MCS SIG | Spatio-temporal and spatial-stochastic modelling of gene regulatory networks

Transcription factors are important molecules which control the levels of mRNA and proteins within cells by modulating the process of transcription and hence translation. Transcription factors are part of a wider family of molecular interaction networks known as gene regulatory networks (GRNs) which play an important role in key cellular processes such as cell division and apoptosis (e.g. the p53-Mdm2, NFκB pathways). Transcription factors exert control over molecular levels through feedback mechanisms, with proteins binding to gene sites in the nucleus and either up-regulating or down-regulating production of mRNA. In many GRNs, there is a negative feedback in the network and the transcription rate is reduced. Typically, this leads to the mRNA and protein levels oscillating over time and also spatially between the nucleus and cytoplasm. When experimental data for such systems is analyzed, it is observed to be noisy and in many cases the actual numbers of molecules involved are quite low. In order to model such systems accurately and connect with the data in a quantitative way, it is therefore necessary to adopt a stochastic approach as well as take into account the spatial aspect of the problem. In this talk, we present both deterministic and stochastic spatio-temporal models of both observed GRNs and synthetic GRNs e.g. repressilators and activator-repressor systems.