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Updated: Jan 12, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
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.
Abstract:
Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design-build-test-learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.
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