Unravelling gene regulatory networks for differentiating stem cells

Florian Buettner

Time: 3:40 - 4:20 PM

Type: Invited presentation

Affiliation: The EMBL-European Bioinformatics Institute (EMBL-EBI), Cambridge

Abstract

Reconstruction of gene regulatory networks form of experimental data is an important challenge in computational biology as it allows to gain deeper insights in how genes interact on different molecular levels in order to execute specific biological functions. This in turn, is crucial for understanding complex processes such as cell differentiation and disease. In recent years, technological advances allow us to measure gene expression at the level of single cells, which are often being regarded as the fundamental unit in biology. The availability of experimental data at this unprecedented resolution gives rise to new opportunities and challenges for reconstructing regulatory network dynamics. Here, I will illustrate current approaches for network reconstruction based on single-cell gene expression data at the example of early blood development. In addition to these standard methods, which are usually based on the application of network reconstruction methods for static bulk data, I will present our recent efforts on unravelling the dynamics of such networks. In contrast to previously developed methods, our approach allows for the accurate reconstruction of Boolean logic gates and presence/absence of regulatory edges by leveraging the rich information inherent in the single-cell data. This resulted in a computationally executable transcriptional regulatory network model of blood development that revealed new insights in the transcriptional programs that underlie organogenesis.