Structured stochastic models for biochemical and biological systems

Experiments in a wet lab can be time consuming, expensive, and not very scalable. In silico experiments, although not able to replace them, still can provide excellent suggestions on the direction of future research. Most of the modelling techniques in systems biology are based on differential equations (ODEs) or their stochastic discretised variants. However, these formalisms are not intrinsically structured and, in most cases, our models are statically generated before simulations are performed. As a result, many problems that are combinatorial in nature, cannot be efficiently simulated, and often, not even explicitly represented. Hopefully, this is just a shortcoming of our lazy representations, and we could address it with a more structured approach to model design.

In this talk, I will present a new framework for stochastic models and simulations which I have been developing as part of my PhD project. The aim of the framework is to provide cleaner and more concise models for a wide range of problems, where interactions are driven by local patterns and their structure is characterised by combinatorial complexity. And to simulate them, hopefully...