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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Microorganisms rely on proteins as an essential carbon and energy source, particularly in environments with limited polysaccharides or lipids. However, proteins are too large to cross the plasma membrane unaided, necessitating enzymatic degradation. Microbes secrete extracellular proteases and peptidases that hydrolyze proteins into peptides, which can then be transported across the membrane. Once inside the cell, intracellular proteases degrade these peptides into free amino acids, which...
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Artificial intelligence-powered biofoundries for protein engineering and metabolic engineering.

Junyu Chen1, Nilmani Singh2, Jingxia Lu3

  • 1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States.

Current Opinion in Biotechnology
|November 5, 2025
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Summary
This summary is machine-generated.

Synthetic biology leverages artificial intelligence (AI) and automated biofoundries to accelerate protein and metabolic engineering. This shift enables autonomous experimentation, driving innovation in synthetic biology.

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Area of Science:

  • Synthetic biology
  • Artificial intelligence
  • Biofoundries

Background:

  • Synthetic biology is advancing rapidly.
  • Integration of artificial intelligence (AI) and automated biofoundries is a key development.
  • This convergence transforms traditional experimental approaches.

Purpose of the Study:

  • To review recent advances in AI and biofoundry integration for synthetic biology.
  • To highlight the impact on protein and metabolic engineering.
  • To emphasize the potential for accelerated scientific discovery.

Main Methods:

  • Review of workflow development in AI-powered biofoundries.
  • Analysis of AI models for biological applications.
  • Examination of integration strategies between AI and biofoundries.

Main Results:

  • AI and biofoundries accelerate the design-build-test-learn cycle.
  • Automation shifts engineering from manual to autonomous experimentation.
  • Significant progress in automated protein and metabolic engineering.

Conclusions:

  • AI-powered biofoundries are revolutionizing synthetic biology.
  • Autonomous experimentation enhances efficiency and speed.
  • This integration promises accelerated innovation and discovery.