Single cell RNA sequencing (scRNA-seq) has revolutionized our understanding of hematopoiesis, allowing for high-resolution analysis of gene expression dynamics along the differentiation process. However, since the cells are destroyed with the measurement, individual cells cannot be traced over time, demanding computational tools to accurately infer differentiation trajectories. Here, we will use optimal transport and dynamical systems theory to develop inference methods to estimate differentiation trajectories of single cells. This will enable us to study the gene expression dynamics in various datasets with a special focus on mast cell development.