Single cell biology approach for immune cell signalling
Biological signalling systems are inherently complex and their regulation needs to be understood at all levels. One example is the failure to resolve inflammation in single cells, which is associated with out-of-control tissue-level responses characteristic in many autoimmune diseases.
My previous work has suggested that inflammation may be controlled through subtle changes in single cell dynamics and varying cellular heterogeneity through underlying molecular networks. We use an interdisciplinary systems biology approach to build a quantitative understanding of a set of cellular and molecular cytokine networks that together regulate inflammatory processes. We employ state-of-the-art multi-scale mathematical modelling, live-cell microscopy (including microfluidic tissue models) and quantitative single cell gene expression. Thses allow studies of emergence and function of spatial and temporal dynamics during inflammation. A more quantitative understanding of these non-linear and non-intuitive processes might lead to improved therapeutic strategies for inflammatory disease.
Pawel is a theoretician by training (M.S in Control Theory, 2001, PhD in Statistics, 2006, PDRA in Centre for Cell Imaging, Liverpool, 2006-11) investigating regulation of inflammatory signalling networks. His theoretical work resulted in stochastic models of transcription and the NF-kappaB system, and suggested that heterogeneity in single-cell NF-kappaB responses might underlie robust tissue-level inflammation. He is a lecturer in the Faculty of Biology, Medicine and Health where he established a theoretical and experimental systems immunology lab (during his BBSRC David Phillips Fellowship, 2011-2016). He has developed quantitative live cell imaging and gene expression platforms to study immune cell signalling, incorporating measurements of TF dynamics, transcription and inflammasome activation. Recently, he has developed protocols for quantitative single cell approaches to study responses to pathogen infections.