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Protein Diffusion in the Membrane

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...

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Related Experiment Video

Updated: Jun 3, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

MorphoDiff: Cellular Morphology Painting with Diffusion Models.

Zeinab Navidi^1,2,3, Jun Ma^2,3, Esteban A Miglietta⁴

¹Department of Computer Science, University of Toronto, Toronto, ON, Canada.

Biorxiv : the Preprint Server for Biology

|January 7, 2025

Summary

This summary is machine-generated.

MorphoDiff is a new AI pipeline that predicts cell morphology changes from chemical or genetic perturbations. This tool generates high-resolution cell images, aiding drug discovery by exploring cellular responses in silico.

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Last Updated: Jun 3, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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Area of Science:

Cellular Biology
Bioinformatics
Computational Biology

Background:

Understanding cellular responses to stimuli is vital for biological research and drug development.
High-content image-based assays are cost-effective for studying cellular phenotypes and biological processes.

Purpose of the Study:

Introduce MorphoDiff, a generative pipeline for predicting high-resolution cell morphological responses to perturbations.
Provide the first framework for guided, high-resolution cell morphology predictions across chemical and genetic interventions.

Main Methods:

Integrate perturbation embeddings as guiding signals within a 2D latent diffusion model.
Utilize three open-source Cell Painting datasets for validation.

Main Results:

MorphoDiff generates high-fidelity cell morphology images.
The model produces biologically meaningful signals across diverse interventions.
Demonstrated generalization across chemical and genetic perturbations.

Conclusions:

MorphoDiff facilitates efficient in silico exploration of perturbational landscapes.
The framework supports more effective drug discovery studies by predicting cellular responses.