Quantitative modelling of embryonic development
The study of morphogenesis promises to shed light on developmental diseases and to pave the way for the growth of artificial organs. Recent years have seen a rise in quantitative data for many embryonic processes. However, these new data lead to challenges at each stage of the scientific method, including the design of quantitative hypotheses as well as data analysis and data interpretation. Here, I will present projects that illustrate how mathematical methods can help overcome challenges in the quantitative study of embryonic development. I will show how multi-scale, cell-based models can be designed to make experimentally testable predictions on tissue growth. I will present a novel algorithm that uses graph theoretic concepts to enable cell-tracking in live-imaging microscopy videos. Finally, I will discuss how Bayesian inference can provide insights into the mechanics of tissue growth and into the roles of gene expression dynamics during cell differentiation.