Literature DB >> 21528359

Trans-theta logistics: a new family of population growth sigmoid functions.

F Kozusko1, M Bourdeau.   

Abstract

Sigmoid functions have been applied in many areas to model self limited population growth. The most popular functions; General Logistic (GL), General von Bertalanffy (GV), and Gompertz (G), comprise a family of functions called Theta Logistic ([Formula: see text] L). Previously, we introduced a simple model of tumor cell population dynamics which provided a unifying foundation for these functions. In the model the total population (N) is divided into reproducing (P) and non-reproducing/quiescent (Q) sub-populations. The modes of the rate of change of ratio P/N was shown to produce GL, GV or G growth. We now generalize the population dynamics model and extend the possible modes of the P/N rate of change. We produce a new family of sigmoid growth functions, Trans-General Logistic (TGL), Trans-General von Bertalanffy (TGV) and Trans-Gompertz (TG)), which as a group we have named Trans-Theta Logistic (T [Formula: see text] L) since they exist when the [Formula: see text] L are translated from a two parameter into a three parameter phase space. Additionally, the model produces a new trigonometric based sigmoid (TS). The [Formula: see text] L sigmoids have an inflection point size fixed by a single parameter and an inflection age fixed by both of the defining parameters. T [Formula: see text] L and TS sigmoids have an inflection point size defined by two parameters in bounding relationships and inflection point age defined by three parameters (two bounded). While the Theta Logistic sigmoids provided flexibility in defining the inflection point size, the Trans-Theta Logistic sigmoids provide flexibility in defining the inflection point size and age. By matching the slopes at the inflection points we compare the range of values of inflection point age for T [Formula: see text] L versus [Formula: see text] L for model growth curves.

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Year:  2011        PMID: 21528359     DOI: 10.1007/s10441-011-9131-3

Source DB:  PubMed          Journal:  Acta Biotheor        ISSN: 0001-5342            Impact factor:   1.774


  2 in total

1.  Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach.

Authors:  Amiya Ranjan Bhowmick; Gaurangadeb Chattopadhyay; Sabyasachi Bhattacharya
Journal:  J Biol Phys       Date:  2014-01-10       Impact factor: 1.365

2.  A noble extended stochastic logistic model for cell proliferation with density-dependent parameters.

Authors:  Trina Roy; Sinchan Ghosh; Bapi Saha; Sabyasachi Bhattacharya
Journal:  Sci Rep       Date:  2022-05-30       Impact factor: 4.996

  2 in total

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