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Related Concept Videos

Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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Introduction to Enzymes

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Catalytically Perfect Enzymes01:07

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
Synthetic Biology02:55

Synthetic Biology

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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Enzyme Kinetics

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  3. Artificial Intelligence Tools For Enzyme Engineering And Metabolic Engineering.
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  3. Artificial Intelligence Tools For Enzyme Engineering And Metabolic Engineering.

Related Experiment Video

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
08:10

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

Published on: August 8, 2016

Artificial intelligence tools for enzyme engineering and metabolic engineering.

Michael Volk1, Vishal Kaushik Pillalamarri2, Ruihan Guo3

  • 1Department of Chemical and Biomolecular Engineering, 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
|June 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing energy biotechnology by enhancing enzyme engineering and metabolic engineering. AI models are integrating diverse data for designing better enzymes and microbial factories.

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GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

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GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
11:38

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Synthetic Biology

Background:

  • Enzyme engineering and metabolic engineering are key to advancing energy biotechnology.
  • Artificial intelligence (AI) has emerged as a powerful tool in designing enzymes and microbial cell factories.
  • Recent progress highlights AI's role in optimizing biological systems for energy applications.

Purpose of the Study:

  • To review recent advancements in AI-driven enzyme redesign and metabolic engineering.
  • To explore the application of AI in creating novel enzymes and improving microbial productivity.
  • To discuss the integration of AI models across different biological scales.

Main Methods:

  • Utilizing protein language models for enzyme redesign.
  • Employing generative models for de novo enzyme design.
  • Applying AI tools for engineering metabolic pathways and cellular phenotypes.
  • Integrating diverse data representations for AI model development.

    • Main Results:

    • AI models are increasingly successful in designing effective enzymes.
    • AI tools are enhancing the productivity of microbial cell factories.
    • AI is enabling the engineering of complex metabolic networks and cellular functions.
    • A shift towards integrated AI models that consider multiple data modalities is observed.

    Conclusions:

    • AI is a transformative technology in energy biotechnology, accelerating innovation in enzyme and metabolic engineering.
    • Integrating diverse data representations across scales is crucial for future advancements in AI-driven biotechnology.
    • The synergy between AI and biological engineering holds significant promise for sustainable energy solutions.