Resolving mesodermal diversification with single cell transcriptomics (360G-Wellcome-203942_Z_16_A)

During the course of development, cells divide, migrate, and specialize to form major organ systems. Furthemore, among most mammals and birds, mouse cells differentiation follows a unique morphology. Understanding the molecular mechanisms underlying such process is a core issue in Biology and a curiosity in mouse, which despite differences still share fundamental properties during the process. The challenge has been addressed by leveraging current high-throughput technologies such as single cell transcriptomics. The amount and complexity of this data requires innovative mathematical frameworks that take advantage of current computational capacities. I am intersted on resolving mesodermal diversification during mouse gastrulation. Based on the premise that single cell profiles represent snapshot measurements of expression as cells traverse a differentiation process, I will use probabilistic modeling among other statistical and mathematical methodologies to reconstruct a measure of a cell’s progression through some biological process, and to model how cells undergo some fate decision and branch into two or more distinct cell types. In particular, Bayesian Inference has shown to be a useful approach to take advantage of computational resources, and to include prior knowledge into models, by providing a formal probabilistic framework that allows learning from the data in order to make predictions.