Learning Dynamics from Multicellular Graphs with Deep Neural Networks

• URL: http://arxiv.org/abs/2401.12196v2
• Date: Mon, 8 Jul 2024 14:24:40 GMT
• Title: Learning Dynamics from Multicellular Graphs with Deep Neural Networks
• Authors: Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu, Monilola A. Olayioye, Markus J. Buehler, Ming Guo,
• Abstract summary: We show that using a graph neural network (GNN), the motion of multicellular collectives can be inferred from a static snapshot of cell positions.
• Score: 7.263827692589625
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multicellular self-assembly into functional structures is a dynamic process that is critical in the development and diseases, including embryo development, organ formation, tumor invasion, and others. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex processes. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that using a graph neural network (GNN), the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets.

Related papers

• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  We introduce Artificial Kuramotoy Neurons (AKOrN) as a dynamical alternative to threshold units. We show that this idea provides performance improvements across a wide spectrum of tasks. We believe that these empirical results show the importance of our assumptions at the most basic neuronal level of neural representation.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• Multicell-Fold: geometric learning in folding multicellular life [0.34952465649465553]
  How a group of cells fold into specific structures is a central question in biology that defines how living organisms form. We propose a geometric deep learning model that can predict multicellular folding and embryogenesis. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements.
  arXiv  Detail & Related papers  (2024-07-09T17:21:49Z)
• Engineering morphogenesis of cell clusters with differentiable programming [2.0690546196799042]
  We discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios.
  arXiv  Detail & Related papers  (2024-07-08T18:05:11Z)
• Integrating GNN and Neural ODEs for Estimating Two-Body Interactions in Mixed-Species Collective Motion [0.0]
  We present a novel deep learning framework to estimate the underlying equations of motion from observed trajectories. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions.
  arXiv  Detail & Related papers  (2024-05-26T09:47:17Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Zyxin is all you need: machine learning adherent cell mechanics [0.0]
  We develop a data-driven biophysical modeling approach to learn the mechanical behavior of adherent cells. We first train neural networks to predict forces generated by adherent cells from images of cytoskeletal proteins. We next develop two approaches - one explicitly constrained by physics, the other more continuum - that help construct data-driven models of cellular forces.
  arXiv  Detail & Related papers  (2023-03-01T02:08:40Z)
• 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.