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NERC E4 Evolution to the rescue: the role of ecology in antibiotic resistance evolution

  • Full or part time
    Dr H Alexander
    Dr L McNally
  • Application Deadline
    Thursday, January 09, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Interested individuals must follow the "how to apply" link on the Geosciences E4 Doctoral Training Partnership web page: http://www.ed.ac.uk/e4-dtp/how-to-apply

Evolution may “rescue” a population from environmental change. This project asks how ecological interactions affect the chance of rescue, using bacteria treated with antibiotics as a study system.
Project background

Severe environmental changes can threaten populations with extinction unless they adapt rapidly, known as “evolutionary rescue”. Applications arise both in conservation, where we wish to promote rescue from climate change or pollution; and in medicine, where we apply treatments intended to eradicate pathogens, but they may be rescued by evolving resistance. Despite these opposing goals, the underlying dynamic processes are similar – in particular, rescue relies on probabilistic events occurring in declining populations. Evolutionary rescue has become a hot topic over the past decade, yet little is known about the impact of ecological interactions within the population. Bacteria treated with antibiotics serve as a model system to address this question: they compete for limited resources and can cooperate in absorbing or degrading antibiotics. Moreover, a better understanding of this system could suggest new strategies to tackle the pressing public health issue of antibiotic resistance.
Research questions

1) Model development: How can we capture ecological interactions in a mathematical model describing the coupled dynamics of bacteria, resources, and antibiotics?

2) Computational methods: Nonlinear interactions raise challenges for model tractability, particularly when accounting for stochasticity inherent to rescue. How can we merge solution techniques for deterministic and stochastic systems to efficiently generate model predictions?

3) Biological insights: As sensitive bacteria consume resources and degrade antibiotics, the environment in which resistance arises continually changes. How does the probability that resistant bacteria emerge in the population change over time and with antibiotic dose?

4) Biological insights: Ecological interactions with sensitive bacteria could have two opposing effects: competition for resources could hinder emergence of resistance, while absorption of antibiotics could be protective. When does the effect of competition outweigh the effect of protection, or vice versa?

This project will primarily use mathematical modelling to study the process of evolutionary rescue, i.e. emergence of resistance, in a bacteria/antibiotic system. Models may include both deterministic (ordinary differential equations) and stochastic (birth-death process) components. Analytical, numerical, and/or stochastic simulation techniques will be applied to the model to generate biological insights. Depending on student interest and direction of the project, key model predictions may be tested experimentally towards the end of the project.


Year 1: Specialised training in stochastic processes and/or biology depending on student background; literature review; and initial development of models.
Year 2: Model refinement and implementation of computational methods to analyse the model and address biological questions.
Year 3: Depending on student interest and progression, either (a) use the model framework to explore intervention strategies, or (b) test model predictions experimentally in established wet lab systems.


A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. Depending on student background, there will be the opportunity to gain training as required in biology, probability and statistics, and/or coding. Opportunities may include attending the Academy for PhD Training in Statistics (https://warwick.ac.uk/fac/sci/statistics/apts/students/), the peer-run Coding Club for R skills, and/or taught courses at the UoE. The supervisors will provide specialist training in mathematical modelling and evolutionary theory, and basic microbiology lab skills if applicable. There will be plenty of opportunities to interact with and learn from mathematicians and biologists in the host institute and the broader community studying antimicrobial resistance evolution in Edinburgh (https://www.ed.ac.uk/edinburgh-infectious-diseases/amr).

Applicants should have strong mathematical/quantitative skills, such as (but not limited to) a degree in maths or physics. Experience in programming (e.g. R, C, or Matlab) is advantageous. In-depth prior knowledge of biology is not a prerequisite, but interest and motivation to learn are essential.

Funding Notes

This project is eligible for the E4 Doctoral Training Partnership. The E4 projects are currently available for full NERC studentship funding which is competitive by interview AND AVAILABLE ONLY TO UK citizens and to EU citizens who have worked or studied in the UK for the previous 3 years.
For application details see View Website
Further details here - View Website


1. Alexander, HK et al. “Evolutionary rescue: linking theory for conservation and medicine”, Evol Appl 7:1161-1179 (2014).

2. Brockhurst, MA et al. “Assessing evolutionary risks of resistance for new antimicrobial therapies”, Nat Ecol Evol 3:515-517 (2019).

3. Lipsitch, M and Levin, BR. “The population dynamics of antimicrobial chemotherapy”, Antimicrob Agents Chemother 41:363-373 (1997).

4. Kouyos, RD et al. “The path of least resistance: aggressive or moderate treatment?”, Proc R Soc B 281:20140566 (2014).

5. Vega, NM and Gore, J. “Collective antibiotic resistance: mechanisms and implications”, Curr Opin Microbiol 21:28-34 (2014).

6. Wale, N et al. “Resource limitation prevents the emergence of drug resistance by intensifying within-host competition”, PNAS 114:13774-13779 (2017).

Related Subjects

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