Development of novel approximation methods to study the stochastic dynamics of biological systems

Interested individuals must follow Steps 1, 2 and 3 at this link on how to apply
http://www.ed.ac.uk/biology/prospective-students/postgraduate/pgr/how-to-apply

Biological dynamics is widespread in nature and occur at a multitude of scales; for example intrinsic noise affects the time evolution of the concentrations in biochemical systems, whilst demographic noise plays an important role in the dynamics of a population of interacting organisms. Master equations [1] can be written down which describe the stochastic dynamics of such systems, however they can rarely be exactly solved. Thus the development of approximation methods is of paramount importance to uncover some of the information contained in these master equations.

In this project the aim is to develop novel accurate approximations to the master equation. Topics of interest include (but are not limited to) the derivation of rigorous hybrid stochastic-deterministic descriptions, stochastic model reduction in the absence of time scale separation and approximations based on series expansions in relevant parameters. Some of these methods may be based on approximation methods that we have derived for biochemical systems in the past few years [2,3]. The new methods will be applied to model and yield insight into the dynamics of systems at various spatial scales e.g. complex biochemical systems in single cells, cell-cell interactions in a tissue and disease transmission in a population.

The ideal candidate will have at least an upper second-class honours degree in Physics, Applied Mathematics or another quantitative discipline and a desire to apply his/her skills to understanding biological systems. The successful candidate will join the stochastic modelling group of Dr. Ramon Grima in the Center for Systems and Synthetic Biology (SynthSys) at the University of Edinburgh http://grimagroup.bio.ed.ac.uk/index.html.