Research in Pirism Lab is mostly based on use of NGS data in systems modelling. We are particularly interested in analysis and modelling of dynamic processes such as stem cell differentiation and tumour growth. We use single cell NGS data to be able to represent the heterogeneity in the cell populations accurately in our models, which involves use of machine learning methods as well as rigorous statistical analysis of the data. We aim to extend our models to 3-dimensions to accurately describe the cells in the context of their extracellular environment, which in turn regulates the intracellular dynamics via signalling pathways. Better understanding the dynamics of cellular processes and interactions may lead to discovery of targets to regulate these processes by human intervention.