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Related Experiment Video

Updated: Jan 16, 2026

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

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Identifying an optimal perturbation to induce a desired cell state by generative deep learning.

Younghyun Han¹, Hyunjin Kim¹, Chun-Kyung Lee¹

¹Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

|September 25, 2025

Summary

We developed PAIRING, a deep learning tool to identify cellular perturbations for desired cell states. This method aids in understanding gene expression changes and has potential for therapeutic development.

Keywords:

cell state control computational biology deep learning perturbation identification systems biology

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Published on: December 7, 2021

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Controlling cell states is crucial in biological research.
Identifying specific perturbations for desired cell state changes is challenging.

Purpose of the Study:

To present PAIRING (perturbation identifier to induce desired cell states using generative deep learning).
To identify cellular perturbations that lead to specific, desired cell states.

Main Methods:

PAIRING embeds cell states into a latent space, decomposing them into basal states and perturbation effects.
Optimal perturbations are identified by comparing decomposed effects with the desired cell state transition vector.
The method utilizes generative deep learning for perturbation identification.

Main Results:

PAIRING successfully identifies perturbations to transform cell states across various transcriptome datasets.
The tool was used to identify perturbations for reverting colorectal cancer cells to a normal-like state.
Simulations provided mechanistic insights into gene expression changes induced by perturbations.

Conclusions:

PAIRING offers a novel approach to identify perturbations for targeted cell state control.
The method has significant potential for advancing therapeutic development in various biological domains.
PAIRING provides mechanistic insights into gene expression regulation.