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MRC Precision Medicine DTP: Understanding the mechanisms of HIV-1 infection using mathematical modelling and phylogenetics

Project Description


If HIV-1 infection is left untreated the viral load of chronically infected individuals can rise to million copies per millilitre of blood with hundreds of genetic variants. This virus proliferation within an individual is in stark contrast to the exceptionally low chance of infection: on average 1 in every 10,000 sexual exposures leads to infection, and of these infections, it is likely that most are initiated by a single genetic variant (Talbert-Slagle et al. 2014).

In what circumstances does sexual transmission result in more than one variant? Answering this question will get to the heart of understanding HIV sexual transmission and the effectiveness of a potential vaccine.

There are known risk factors associated with transmitting or acquiring HIV, but the exact mechanisms underlying these risk factors and the impact on the number of ‘founder’ strains are unknown. Understanding the mechanisms underlying the role of the transmitting partner and the recipient partner in determining the number of founder stains will likely help quantify the dynamics of HIV acquisition.

With an increasingly rich data source of next-generation sequence data available, we are now able to build a picture of infection dynamics through infection. These data provide a window into the founder strain dynamics that take place at the onset of infection (Keele et al. 2008). This PhD project will use existing and newly generated next-generation sequence data to understand estimates of the number and type of these founder strains (Romero-Severson et al. 2016). Then, using phylogenetic, mathematical or statistical approaches, the student will evaluate how the number and type of these founder strains impact disease progression.

While the exact nature of the PhD can be tailored to the candidate’s interest and skills, this PhD project could approach this quantitative scientific problem from three angles:

Evaluate whether different methodologies to calculate the number of founder strains provide consistent results. Founder strains are genetically homogeneous lineages of viruses that go on to successfully replicate within the host during its infection. Both the nature and number of founder strains holds important information on an individual’s risk of infection, their ultimate prognosis and whether vaccine candidates will be successful (Janes et al. 2015). However estimating the number of founder strains is usually time-consuming and computationally intensive. Accurately using data on the number of founder strains will depend on confidently relying on these previous estimates.
Develop a mathematical model to harmonize collated data on the number of founder strains with the risk of transmission by route and by stage of infection. It is anticipated that the mathematical modelling undertaken by the student will be data-driven from diverse sources such as phylogenetic analysis, epidemiology and vaccine trials and could involve statistical model fitting methodology.
Extend the mathematical model to incorporate the role of founder strains on disease progression and viral load. A suitable candidate would be one interested in using or developing their quantitative skills in the area of infectious disease and genetics/sequencing.


1) Evaluate whether different methodologies to calculate the number of founder strains provide consistent results.

2) Develop a mathematical model to harmonize data on the number of founder strains with the risk of transmission by route and by stage of infection

3) Extend the mathematical model to incorporate the role of founder strains on disease progression and viral load

Training outcomes

The project will use an interdisciplinary combination of genetic sequence data analysis, statistical modelling and mathematical analysis. The candidate will develop their quantitative skills through the use of genetic, statistical and mathematical analysis. The student will develop or extend their programming expertise in languages, such as R or Python. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub.

The student will learn to communicate their research through publication in peer-reviewed journals and presentation in scientific conferences. By working closely with experts in sequence data, phylogenetic analysis and mathematical modelling, the student will become comfortable working within an interdisciplinary environment and interacting with a diverse scientific team.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

Funding Notes

Start: September 2019

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:


Janes, H., Herbeck, J.T., Tovanabutra, S., Thomas, R., Frahm, N., Duerr, A., Hural, J., Corey, L., Self, S.G., Buchbinder, S.P. and McElrath, M.J., 2015. HIV-1 infections with multiple founders are associated with higher viral loads than infections with single founders. Nature medicine, 21(10), p.1139.

Keele, Brandon F., Elena E. Giorgi, Jesus F. Salazar-Gonzalez, Julie M. Decker, Kimmy T. Pham, Maria G. Salazar, Chuanxi Sun et al. "Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection." Proceedings of the National Academy of Sciences105, no. 21 (2008): 7552-7557.

Romero-Severson, Ethan O., Ingo Bulla, and Thomas Leitner. "Phylogenetically resolving epidemiologic linkage." Proceedings of the National Academy of Sciences (2016): 201522930.

Talbert-Slagle, K., Atkins, K.E., Yan, K.K., Khurana, E., Gerstein, M., Bradley, E.H., Berg, D., Galvani, A.P. and Townsend, J.P., 2014. Cellular superspreaders: an epidemiological perspective on HIV infection inside the body. PLoS pathogens, 10(5), p.e1004092.

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