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Related Experiment Videos

An algorithm to fit the Gompertz function to growth curves.

H Pasternak1, B A Shalev

  • 1Agricultural Engineering Institute, Agricultural Research Organization, Bet Dagan, Israel.

Computer Applications in the Biosciences : CABIOS
|June 1, 1992
PubMed
Summary
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A new algorithm simplifies fitting the Gompertz growth function on microcomputers. This method offers a practical approach for biological growth curves, livestock management, and tumor growth analysis.

Area of Science:

  • Computational Biology
  • Biomathematics
  • Agricultural Science

Background:

  • The Gompertz function is widely used to model biological growth.
  • Existing methods for fitting the Gompertz function can be computationally intensive or require mainframe access.
  • There is a need for accessible and efficient algorithms for growth curve analysis.

Purpose of the Study:

  • To present a novel, microcomputer-compatible algorithm for fitting the Gompertz growth function.
  • To demonstrate the algorithm's utility in various biological and agricultural applications.
  • To provide an alternative to complex computational methods for growth modeling.

Main Methods:

  • Development of a searching technique algorithm to solve equations derived from the Gompertz function.

Related Experiment Videos

  • Implementation of the algorithm for microcomputer programming.
  • Application and validation using experimental livestock growth data.

    • Main Results:

    • The algorithm provides an easy-to-program solution for fitting the Gompertz growth function.
    • Successful application demonstrated on livestock growth data, indicating practical utility.
    • The method is effective even with limited access to mainframe computing resources.

    Conclusions:

    • The presented algorithm offers a valuable and accessible tool for Gompertz function fitting.
    • This approach supports diverse applications, including biological growth modeling, livestock enterprise management, and clinical tumor analysis.
    • The algorithm's simplicity and microcomputer compatibility enhance its practical applicability in research and industry.