Analysis of whole-genome sequencing data has been greatly outpaced by the experimental techniques that generate those datasets. The computational challenges associated with these analyses typically make machine learning methods more suitable over more conventional methods like dimensionality reduction, which limits the information obtainable from a dataset. In this paper, we focus on the biophysical model of RNA velocity, which yields meaningful insights into the functional trajectories of individual cells. There are many downstream applications, such as the identification of key genes driving a disease pathway. We improve the dynamical model by relaxing unrealistic assumptions and using the resulting generative process to train a diffusion model to compute pseudotime. Our probabilistic model is able to quantify the uncertainty in its pseudotime predictions. Finally, we demonstrate the efficacy of our model on a series of benchmark tasks.