[2510.02903] Learning Explicit Single-Cell Dynamics Using ODE Representations

Computer Science > Machine Learning arXiv:2510.02903 (cs) [Submitted on 3 Oct 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Learning Explicit Single-Cell Dynamics Using ODE Representations Authors:Jan-Philipp von Bassewitz, Adeel Pervez, Marco Fumero, Matthew Robinson, Theofanis Karaletsos, Francesco Locatello View a PDF of the paper titled Learning Explicit Single-Cell Dynamics Using ODE Representations, by Jan-Philipp von Bassewitz and 5 other authors View PDF HTML (experimental) Abstract:Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rapid growth of single-cell datasets, this has also become a particularly promising and active domain for machine learning. Current state-of-the-art models, however, rely on computationally expensive optimal transport preprocessing and multi-stage training, while also not discovering explicit gene interactions. To address these challenges we propose Cell-Mechanistic Neural Networks (Cell-MNN), an encoder-decoder architecture whose latent representation is a locally linearized ODE governing the dynamics of cellular evolution from stem to tissue cells. Cell-MNN is fully end-to-end (besides a standard PCA pre-processing) and its ODE representation explicitly learns biologically consistent and interpretable gene interactions. Empirically, we show that Cell-MNN achieves competitive performance on single...