Stochastic modelling of intra-cellular bacterial infections

The aim of this PhD studentship is to make use of mathematical models to characterise and better understand Francisella infection after bacterial escape from the lung.

The underlying challenge is to develop a quantitative understanding of in-host infectious disease progression that
(i) integrates biological and immunological data, and
(ii) increases understanding of the mechanisms at molecular, cellular and population levels of the innate and the adaptive immune systems.

This work supports the `three Rs’ objectives shared by government, industry and
academia (i.e. the reduction, refinement and replacement of animals in experimentation), by seeking mathematical and computational approaches as an adjunct or alternative to animal experiments.

Mathematical Biology and Medicine Group

In the Leeds Mathematical Biology and Medicine group, research is being carried out in theoretical immunology, and evolutionary dynamics. The immune system is one of the most complicated multiscale systems imaginable. The adaptive immune system of a vertebrate is a vast army of cells and molecules that cooperate to seek out, mark, bind to and destroy pathogens. Stochastic modelling is ideally suited to immunology at many scales. For example, cells live in a Brownian world, where motion is partly directed and partly random, so the battle between invading pathogens and the immune system is best described statistically. Similarly, gene expression is influenced by noise and fluctuations: small numbers of molecules as well as the intrinsically stochastic nature of biochemical reactions mean that fluctuations must be taken into account in order to understand cellular function.

http://www1.maths.leeds.ac.uk/research/mathbiomed/