michael Mak (Yale University).
The project may involve several of the following methods: differential equations, metabolic flux analysis, optimisation, agent based models, stochastic processes, image analysis, statistical analysis. Which methods are ultimately employed on this project will depend on the skills and interests of the successful candidate, and training on methods the candidate wishes to learn will be provided. The only essential requirement are strong academic performance (e.g. evidenced through an excellent degree in Mathematics, Theoretical Physics or related subjects), and motivation to work in an interdisciplinary environment.
Title: Mathematics of FEAR: How fear protects us from Dieseases and Accidents
Fear is a key driver for us to act cautiously. This is obvious for immediate threats, such as, running away from a bear or a fire. Here, our fear is similar to the one of animals. However, humans also posses a collective form of fear that may even appear irrational on an individual level, yet may collectively protect us from invisible risks.
The currently occurring COVID pandemic is a prime example where fear has led many countries to enact drastic measures to prevent or limit the spread of COVID. Here, the risk for some (e.g. young and healthy) may rationally be small, yet fear can drive people to act cautiously, limiting or preventing pandemic spread.
Air plane disasters are another example where the risk for an individual during one trip is very small, making fear of death irrational relative to the fear of other causes of death. Yet, one may argue that air planes are only safe because many people fear flying, thereby, forcing the industry to implement stringent safety measures.
In this project, the student will develop mathematical models to explain how fear, rational or irrational on an individual level, may lead to a benefit to society by reducing the total risk level. We will therefore employ agent based models, game theory and complex systems to demonstrate how fear can be a key driver to reduce death.
This PhD project will be jointly supervised by Dr. Fabian Spill and Dr. Samuel Johnson.
Title: Dynamic interplay of cell shape and tumour evolution
Tumour cells are long known to appear in vastly diverse shapes, both across patients and within a single tumour. This is in stark contrast to the regularity of cell shapes of healthy epithelial tissues from which most tumours originate. However, regularity does not mean homogeneity: different types of cells in different parts of organs may also exhibit strongly varying cell shapes. Such shape variations assist pathologists in distinguishing healthy and cancerous tissue. Yet, little is known about the active role shape variations play during tumour evolution, and how shape contributes to resistance.
In this project, we will develop new mathematical and physical models to uncover how shape variations actively change cell behaviour and thus contribute to the evolutionary dynamics affecting disease progression and treatment response. We will then validate theoretical predictions with in vitro data from our experimental collaborators, relating cell shape to cellular behaviour for various kinds of cells. Specifically, we will compare healthy and cancerous cells. Moreover, we will investigate in vivo data of colorectal cancers of various stages (ranging from healthy tissue to advanced metastatic cancers) and infer causal relationships between genetically or environmentally driven shape alterations and cell behaviour. The project may be performed in close collaborations with experimental collaborators, for example, Prof. Chris Bakal (Cancer cell shape, Institute of Cancer Research London)
Methods: The project may involve several of the following methods: Partial differential equations, differential geometry, asymptotic analysis, numerical simulations, agent based models, stochastic processes, image analysis, statistical analysis. Which methods are ultimately employed on this project will depend on the skills and interests of the successful candidate, and training on methods the candidate wishes to learn will be provided. The only essential requirement are strong academic performance (e.g. evidenced through an excellent degree in Mathematics, Theoretical Physics or related subjects), and motivation to work in an interdisciplinary environment.
Title: Mathematical Modelling of Cancer-Cell Transmigration Through Blood Vessels
Cancer rarely kills through the primary tumour in which cancer arises, e.g. the prostate in prostate cancer. Instead, cancer cells may travel through the vasculature and colonialize distant organs – a process termed metastasis. A critical step of metastasis is the crossing of the cancer cells through the endothelium – the layer of endothelial cells that line the inside of blood vessels.
This endothelium is a highly dynamical structure that regulates transport of nutrients and oxygen to the tissue in healthy humans. It also allows for formation of gaps that allow immune cells to enter infected tissues. However, precisely these gaps are exploited by the cancer cells to invade the tissues. Recent research has shown that the regulation of these gaps is a complicated project, involving endothelial-cell internal signalling, the mechanics of the endothelium, and the mechano-chemical interactions of the endothelium with the extravasating cells.
Here, we will construct mechanochemical models of cancer-endothelial interactions. We will use this model to probe the major contributing factors to the process of transmigration. The model predictions will be validated through collaborations with leading experimental groups that can measure the forces during this process, such as the group of our collaborators Prof. Emad Moeendarbary at UCL. We will also investigate the bi-directional molecular signalling cascades between cancer and endothelium, and how this signalling contributes to altered cell mechanics and gap opening.
Methods: The project may involve several of the following methods: Discrete or continuum mechanics, Monte Carlo simulations, multiscale modelling, data analysis
The only essential requirement are strong academic performance (e.g. evidenced through an excellent degree in Mathematics, Theoretical Physics or related subjects), and motivation to work in an interdisciplinary environment.
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