Stochastic models of T cell receptor and cytokine receptor signalling: a mathematical and computational approach
Lead Research Organisation:
University of Leeds
Department Name: Applied Mathematics
Abstract
This project supports the Healthcare Technologies Theme by providing cost-effective computational tools to aid development and optimisation of treatments for intracellular bacterial infections using Francisella tularensis as a case study.
Mathematical models and statistical models will be developed to enable simulation of the within-host mechanisms of infection and immune response allowing potential interventions to be explored in silico. A wide range of biological and immunological data will be integrated to increase understanding of the mechanisms at molecular, cellular and population levels.
This application intersects the EPSRC research areas od biological informatics, complexity science and non-linear systems, employing mathematical, statistical and numerical techniques. The work will provide novel computational tools for operational research which will be utilised by the Project Partner.
Mathematical models and statistical models will be developed to enable simulation of the within-host mechanisms of infection and immune response allowing potential interventions to be explored in silico. A wide range of biological and immunological data will be integrated to increase understanding of the mechanisms at molecular, cellular and population levels.
This application intersects the EPSRC research areas od biological informatics, complexity science and non-linear systems, employing mathematical, statistical and numerical techniques. The work will provide novel computational tools for operational research which will be utilised by the Project Partner.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509243/1 | 01/10/2015 | 31/12/2021 | |||
| 1665655 | Studentship | EP/N509243/1 | 01/09/2015 | 31/08/2019 | Jonathan Carruthers |
| Description | A multi-scale model has been developed for the infection dynamics of Francisella tularensis in the lung following inhalation of bacteria. The multi-scale model consists of three levels, representing intracellular dynamics, within-host dynamics and population-level dynamics. The model is able to predict the probability that an infected individual will display symptoms of tularemia as well as the timescale of these events. A mathematical model for Ebola virus has also been developed. Often, models of viral dynamics focus on either extracellular or intracellular dynamics, but rarely both. This model uses a novel stochastic approach to account for both, whilst also providing a method to calculate the basic reproduction number. This number is a measure of the number of secondary cellular infections caused by a single cell. |
| Exploitation Route | Current work is looking at developing the multi-scale model into a computational tool that computes the distribution of the number of individuals who will develop symptoms in a specific area given the initial dose they each inhale. Linking this model with predictions from indoor/outdoor dispersion models would aid medical support in the event of an unexpected release of Francisella tularensis bacteria. Furthermore, the same modelling framework applied here can be adapted to encompass other potential bioterrorism agents. The modelling framework for Ebola virus can be applied to other viral infections, offering a new approach to study intracellular dynamics. The insight into intracellular and within-host dynamics that this model provides can also be linked to population level models, to give a greater understanding of how Ebola virus disease spreads. |
| Sectors | Aerospace, Defence and Marine,Healthcare |