Multicell-Fold: geometric learning in folding multicellular life

• URL: http://arxiv.org/abs/2407.07055v2
• Date: Mon, 22 Jul 2024 17:59:15 GMT
• Title: Multicell-Fold: geometric learning in folding multicellular life
• Authors: Haiqian Yang, Anh Q. Nguyen, Dapeng Bi, Markus J. Buehler, Ming Guo,
• Abstract summary: 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.
• Score: 0.34952465649465553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. To tackle this question, we propose a geometric deep learning model that can predict multicellular folding and embryogenesis, accurately capturing the highly convoluted spatial interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements before they occur at single-cell resolution. Furthermore, using an activation map and ablation studies, we demonstrate that cell geometries and cell junction networks together regulate local cell rearrangement which is critical for embryo morphogenesis. This approach provides a novel paradigm to study morphogenesis, highlighting a unified data structure and harnessing the power of geometric deep learning to accurately model the mechanisms and behaviors of cells during development. It offers a pathway toward creating a unified dynamic morphological atlas for a variety of developmental processes such as embryogenesis.

Related papers

• 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)
• 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)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• 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)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• Real-time Evolution of Multicellularity with Artificial Gene Regulation [0.0]
  This paper presents a real-time simulation involving ''protozoan-like'' cells that evolve by natural selection in a physical 2D ecosystem.
  arXiv  Detail & Related papers  (2023-05-20T17:39:07Z)
• Topology-Guided Multi-Class Cell Context Generation for Digital Pathology [28.43244574309888]
  We introduce several mathematical tools from spatial statistics and topological data analysis. We generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.
  arXiv  Detail & Related papers  (2023-04-05T07:01:34Z)
• 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)
• Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells [52.425628028229156]
  We study the properties of grid cells using recurrent network models. We focus on a simple non-linear recurrent model that underlies the continuous attractor neural networks of grid cells.
  arXiv  Detail & Related papers  (2022-10-06T05:26:49Z)
• Growing Isotropic Neural Cellular Automata [63.91346650159648]
  We argue that the original Growing NCA model has an important limitation: anisotropy of the learned update rule. We demonstrate that cell systems can be trained to grow accurate asymmetrical patterns through either of two methods.
  arXiv  Detail & Related papers  (2022-05-03T11:34:22Z)
• Constrained Multi-shape Evolution for Overlapping Cytoplasm Segmentation [49.992392231966015]
  We present a novel and effective shape prior-based approach, called constrained multi-shape evolution. It segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors. In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors.
  arXiv  Detail & Related papers  (2020-04-08T09:08:07Z)

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.