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Updated: Sep 9, 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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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, NY.

Biorxiv : the Preprint Server for Biology

|September 5, 2025

Summary

This summary is machine-generated.

Squidiff predicts cell transcriptomic changes, accelerating drug discovery and understanding disease mechanisms. This computational tool aids in rapid hypothesis generation for precision medicine applications.

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Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Single-cell sequencing reveals cellular heterogeneity but mapping transcriptomic changes to stimuli is challenging.
Current experimental methods for studying cellular responses to stimuli like radiation or drugs are labor-intensive.
Elucidating disease mechanisms requires efficient tools to predict cellular behavior under various conditions.

Purpose of the Study:

To develop a novel computational framework, Squidiff, for predicting transcriptomic alterations in diverse cell types in response to environmental stimuli.
To demonstrate the utility of Squidiff in modeling complex biological processes and predicting cellular responses.
To facilitate *in silico* screening of molecular landscapes for accelerated hypothesis generation and drug discovery.

Main Methods:

Developed Squidiff, a diffusion model-based generative framework for predicting transcriptomic changes.
Integrated continuous denoising and semantic feature learning to capture transient cell states.
Applied the model to diverse scenarios including cell differentiation, gene perturbation, and drug response prediction.

Main Results:

Squidiff accurately predicts transcriptomic landscapes across various cell types and conditions.
Demonstrated robustness in modeling cell differentiation, gene perturbations, and drug responses.
Successfully modeled blood vessel organoid development and cellular responses to neutron irradiation and growth factors.

Conclusions:

Squidiff enables efficient *in silico* prediction of transcriptomic changes, overcoming experimental limitations.
The framework facilitates rapid hypothesis generation and provides insights for precision medicine.
Squidiff represents a significant advancement in computational tools for biological research and drug development.