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Related Experiment Video

Updated: May 4, 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

Innovating cell culture process development with deep learning-powered robotic experimentation using the first

Shuting Xu¹, Yanting Huang¹, Xin Shen¹

¹Cell Culture Process Development (CCPD), WuXi Biologics, Shanghai, China.

Biotechnology Progress

|June 21, 2025

Summary

This study introduces an AI-driven robotic lab framework for biologics process development. It significantly improves cell culture efficiency and protein production titers, accelerating biomanufacturing.

Keywords:

IoT cell culture deep learning digital twin process development robotic experimentation smart lab

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Last Updated: May 4, 2026

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Automated Production of Human Induced Pluripotent Stem Cell-Derived Cortical and Dopaminergic Neurons with Integrated Live-Cell Monitoring

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Published on: August 6, 2020

Area of Science:

Biotechnology
Process Engineering
Artificial Intelligence

Background:

Traditional biologics process development is inefficient, relying on labor-intensive optimization.
Current methods for antibody and recombinant protein production require extensive iterative cell culture optimization.

Purpose of the Study:

To develop an autonomous laboratory framework, the Industrial Smart Lab Framework for Cell Culture (ISLFCC), to enhance cell culture processes.
To accelerate biologics development and biomanufacturing through AI and robotic automation.

Main Methods:

Implemented ISLFCC, combining deep learning (decoder-only transformer models) with robotic experimentation.
Utilized an IoT system for data transmission from bioreactors to AI models and execution of automated actions.
Employed AI to predict cell states and recommend optimal actions like nutrient feed and temperature adjustments.

Main Results:

Achieved an average titer increase of 26.8% for three cell clones in a single batch.
Maintained lactate levels below 1 g/L without late-phase rebound.
Demonstrated enhanced reproducibility, data accuracy, adaptability, and scalability in 3 and 15 L bioreactors.

Conclusions:

The ISLFCC framework offers a transformative, autonomous, and data-driven approach to biomanufacturing.
This AI-driven methodology significantly accelerates biologics development compared to traditional empirical methods.
Represents a paradigm shift towards automated, high-throughput cell culture in biopharmaceutical production.