Bioinformatics Club
Venue: Level 12 Conference Room

Time: 16 Feb 2017, 4pm

Speaker: Samuel Collombet

Deciphering the regulatory network controlling blood cell specification through ChiP-seq meta-analysis and dynamical modelling

Abstract:
Blood cells arise from a common set of hematopoietic stem cells that differentiate into more specific progenitors, ultimately leading to different functional lineages. This process relies on the activation and repression of different genes modules, controlled by transcription factors (TFs). Novel high-throughput technologies allow the identification of cell-specific regulatory elements by characterising chromatin states and TFs binding sites, in conjunction with gene expression profiling. Proper integration and analysis of these data enable the delineation of novel regulatory interactions, which can be modelled and analysed using formal methods, thereby fostering our understanding of the mechanisms controlling cell fate at a system level, and enabling the prediction of the effects of molecular perturbations in silico. Combining public and novel data from molecular genetic experiments (qPCR, western blot, EMSA) or genome-wide assays (RNA-seq, ChIP-seq), we have assembled a comprehensive regulatory network encompassing the main transcription factors and signalling components involved in myeloid and lymphoid lineage development. Using a multilevel logical framework, we built a dynamical model allowing us to simulate cells differentiation, commitment and reprogramming in silico. To improve the accuracy of our model, we performed a meta-analysis of available TF ChIP-seq datasets for myeloid and lymphoid cells, confirming previously known regulations or confirming new ones (26 confirmed and 66 predicted regulations). We then iteratively included some predicted regulations in our model, performed static or dynamical analysis (stables states analysis or differentiation/reprogramming simulations), and compared the results with gene expression data and phenotypes. This approach enabled us to predict several important, previously unknown regulations, which were further confirmed experimentally. Finally we used our model to delineate novel trans-differentiation protocols that can be tested experimentally.