Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Putting intentions into cell biochemistry: an artificial intelligence perspective.

Catholijn M Jonker1, Jacky L Snoep, Jan Treur

  • 1Department of Artificial Intelligence, Vrije Universiteit Amsterdam, De Boelelaan 1081a, Amsterdam, 1081 HV, The Netherlands. jonker@cs.vu.nl

Journal of Theoretical Biology
|January 12, 2002
PubMed
Summary

This study introduces an Artificial Intelligence approach to model complex cellular processes, simplifying the understanding of bacterial regulation. This AI method efficiently predicts cellular responses, overcoming limitations of traditional computational models.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Phosphoglucose isomerase is important for carbon distribution over central carbon metabolic pathways in cyanobacteria.

Plant physiology and biochemistry : PPB·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

Metabolic thermodynamics: pertinent reference state and energy potentials.

The FEBS journal·2026
Same author

From human teams to hybrid intelligence teams: identifying, characterizing, and evaluating foundational quality attributes.

Autonomous agents and multi-agent systems·2026
Same author

Identification of a distinct family of polyphosphate kinases relevant to energy homeostasis in Crenarchaeota.

The Journal of biological chemistry·2026
Same author

Hallmarks of disease: how tuned hierarchies of intelligent molecular neural networks (MNNs) may matter.

Endocrine-related cancer·2025

Area of Science:

  • Computational Biology
  • Systems Biology
  • Artificial Intelligence in Biology

Background:

  • Living cells exhibit complex, nonlinearly interacting processes that hinder comprehensive understanding.
  • Traditional methods involve intensive calculations of individual rate equations, often yielding complex results.
  • Cellular 'decisions' involve functional units, suggesting potential for simplified modeling.

Purpose of the Study:

  • To explore the application of Artificial Intelligence (AI) methods for modeling cellular regulation.
  • To demonstrate a simpler approach to understanding complex biological systems.
  • To validate the AI method for specific bacterial regulatory phenomena.

Main Methods:

  • Utilizing an AI description method based on beliefs, desires, and intentions for modeling cellular agents.

Related Experiment Videos

  • Applying the AI method to describe essential aspects of cellular regulation.
  • Implementing the computational model in Prolog for enhanced efficiency.

    • Main Results:

    • Successfully described essential aspects of cellular regulation using the AI approach.
    • Demonstrated the method's efficacy for catabolite repression and substrate induction in Escherichia coli.
    • Showcased the computational efficiency and accuracy of the Prolog implementation.

    Conclusions:

    • AI-based modeling offers a simplified and efficient alternative to traditional methods for understanding cellular regulation.
    • The AI approach effectively models complex biological phenomena, reducing reliance on tedious human reasoning.
    • This study highlights the potential of AI in deciphering intricate biological systems like bacterial metabolism.