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Updated: Jan 12, 2026

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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Squidiff: predicting cellular development and responses to perturbations using a diffusion model.

Siyu He^1,2,3, Yuefei Zhu¹, Daniel Naveed Tavakol¹

¹Department of Biomedical Engineering, Columbia University, New York, NY, USA.

|November 3, 2025

Summary

This summary is machine-generated.

Squidiff, a new diffusion model, predicts cell transcriptomic changes during differentiation, gene perturbation, and drug responses. This tool aids in understanding cell fate and disease mechanisms.

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Single-cell sequencing reveals cellular heterogeneity and responses to stimuli.
Mapping transcriptomic changes and disease mechanisms across cell types is challenging.

Purpose of the Study:

Introduce Squidiff, a diffusion model-based generative framework.
Predict transcriptomic changes in diverse cell types under various environmental conditions.
Facilitate in silico screening and hypothesis generation for cell fate decisions.

Main Methods:

Utilized a diffusion model-based generative framework (Squidiff).
Integrated continuous denoising and semantic feature learning.
Applied the model to cell differentiation, gene perturbation, and drug response prediction.

Main Results:

Demonstrated Squidiff's robustness across multiple biological scenarios.
Successfully modeled blood vessel organoid development and cellular responses to stimuli.
Enabled prediction of high-resolution transcriptomic landscapes over time and conditions.

Conclusions:

Squidiff enables in silico screening of molecular landscapes and cellular state transitions.
Facilitates rapid hypothesis generation for biological research.
Provides insights into the regulatory principles governing cell fate decisions.