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Modeling and artificial intelligence approaches to enzyme systems.

D Garfinkel, C A Kulikowski, V W Soo

    Federation Proceedings
    |June 1, 1987
    PubMed
    Summary
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    Biological laboratory microcomputers and artificial intelligence (AI) now enable complex hypothesis testing through modeling. AI techniques applied to microcomputers facilitate physiological system studies, enzyme kinetics, and experimental design.

    Area of Science:

    • Biochemistry
    • Computational Biology
    • Physiology

    Background:

    • Modeling is crucial for formulating and testing complex biological hypotheses.
    • Advancements in microcomputer technology and artificial intelligence (AI) have made sophisticated modeling accessible to researchers.
    • AI shares similarities with modeling, making its techniques applicable to biological research.

    Purpose of the Study:

    • To describe the application of microcomputers and AI in physiological system studies.
    • To demonstrate the use of AI and microcomputers for modeling isolated enzyme kinetics and multienzyme systems.
    • To explore the potential of AI in enhancing the modeling process, including experimental design and data evaluation.

    Main Methods:

    • Utilized an IBM PC microcomputer for fitting kinetic enzyme models and constructing a hexokinase kinetics database.

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  • Employed microcomputer-based programs (BASIC, spreadsheets, databases) for building complex multienzyme models.
  • Explored the application of AI expert systems for model fitting, evaluation, and experimental design.

    • Main Results:

    • Successfully fitted kinetic enzyme models and designed optimal kinetic experiments using a microcomputer.
    • Constructed a comprehensive enzyme kinetics database.
    • Developed methods for building complex multienzyme models using standard software and AI techniques.

    Conclusions:

    • Microcomputers and AI significantly enhance the capabilities for biological modeling and hypothesis testing.
    • AI techniques can automate and improve various stages of the modeling process, from data analysis to experimental design.
    • Future applications of PCs are expected to include solving differential equations and performing sensitivity analysis, further advancing biological modeling.