Deep learning of geometrical cell division rules

2025/07/30 by Alexandre Durrmeyer, Durrmeyer, Alexandre, Jean-Christophe Palauqui +4 · 2 voices
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Numerical Analysis Techniques #Cell Behavior (q-bio.CB) #FOS: Biological sciences #FOS: Computer and information sciences #I.2.6 #I.6 #J.3 #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.CB #q-bio.QM

paper · pdf · doi:10.48550/arxiv.2507.22587

openalex publication_date 2025/07/30 · arxiv published 2025/07/30 · arxiv updated 2025/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The positioning of new cellular walls during cell division plays a key role in shaping plant tissue organization. The influence of cell geometry on the positioning of division planes has been previously captured into various geometrical rules. Accordingly, linking cell shape to division orientation has relied on the comparison between observed division patterns and predictions under specific rules. The need to define a priori the tested rules is a fundamental limitation of this hypothesis-driven approach. As an alternative, we introduce a data-based approach to investigate the relation between cell geometry and division plane positioning, exploiting the ability of deep neural network to learn complex relationships across multidimensional spaces. Adopting an image-based cell representation, we show how division patterns can be learned and predicted from mother cell geometry using a UNet architecture modified to operate on cell masks. Using synthetic data and A. thaliana embryo cells, we evaluate the model performances on a wide range of diverse cell shapes and division patterns. We find that the trained model accounted for embryo division patterns that were previously irreconcilable under existing geometrical rules. Our work shows the potential of deep networks to understand cell division patterns and to generate new hypotheses on the control of cell division positioning.

Citations

• Opportunities and challenges for deep learning in cell dynamics research
• Denoising Diffusion Probabilistic Models
• On Translation Invariance in CNNs: Convolutional Layers can Exploit\n Absolute Spatial Location
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Deep Unsupervised Learning using Nonequilibrium Thermodynamics
• Adam: A Method for Stochastic Optimization
• Multilayer feedforward networks are universal approximators

Discussions

• How could deep learning help us to understand the patterns of cell division in developing plant tissues? Find out more in our latest preprint, 'Deep Learning of Geometrical Cell Division Rules' by A. [bsky, 4 points, 0 comments]
• This also looks interesting, excited to read- arxiv.org/abs/2507.22587 [bsky, 2 points, 0 comments]

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