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Details of Grant 

EPSRC Reference: EP/T031727/1
Title: Impact of network-structured populations on evolution
Principal Investigator: Sharkey, Professor KJ
Other Investigators:
Baker, Professor KS
Researcher Co-Investigators:
Dr K H PATTNI
Project Partners:
Public Health England
Department: Mathematical Sciences
Organisation: University of Liverpool
Scheme: Standard Research
Starts: 08 February 2021 Ends: 07 February 2024 Value (£): 462,219
EPSRC Research Topic Classifications:
Non-linear Systems Mathematics
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Jun 2020 EPSRC Mathematical Sciences Prioritisation Panel June 2020 Deferred
31 Aug 2020 EPSRC Mathematical Sciences Prioritisation Panel September 2020 Announced
Summary on Grant Application Form
Populations are often structured in the sense that individuals are only interacting with their immediate neighbours rather than all individuals. Within these structures, evolutionary dynamics occur. These dynamics underpin many areas including the evolution of our species, the development of antimicrobial resistance and seasonal influenza as well as the emergence of ideas, language and society.

A key way of representing this type of structure is by networks of contacts, and Evolutionary Graph Theory (EGT) has been developed to describe and understand these processes. This structure is idealised in several ways and here, our primary objective is to make this more directly applicable to understanding and describing evolutionary dynamics and especially pathogen evolutionary dynamics.

Evolutionary graph theory assumes a clear distinction between evolutionary dynamics and the underpinning ecological processes of birth and death that drive it. This leads to a lack of realism in the biological processes and limits its utility for addressing real problems. By resolving this, we will obtain a new, more applicable framework. We shall determine the robustness of the theorems of evolutionary graph theory and the extent to which they translate to more realistic scenarios.

Crucially, this model will be the first to be coupled with empirical data from real antimicrobial resistance evolution experiments performed on structured populations in laboratory conditions. This will demonstrate the applicability of the new mathematical framework, providing support for its application in describing real-world systems of pathogen evolution which occurs in structured populations critical for health such as antimicrobial resistance in hospital environments, influenza over airline routes as well as geographic constraints.

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Organisation Website: http://www.liv.ac.uk