Development of a computational mechanistic modelling framework for prediction of the development of antimicrobial resistance in homogenous populations

Lead Research Organisation: University of Aberdeen
Department Name: Institute of Medical Sciences


Resistance to antimicrobial agents is recognized as the major issue facing both industry and medicine. However, there is less consensus on the role, in generation of resistance, of other antimicrobials that are used in consumer products. Triclosan is a widely used antimicrobial in consumer products and a public opinion on it and antimicrobial resistance was published by the Scientific Committee on Consumer Safety1. Despite intensive effort, there is no quantitative framework available to describe and predict the development of resistance to 'actives'. In this project we will develop a modelling framework using microbiological and molecular techniques for the data generation and state-of-the art modelling approaches and computational capabilities for the framework development. This will provide the PhD student with an excellent training in microbial physiology and in systems biology. In parallel, the student will gain experience of industrial research through investigating a problem that is important both scientifically and for society.

The proposed project will use systems biology approaches and laboratory generated data to inform and parameterise the models that will be developed. Models will be created using molecular dynamics, kinetic modelling and other methodologies. Unilever has access to high performance computing capabilities (both company-based and externally-located), that will be used for development and running of such models. Triclosan will be used as a case study. This compound is a well-studied 'active' with a very specific target and mechanism of action.
The key outputs fall under the following categories:

A modelling framework, integrating molecular dynamics, that can predict the effect of mutations on the affinity of Triclosan for FabI

A modelling framework that will be able to predict the kinetics of FabI integrated into the metabolic pathway in the presence of Triclosan that takes into account transient cell to cell variations in FabI and the other members of the pathway that arise via gene duplications and the stochastic effects in gene expression

An integrated multi-scale model that will predict the reaction of a homogenous population of microorganisms to the exposure to an antimicrobial agent, incorporating the new models.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/N503575/1 01/10/2015 30/09/2019
1654720 Studentship BB/N503575/1 01/10/2015 30/09/2019 Joshua Corkhill