Stochastic reaction networks are particular continuous-time Markov chains mainly used to interpret biochemical phenomena and design experiments and useful modifications in the setting of synthetic biology. The application of stochastic reaction networks can however be extended to other areas (such as epidemiology, ecology, social interactions) where the time evolution of interacting objects is under investigation. Stochastic reaction networks are qualitatively described by a finite set of transformation rules arranged in a finite directed graph, while the state space of the process is allowed to be infinite. The research directions of our group include:
- understand how dynamical properties of the stochastic reaction network (such as its long-term behavior and certain robustness properties) relate to features of the associated finite directed graph;
- lower the complexity of large models by approximating their dynamics with that of simpler models;
- construct efficient simulation schemes;
- develop efficient techniques to perform statistical inference.