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Updated: May 16, 2025

The Multifaceted Benefits of Protein Co-expression in Escherichia coli
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[Research progress in mechanism models and artificial intelligence models for protein expression systems].

Yi Yang1,2, Jun DU1,2, Chunhe Yang1,2

  • 1Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.

Sheng Wu Gong Cheng Xue Bao = Chinese Journal of Biotechnology
|April 2, 2025
PubMed
Summary

This review explores protein expression models, including mechanism-based and AI-driven approaches. These models help understand protein production and optimize sequences for biotechnology applications.

Keywords:
artificial intelligencechemical reaction networkdeep learningprotein expression systemtranscriptiontranslation

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Area of Science:

  • Molecular Biology
  • Biotechnology
  • Computational Biology

Background:

  • Protein expression is a complex, regulated process vital for cellular function and biotechnology.
  • Understanding protein expression mechanisms is key to cellular organization and biotechnological advancements.
  • Current models aim to simulate and predict protein expression dynamics.

Purpose of the Study:

  • To review recent progress in mechanism models and AI prediction algorithms for protein expression.
  • To explore quantitative simulation of protein expression processes.
  • To analyze regulatory factors influencing protein expression.

Main Methods:

  • Review of chemical reaction network models for simulating protein expression elementary processes.
  • Examination of artificial intelligence (AI) prediction algorithms for analyzing regulatory factors.
  • Discussion of data-driven AI models for sequence optimization.

Main Results:

  • Mechanism models mathematically describe protein expression but face parameter determination challenges.
  • AI models focus on sequence features (protein/DNA) to predict and optimize expression.
  • Combined mechanism and AI models offer deeper insights into protein expression.

Conclusions:

  • Protein expression modeling is crucial for understanding cellular regulation and advancing biotechnology.
  • AI and mechanism models provide complementary approaches to studying protein expression.
  • Integrated models support efficient high-value protein production and coordinated protein regulation.