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A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

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Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing.

Zhenkun Shi^1,2, Pi Liu^1,2, Xiaoping Liao^1,2

¹Key Laboratory of Systems Microbial Technology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.

Biodesign Research

|October 18, 2023

Summary

Artificial intelligence (AI) and machine learning (ML) accelerate the design of proteins, pathways, and strains for biomanufacturing. Integrating data-driven methods with mechanistic models is key for developing advanced synthetic strains.

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A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

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Area of Science:

Biotechnology
Synthetic Biology
Bioengineering

Background:

Artificial intelligence (AI) and machine learning (ML) are transforming scientific research.
Data-driven methods are increasingly vital for innovation in biomanufacturing.
Developing artificial cell factories relies on advanced computational approaches.

Purpose of the Study:

To review data-driven methods for designing proteins, pathways, and strains in biomanufacturing.
To present relevant data types, databases, and algorithms (ML and deep learning).
To demonstrate applications in cell factory development and discuss limitations.

Main Methods:

Review of recent studies applying AI/ML in biomanufacturing.
Introduction to data types, databases, and algorithms.
Case examples of data-driven applications in protein design, metabolic modeling, and pathway optimization.

Main Results:

AI/ML methods enable prediction of protein function and improvement of metabolic models.
Data-driven approaches aid in estimating kinetic parameters and designing novel biosynthesis pathways.
Pathway optimization is enhanced through computational analysis.

Conclusions:

Data-driven approaches show significant promise for advancing cell factory development.
Current limitations necessitate integration with mechanistic models for synergistic benefits.
Combined approaches will accelerate the creation of synthetic strains for industrial biomanufacturing.