Decoding transcriptional programs of blood cell development

Established: December 3, 2012

Understanding the mechanisms that govern stem cell self-renewal and cell fate decisions are fundamental to regenerative medicine and to understanding how these mechanisms are perturbed in disease states. Blood cell development (haematopoiesis) has long stood as a paradigm for studying stem cell biology. Genes encoding transcriptional regulators and components of cell signalling pathways are recognised as powerful regulators of developmental processes including the development of blood cells. The interplay of sensing the external environment (through cell signalling genes) and controlling internal cellular states (through transcriptional control of gene expression) is therefore critical for the appropriate execution of complex biological phenomena such as the development of blood cells from immature precursors. Importantly, mutations in both transcriptional and signalling regulators are the basis for most cases of leukaemia, thus suggesting that a better understanding of normal blood cell development will be critical to discover how dysregulation of this process can cause cancer.

The complexity of multi-gene interactions poses significant intellectual and experimental challenges. Network executable models are therefore increasingly recognized as a powerful approach to deal with the complexity of biological processes including the intricate interactions between transcriptional regulators and signal transduction pathways. An important challenge for regulatory network reconstruction is to devise executable models that can represent the dynamic interactions between important sub-circuits and represent the changes in gene expression when cells are undergoing defined differentiation steps. In collaboration with the Gottgens lab (Cambridge Institute for Medical Research) we have synthesized an executable model for early blood development from single-cell gene expression data derived from embryonic stem cells. Through the modelling of steady states and dynamic network behaviour, we are working to identify specific genes and feedback loops within the network that are likely key players in cellular decision making such as the dynamic processes of stem cell maintenance and/or differentiation.