Engineering morphogenesis of cell clusters with differentiable programming

• URL: http://arxiv.org/abs/2407.06295v1
• Date: Mon, 8 Jul 2024 18:05:11 GMT
• Title: Engineering morphogenesis of cell clusters with differentiable programming
• Authors: Ramya Deshpande, Francesco Mottes, Ariana-Dalia Vlad, Michael P. Brenner, Alma dal Co,
• Abstract summary: 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.
• Score: 2.0690546196799042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the rules underlying organismal development is a major unsolved problem in biology. Each cell in a developing organism responds to signals in its local environment by dividing, excreting, consuming, or reorganizing, yet how these individual actions coordinate over a macroscopic number of cells to grow complex structures with exquisite functionality is unknown. Here we use recent advances in automatic differentiation to discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We consider a growing tissue with cellular interactions are mediated by morphogen diffusion, differential cell adhesion and mechanical stress. Each cell has an internal genetic network that it uses to make decisions based on its local environment. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios, including the symmetry breaking of an embryo from an initial cell, the creation of emergent chemical gradients,homogenization of growth via mechanical stress, programmed growth into a prespecified shape, and the ability to repair from damage. When combined with recent experimental advances measuring spatio-temporal dynamics and gene expression of cells in a growing tissue, the methodology outlined here offers a promising path to unravelling the cellular basis of development.

Related papers

• How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities [46.671834972945874]
  We propose a vision of leveraging advances in AI to construct virtual cells. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration.
  arXiv  Detail & Related papers  (2024-09-18T02:41:50Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• 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)
• Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems [59.117526206317116]
  We show that CELL can adaptively evolve into different models for different tasks and data. Experiments on four real-world datasets demonstrate that CELL significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-29T02:35:23Z)
• 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)
• 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)
• 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)
• 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)
• Empowered Neural Cellular Automata [0.0]
  empowerment measures the amount of control an agent exerts on its environment via its sensorimotor system. We show that the addition of empowerment as a secondary objective in the evolution of a neural cellular automaton results in higher fitness compared to evolving for morphogenesis alone.
  arXiv  Detail & Related papers  (2022-04-27T19:37:26Z)
• Epigenetic evolution of deep convolutional models [81.21462458089142]
  We build upon a previously proposed neuroevolution framework to evolve deep convolutional models. We propose a convolutional layer layout which allows kernels of different shapes and sizes to coexist within the same layer. The proposed layout enables the size and shape of individual kernels within a convolutional layer to be evolved with a corresponding new mutation operator.
  arXiv  Detail & Related papers  (2021-04-12T12:45:16Z)

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.