CellFlow: Simulating Cellular Morphology Changes via Flow Matching

• URL: http://arxiv.org/abs/2502.09775v1
• Date: Thu, 13 Feb 2025 21:10:00 GMT
• Title: CellFlow: Simulating Cellular Morphology Changes via Flow Matching
• Authors: Yuhui Zhang, Yuchang Su, Chenyu Wang, Tianhong Li, Zoe Wefers, Jeffrey Nirschl, James Burgess, Daisy Ding, Alejandro Lozano, Emma Lundberg, Serena Yeung-Levy,
• Abstract summary: We introduce CellFlow, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations. CellFlow generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes.
• Score: 44.8979602893102
• License:
• Abstract: Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlow, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlow models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects -- a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlow generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlow enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research.

Related papers

• Contrastive learning of cell state dynamics in response to perturbations [0.5665024011188763]
  DynaCLR is a framework for modeling cell dynamics via contrastive learning of representations of time-lapse datasets. We show how DynaCLR can be used to map the morphological dynamics of cells to a temporally regularized embedding space.
  arXiv  Detail & Related papers  (2024-10-15T05:01:25Z)
• Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen [76.02070962797794]
  We present Cell Flow for Generation, a flow-based conditional generative model for multi-modal single-cell counts. Our results suggest improved recovery of crucial biological data characteristics while accounting for novel generative tasks.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• 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)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies [0.0]
  We introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module. We apply our pipeline to analyze microglial cell phagocytosis in FTD and obtain statistically reliable results.
  arXiv  Detail & Related papers  (2023-04-26T18:10:35Z)
• Towards Learned Simulators for Cell Migration [2.5331228143087565]
  A neural simulator for cellular dynamics can augment lab experiments and traditional methods to enhance our understanding of a cell's interaction with its physical environment. We propose an autoregressive probabilistic model that can reproduce dynamics of single cell migration. We observe that standard single-step training methods do not only lead to inconsistent stability, but also fail to accurately capture the aspects of the dynamics.
  arXiv  Detail & Related papers  (2022-10-02T14:01:09Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images [1.408123603417833]
  We propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The obtained results suggest a performance boost over traditional U-Net architectures.
  arXiv  Detail & Related papers  (2021-06-10T15:54:38Z)
• Inference of cell dynamics on perturbation data using adjoint sensitivity [4.606583317143614]
  Data-driven dynamic models of cell biology can be used to predict cell response to unseen perturbations. Recent work had demonstrated the derivation of interpretable models with explicit interaction terms. This work aims to extend the range of applicability of this model inference approach to a diversity of biological systems.
  arXiv  Detail & Related papers  (2021-04-13T19:15:56Z)
• 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.