Learning Explicit Single-Cell Dynamics Using ODE Representations

• URL: http://arxiv.org/abs/2510.02903v1
• Date: Fri, 03 Oct 2025 11:15:16 GMT
• Title: Learning Explicit Single-Cell Dynamics Using ODE Representations
• Authors: Jan-Philipp von Bassewitz, Adeel Pervez, Marco Fumero, Matthew Robinson, Theofanis Karaletsos, Francesco Locatello,
• Abstract summary: Cell-Mechanistic Neural Networks (Cell-MNN) is an encoder-decoder architecture whose latent representation is a locally linearized ODE governing the dynamics of cellular evolution from stem to tissue cells.<n>We show that Cell-MNN achieves competitive performance on single-cell benchmarks, surpasses state-of-the-art baselines in scaling to larger datasets and joint training across multiple datasets, while also learning interpretable gene interactions that we validate against the TRRUST database of gene interactions.
• Score: 33.16920280365721
• License: http://creativecommons.org/licenses/by/4.0/
• 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-cell benchmarks, surpasses state-of-the-art baselines in scaling to larger datasets and joint training across multiple datasets, while also learning interpretable gene interactions that we validate against the TRRUST database of gene interactions.

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