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Engineering de novo microscopic ecologies to monitor and dissect disease spread


Project Description

Understanding what regulates the spread of diseases in space and time is critical if we are to combat them effectively. However, identifying the determinants of disease spread in natural populations -- be they human or animal -- is challenging due to the difficulties of establishing whether an individual is infected or not, lack of replication, and non-control conditions. Furthermore, the complex spatial ecology of connected populations is known to be key for predicting disease spread, but often difficult to quantify and monitor under natural conditions. A solution to these issues is to use model systems -- small-scale experiments where the movement of individuals can be tracked over time, their infection status determined, and treatments such as environmental warming implemented. One such model system is the ciliate protist Paramecium caudatum which is infected by the bacteria Holospora undulata. However, at present identifying whether an individual is infected requires it to be killed, fixed, and stained. Were it possible to non-invasively identify whether individual P. caudatum are infected or not, this model system could be used to generate valuable high-dimensional high-resolution data on the dynamics of disease spread at a landscape-scale.

The aim of this project is to develop a new experimental system and supporting computational tools to enable the creation of engineered ecologies in which the spread of H. undulata infections of P. caudatum can be easily tracked over time. To control the structure of the ecologies produced, spatial organisation will be constrained using 3D printed arenas (see image above), and monitoring will be carried out using automated microscopy. The focus of this project will be on developing two complementary ways to infer the infection state of individual P. caudatum cells from these microscopy images. The first approach will use bio-engineering tools to directly modify P. caudatum cells such that infection by H. undulata leads to the expression of a pigment which changes the colour of a cell. This will be achieved by using transcriptomics data of healthy and infected cells, and then developing synthetic genetic circuitry to redirect the native infection response to the production of a desired pigment. The second approach will attempt to use machine learning techniques to see whether subtle changes in cell behaviour, morphology and appearance might alone provide sufficient information to determine infection state. This will involve the training of deep artificial neural networks (e.g. U-Net) with large numbers of healthy and infected cell images, validating the accuracy of the predictions, and then integrating this system into the monitoring software of the arenas.

The student will be given training in cutting-edge molecular and synthetic biology methods, 3D printing, robotics and microscopy at the University of Bristol, machine learning techniques for image analysis during regular visits to co-supervisor Dr Metz at the University of Exeter, and essential hands-on microbiology knowledge for working with P. caudatum and H. undulata through a placement with co-supervisor Dr Kaltz at Montpellier. More broadly, there will be opportunities to gain public engagement experience as part of the "Become a Biological Engineer" project run within the Biocompute Lab (www.biocomputelab.org), and the Bristol Doctoral College (BDC) will provide extensive opportunities for training in transferable skills and personal development, including productivity, teaching and communication.

Funding Notes

This is a competition funded project through the NERC GW4+ DTP. There is a competitive selection process. This studentship will cover fees, stipend and research costs for UK students and UK residents for 3.5 years.

Applicants must have an excellent undergraduate or Masters degree (2:1 or first) in an area related to the project (e.g. Biology, Biochemistry, Biological Engineering). They must also be willing to work as part of a highly inter-disciplinaryteam and have a passion for learning the diverse skills needed to make this project a success (e.g. microbiology, synthetic biology, microscopy and machine learning).

References

1. Benton et al. (2007) Microcosm experiments can inform global ecological problems. Trends in Ecology and Evolution 22, 516-521.

2. Lunn et al. (2013) Modelling the Dynamics of an Experimental Host-Pathogen Microcosm within a Hierarchical Bayesian Framework. PLoS One 8, e69775.

3. Greco et al. (2019) Living computers powered by biochemistry. The Biochemist 41, 14-18.

4. Falk et al. (2019) U-Net: deep learning for cell counting, detection, and morphometry. Nature Methods 16, 67-70.

How good is research at University of Bristol in Biological Sciences?

FTE Category A staff submitted: 64.60

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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