A biological growth model using continued fraction of straight lines. Methodological aspects

S-shaped curves are ubiquitous in biology especially when it comes to growth of a population or even an individual. Growth models such as the classical Verhulst-Pearl logistic growth equation and its extensions effectively model such S-shaped growth curves. Most of these models are parametrized by three or more parameters. In this work, continued fraction of straight lines has been applied to model S-shaped curves of biological growth through the use of only two parameters a and m. Here, m is the maximum growth rate and a is the parameter restricting the growth rate. The parameters a and m help to better interpret the data when compared to the logistic growth model since m represents factors promoting growth while a represents restricting factors of growth. This model is effective for modeling both population as well as individual growth, especially around the phase of rapid growth.

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Journal
BIOMATH
Published
2025-08-05
DOI
https://doi.org/10.55630/j.biomath.2025.08.055
Primary Topic
thermodynamics and calorimetric analyses
Type
article
Field-Weighted Citation Impact
0.00
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article

A biological growth model using continued fraction of straight lines. Methodological aspects

I. S. Shruti, S. Vijay Prakash
2 links
BIOMATH
thermodynamics and calorimetric analyses
article

A biological growth model using continued fraction of straight lines. Methodological aspects

I. S. Shruti, S. Vijay Prakash
article en

Abstract

S-shaped curves are ubiquitous in biology especially when it comes to growth of a population or even an individual. Growth models such as the classical Verhulst-Pearl logistic growth equation and its extensions effectively model such S-shaped growth curves. Most of these models are parametrized by three or more parameters. In this work, continued fraction of straight lines has been applied to model S-shaped curves of biological growth through the use of only two parameters a and m. Here, m is the maximum growth rate and a is the parameter restricting the growth rate. The parameters a and m help to better interpret the data when compared to the logistic growth model since m represents factors promoting growth while a represents restricting factors of growth. This model is effective for modeling both population as well as individual growth, especially around the phase of rapid growth.

BIOMATHVol. 14(2)
T. D. Medical College (IN)
Openalex Percentile: Top 81%
thermodynamics and calorimetric analyses
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