Below is a brief list of the current research interests of the group. If you are interested in any of these areas and would like to collaborate or are interested in pursuing a Ph.D. with me in Bath then please email me on c.yates at bath dot ac dot uk
I work in collaboration with experimentalists in Edinburgh on a gene called Kit! Mutations in Kit can cause slower migration of melanocyte neural crest cells leading to non-pigmented areas of skin. I work on linking two different modelling paradigms (discrete stochastic and deterministic continuum) for cell migration and exploiting their complementary advantages when modelling a biological system.
Stochastic simulation methodologies
With a variety of collaborators and PhD students I work on the development of efficient stochastic simulation algorithms. In part I work on developing general methodologies for stochastic simulation (Multi-level for continuous time Markov processes, recycling random numbers in the SSA, avoiding negative populations in tau-leaping). I also work on the development of simulation algorithms specifically designed to speed up the simulation of spatially extended systems (hybrid methods, non-local jumping, adaptive mesh refinement for position-jump processes).
We model the collective migration of locust (and other animal) swarms using self-propelled particle models and more basic stochastic interaction models. In collaboration with scientists in Slovakia I have also started modelling decision making in ants.
The evolution of pleiotropy and redundancy
We model the potential for the evolution of pleiotropy and redundancy as a mechanism by which cheating is regulated in bacterial communities. We use experimental data to inform our stochastic evolution models.
We model the interaction dynamics between migrating nematode worms and more sedentary bacteria which act as food for the worms. We use cellular automaton/PDE hybrid models informed by experimental data.
In collaboration with experimental biologists in Harvard and Yale I contrive computer models which are able to investigate the possible mechanisms by which egg patterns form.
Tracking free-swimming bacteria under the microscope is a highly non-trivial problem. In collaboration with biochemists and mathematicians I have helped to develop a tracking algorithm which is capable of achieving this task very efficiently. We currently have a patent pending on the method.