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Updated: May 10, 2026

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Published on: August 6, 2013

Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.

Huasen Jiang¹, Xiaoyu Huang¹, Xiangpeng Bi¹

¹College of Computer Science and Technology, Ocean University of China, 238 Songling Road, Laoshan District, Qingdao 266404, China.

Briefings in Bioinformatics

|March 8, 2026

Summary

This summary is machine-generated.

Artificial intelligence virtual cells (AIVC) offer a new research paradigm by simulating cellular functions. This review proposes a unified framework for AIVC, addressing challenges to advance predictive life sciences.

Keywords:

AI for science bioinformatics smart healthcare virtual cell

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Last Updated: May 10, 2026

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Published on: November 7, 2025

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Traditional biological research faces limitations in spatiotemporal resolution and processing power for understanding dynamic cross-scale biological events.
Artificial intelligence (AI) advancements have led to the development of the AI virtual cell (AIVC) as a novel research approach.
AIVC integrates multi-omics data and multidisciplinary models to create digital twin systems for simulating cell functions and behaviors.

Purpose of the Study:

To propose a unified definition and technical framework for AI virtual cells (AIVC).
To analyze the cross-scale coupling mechanisms within the "gene-protein-pathway-cell" hierarchy.
To provide a comprehensive overview of existing AIVC models and datasets.

Main Methods:

Decomposition of the AIVC technical construction framework into cross-scale representation engineering, functional submodule design, and multi-component dynamic regulation.
Integration of multi-omics data and multidisciplinary models.
Analysis of cross-scale coupling mechanisms in biological systems.

Main Results:

A proposed unified definition and technical framework for AIVC.
Detailed analysis of "gene-protein-pathway-cell" hierarchy cross-scale coupling mechanisms.
Summary of current AIVC models and datasets, identifying key challenges like data heterogeneity and model interpretability.

Conclusions:

AIVC represents a significant shift in life sciences, moving towards predictability and innovation.
Addressing AIVC's challenges is crucial for accelerating research progress.
This review provides a foundational framework to guide future AIVC development and application.