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Multiphasic growth curve analysis in mice.

W J Koops1, M Grossman, E Michalska

  • 1Department of Animal Breeding, Agricultural University, Wageningen, Netherlands.

Growth
|January 1, 1987
PubMed
Summary
This summary is machine-generated.

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A triphasic growth function better models mouse body weight data than a monophasic one. This multiphasic approach offers deeper insights into the biology of mouse growth.

Area of Science:

  • * Comparative animal physiology
  • * Biomathematics and statistical modeling

Background:

  • * Accurate modeling of animal growth is crucial for understanding developmental biology and physiological processes.
  • * Traditional monophasic growth functions may not fully capture the complex dynamics of growth in mammals.

Purpose of the Study:

  • * To compare the goodness-of-fit of monophasic (logistic) and triphasic growth functions for mouse body weight data.
  • * To evaluate the impact of using mean versus individual weights on growth curve parameters.
  • * To investigate sex-specific differences in growth patterns using multiphasic models.

Main Methods:

  • * Fitting monophasic and triphasic growth functions to body weight data from male and female mice.
  • * Assessing goodness-of-fit using residual variances and Durbin-Watson statistics.

Related Experiment Videos

  • * Analyzing growth curve parameters, including asymptotic weights and auto-correlation.

    • Main Results:

    • * The triphasic function demonstrated a superior fit compared to the monophasic function, indicated by smaller and less correlated residuals.
    • * Residual variances were higher for individual weights than mean weights, and higher for males than females.
    • * Parameters of the triphasic model were generally higher for individual weights, with notable sex-specific differences in later growth phases.

    Conclusions:

    • * Multiphasic growth functions provide a more comprehensive understanding of mouse growth dynamics than monophasic models.
    • * The triphasic model effectively captures complexities in growth, offering enhanced biological insights.
    • * Consideration of individual versus mean weights and sex differences is important for accurate growth modeling.