Learning noisy tissue dynamics across time scales

• URL: http://arxiv.org/abs/2510.19090v2
• Date: Wed, 05 Nov 2025 11:39:05 GMT
• Title: Learning noisy tissue dynamics across time scales
• Authors: Ming Han, John Devany, Michel Fruchart, Margaret L. Gardel, Vincenzo Vitelli,
• Abstract summary: We introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies.<n>This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural differential equations.<n>We show that our model not only captures cell motion but also predicts the evolution of cell states in their division cycle.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural stochastic differential equations where cells are edges of an evolving graph. Cell interactions are encoded in a dual signaling graph capable of handling signaling cascades. The dual graph architecture of our neural networks reflects the architecture of the underlying biological tissues, substantially reducing the amount of data needed for training, compared to convolutional or fully-connected neural networks. Taking epithelial tissue experiments as a case study, we show that our model not only captures stochastic cell motion but also predicts the evolution of cell states in their division cycle. Finally, we demonstrate that our method can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by stochastic ERK waves, paving the way for its use as a digital twin in bioengineering and clinical contexts.

Related papers

• Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing [6.035352293182252]
  We present a neuro-inspired approach to reservoir computing in which a network of in vitro cultured cortical neurons serves as the physical reservoir.<n>We evaluate the system across a sequence of tasks of increasing difficulty, ranging from pointwise stimuli to oriented bars, clock-digit-like shapes, and handwritten digits from the MNIST dataset.
  arXiv  Detail & Related papers  (2026-02-05T15:02:07Z)
• Augmented Structure Preserving Neural Networks for cell biomechanics [2.38142799291692]
  We present a new approach that combines Structure Preserving Neural Networks, which study cell movements as a purely mechanical system, with other Machine Learning tools.<n>This new model, tested on simulated and real cell migration cases, predicts complete cell trajectories following a roll-out policy with a high level of accuracy.
  arXiv  Detail & Related papers  (2025-09-05T07:32:50Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models [68.89389652724378]
  NOBLE is a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection.<n>It predicts distributions of neural dynamics accounting for the intrinsic experimental variability.<n>NOBLE is the first scaled-up deep learning framework validated on real experimental data.
  arXiv  Detail & Related papers  (2025-06-05T01:01:18Z)
• Graph-Based Representation Learning of Neuronal Dynamics and Behavior [2.3859858429583665]
  We introduce the Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN), a novel framework that models time-varying neuronal connectivity.<n>TAVRNN learns latent dynamics at the single-unit level while maintaining interpretable population-level representations.<n>We validate TAVRNN on three diverse datasets: (1) electrophysiological data from a freely behaving rat, (2) primate somatosensory cortex recordings during a reaching task, and (3) biological neurons in the DishBrain platform interacting with a virtual game environment.
  arXiv  Detail & Related papers  (2024-10-01T13:19:51Z)
• Learning Dynamics from Multicellular Graphs with Deep Neural Networks [7.263827692589625]
  We show that using a graph neural network (GNN), the motion of multicellular collectives can be inferred from a static snapshot of cell positions.
  arXiv  Detail & Related papers  (2024-01-22T18:36:29Z)
• Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields [56.69755544814834]
  We present evidence suggesting that depthwise convolutional kernels are effectively replicating the biological receptive fields observed in the mammalian retina. We propose a scheme that draws inspiration from the biological receptive fields.
  arXiv  Detail & Related papers  (2024-01-18T18:06:22Z)
• Rapid prediction of lab-grown tissue properties using deep learning [0.0]
  We use machine learning tools to predict the role of mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. The machine learning algorithm is significantly faster than the biophysical method. Future extensions for scaffolds and 3D bioprinting will open additional applications.
  arXiv  Detail & Related papers  (2023-03-31T12:49:37Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity [0.0]
  We produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. Our models were able to produce a level of network-wide synchrony. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.
  arXiv  Detail & Related papers  (2021-10-15T17:55:04Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.