Using genome-wide data to model signalling-responsive gene regulatory networks

The PhD project will use experimental but also bioinformatics methods to investigate the role of signals in the transcriptional control of haematopoietic stem cell specification. Haematopoietic stem cells (HSCs) ensure lifelong mature blood cell production in the adult organism and the entire pool of adult HSCs is established during embryonic development. Blood cell specification can be faithfully recapitulated by the differentiation of embryonic stem cells (ESCs) in vitro. We have been using this model system to investigate the molecular requirements for blood cell specification. Each of developmental transition is regulated by an orchestrated interplay of stage-specific transcription factors which are connected to common and distinct target genes within a dynamic transcriptional network that regulates the transition from on cellular differentiation state to another (Obier and Bonifer, 2016; Obier et al., 2016; Goode et al., 2016).

However, while the roles of transcriptional regulators of haematopoietic differentiation are beginning to be understood, it is still less clear how outside signals direct their activity and how this leads to developmental-stage specific gene expression. The reason for this is the fact that signalling pathways consist of a myriad of different components operating in a cell type-specific fashion making this process difficult to study. The transmission of signals into the nucleus involves surface molecules such as receptor kinases and at the receiving end in the nucleus inducible transcription factors. Such factors bind to their cognate cis-regulatory elements and alter gene expression in a signalling dependent way. By identifying and studying the function of such sequences and the factors binding to them we can obtain a first insight into which signalling pathways are involved and how signal transduction processes are coordinated at the genomic level. We will also be able to investigate, how such factors integrate into the wider gene regulatory network that is driven by cell fate deciding factors, and how they interact with the chromatin landscape and modify gene expression. The final aim of this work is to generate data and develop methodologies that allow to build models that predict the behaviour of gene regulatory networks and of the components within such networks. The development of such methodology is of the essence if we want to direct cell fates for biotechnology and regenerative medicine approaches.