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Details of Grant 

EPSRC Reference: EP/S020241/1
Title: HyperTraPS-CT: Bayesian inference of coupled stochastic trait evolution in continuous time, applied to multi-drug resistance in tuberculosis
Principal Investigator: Johnston, Dr I
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Sch of Biosciences
Organisation: University of Birmingham
Scheme: New Investigator Award
Starts: 01 January 2019 Ends: 31 December 2020 Value (£): 228,043
EPSRC Research Topic Classifications:
Numerical Analysis Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Nov 2018 EPSRC Mathematical Sciences Prioritisation Panel November 2018 Announced
Summary on Grant Application Form
The rapid development of technology is providing more and more data across sectors of science and society. In many scientifically and medically important contexts, large datasets emerge from processes that involve the acquisition or loss of features over time -- for example, the progression of a disease, or the evolution of antimicrobial resistance. If we can learn from these data about the underlying dynamics of the system (which will often have random components) we can understand the system and make predictions about its future behaviour and response to perturbations. For example, if we understand the pathways by which antimicrobial resistance evolves in a given pathogen, we can form predictions about optimal future interventions for a given strain. Only new mathematical and statistical approaches can help us understand and predict the behaviour of these complex biological systems, in which randomness plays a central role.

In response to these growing questions, this project will develop HyperTraPS-CT, a powerful and novel approach for inferring the dynamics of evolutionary and progressive pathways from data. Previous instances of this approach, despite limitations, have been used to transform our knowledge of the evolution of efficient photosynthesis, and to explore the evolutionary history of complex life. This project will dramatically expand its power by allowing the inference of absolute timescales of evolutionary progression, and the identification of the optimal model structure to describe a given system.

This work has an exceptionally broad set of potential applications across evolutionary biology and medicine, but we will here focus on a specific evolutionary question of global health importance. We will use HyperTraPS-CT to identify the probabilistic structure of pathways underlying the evolution of multi-drug resistance in Mycobacterium tuberculosis, a leading worldwide cause of mortality from infectious disease. Tuberculosis has a substantial UK and global health and economic burden: London is known as the `tuberculosis capital of Western Europe', with incidence rates reported to exceed those in Iraq and Rwanda in 2015, and drug resistance increasing. We will harness recently-produced large-scale data on tuberculosis evolution and address, for the first time, a plethora of questions of applied importance, including the likely next evolutionary steps in tuberculosis multi-drug resistance (MDR), the timescales of MDR emergence, and the interdependence of MDR features in the bacterium. Through our connection with tuberculosis experts at the University of Birmingham Institute for Microbiology and Infection, and the Birmingham Drug Discovery Facility, we will translate these findings -- facilitated by these new mathematical developments -- into actionable insight into tuberculosis evolution.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.bham.ac.uk