Mechanistic gene-regulation-based trajectory inference with bowside

While single-cell RNA sequencing enables the in-depth study of differentiation processes, simultaneous trajectory inference and gene regulatory network reconstruction remain challenging. We propose bowside, a shallow neural stochastic differentiation model that mechanistically models gene regulation. Our method successfully recovers gene expression trends and the underlying regulatory interactions. We demonstrate bowside on hematopoiesis and induced pluripotent stem cell reprogramming data, retrieving known fate-determining network motifs.