The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

Investigating the evolution of dispersal using cultured cell lines
Dispersal and its evolution are central to ecology, impacting processes such as speciation, the maintenance of genetic diversity, and adaptation to changing environments. Extensive theory supports this view, but robust empirical data is lacking, partly due to practical difficulties with current model organisms (Duputie & Massol, Interface Focus 3:0028, 2013). We seek a student to address the evolution and genetics of dispersal using data from a novel model system: cultured cell lines derived from human cancers. The aim of this project is to assess the utility of cancer cells as model systems for understanding the evolutionary ecology of dispersal where there is the potential for complex epigenetic control, and to use empirical data to generate robust evolutionary models that can be generalised to other organisms Key questions in dispersal depend on a) the genetic and epigenetic basis of dispersal phenotypes, and b) the connections between dispersal and normal movement patterns during an organism’s life. Dispersal theories can be tested in the lab with microbial models (Kümmerli et al. Evolution 63:939-49, 2009; Taylor et al. J Evol Biol 26:2644-53, 2013), but these may be overly simple representations of dispersal traits in complex organisms, which are often polygenic and plastic (Saastamoinen et al. Biol. Rev. doi: 10.1111/brv.12356, 2017). Cell lines, like microbes, are small, tractable, clonal, and can be frozen (Taylor et al. Evol. Appl. 6: 535-548, 2013). However, they exhibit complex epigenetic control of phenotype, and appear to show plastic dispersal. Low-nutrient environments can result in increased motility both in the short term, presumably due to plasticity, and longer term, which may be the result of selection (Taylor et al, under review). Lines are available with varying motility and hence dispersal phenotypes. By tracking cells and their descendants, we have found a high broad-sense heritability of motility in cancer cell populations in vitro (Wass et al. in prep.), suggesting cell lines should rapidly respond to selection on motility. This offers an easily identifiable phenotype on which to track the evolution of dispersal traits. Cell motility is a key component of metastasis, which has been analogised to dispersal, although there is debate over the extent to which this analogy is helpful (Arnal et al. Evol. Appl. 8.6 (2015): 541-544)). This multidisciplinary project, using data from in vivo and in vitro cell tracking, will use large datasets of cell behaviour and lines of descent, and agent-based modelling informed and parameterised by real data. Evolutionary processes will be explored under alternative micro-environments to assess the conditions which drive disparate evolutionary pathways. Using video and image analysis, we will quantify variation within populations and correlations between cell relatives, measure population-level responses to environmental change, and examine patterns of motility inheritance. Modelling will involve agent-based simulations and analytical methods to represent the evolution of cell motility under complex genetic control. Insights will be gained into the role of genetic architecture in the evolution of dispersal. Colleagues and collaborators bring expertise in agent-based modelling (Sibly, Johnston), image analysis (Stumpf, Pruessner); cancer biology (Malanchi, Bonnet, Dash); experimental evolution (Brockhurst) and metastasis (Aznar-Benitah, Barcelona).
Louise Johnson
James Bullock
Cristina Lo Celso, Life Sciences, Imperial College c.lo-celso@imperial.ac.uk; Tiffany B. Taylor, Department of Biology and Biochemsitry, University of Bath t.b.taylor@bath.ac.uk; Philip Dash, Reading p.r.dash@reading.ac.uk
Ecological observations / data collection, Evolutionary observations / data collection
James Bullock
The student will analyse large datasets of motility data from time-lapse microscopy tracking lineages of cancer cells, developing key skills in statistics and particularly the analysis of large datasets. Developing agent based models of the dispersal process will instil or develop valuable and transferrable computational and programming skills.
This project aims to use cancer cells, and the techniques often applied to them, to understand the evolutionary and ecological process of dispersal. This is a novel approach and the opposite of that generally taken in the burgeoning field of cancer evolution, which typically imports ideas from evolution and ecology to understand cancer.
The key questions to be addressed regard the evolution of dispersal behaviours under complex genetic control; more generally, we will be investigating how the particular genetic architecture of selected traits affects their evolution, and how phenotypic plasticity affects the process of dispersal evolution.
Understanding dispersal evolution is crucial in many real-world situations such as predicting the spread of invasive species or responses to environmental change. While not the primary focus, this project could also contribute to an understanding of the extent to which evolutionary and ecological theory can usefully explain or predict cancer progression.
Establishing cell lines as a tractable system for the evolution of dispersal would make available to ecologists a new study system with short generation times and high population sizes, whose motility behaviours are relatively well-understood at the molecular level. A range of powerful molecular techniques originally developed for biomedicine could be employed to understand dispersal.
With the aim of better understanding dispersal ecology, this collaboration includes analytical and experimental methods adapted from experimental evolution and quantitiative genetics, but also employs techniques from the biomedical sciences, including cell and molecular biology and bioimage analysis. The supervisory team is extremely broad in expertise.
Population genetics and evolution
In addition to the quantitative methods training provided via the QMEE CDT itself, we will provide training in agent-based modelling and if necessary methods including image analysis and cell tracking. We will also encourage the student to attend advanced-level taught modules in cell biology and, if necessary, evolutionary genetics, to build a strong understanding of the project background.
Imaging and image analysis traiing in the laboratories of Phil Dash (Reading) and/or Cristina Lo Celso (Imperial); agent-based modelling and teaching of background material at Reading.
No
2017-10-02 18:57:03