Literature DB >> 12941534

Combining Gompertzian growth and cell population dynamics.

Frank Kozusko1, Zeljko Bajzer.   

Abstract

A two-compartment model of cancer cells population dynamics proposed by Gyllenberg and Webb includes transition rates between proliferating and quiescent cells as non-specified functions of the total population, N. We define the net inter-compartmental transition rate function: Phi(N). We assume that the total cell population follows the Gompertz growth model, as it is most often empirically found and derive Phi(N). The Gyllenberg-Webb transition functions are shown to be characteristically related through Phi(N). Effectively, this leads to a hybrid model for which we find the explicit analytical solutions for proliferating and quiescent cell populations, and the relations among model parameters. Several classes of solutions are examined. Our model predicts that the number of proliferating cells may increase along with the total number of cells, but the proliferating fraction appears to be a continuously decreasing function. The net transition rate of cells is shown to retain direction from the proliferating into the quiescent compartment. The death rate parameter for quiescent cell population is shown to be a factor in determining the proliferation level for a particular Gompertz growth curve.

Entities:  

Mesh:

Year:  2003        PMID: 12941534     DOI: 10.1016/s0025-5564(03)00094-4

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  17 in total

1.  Parameter non-identifiability of the Gyllenberg-Webb ODE model.

Authors:  Niklas Hartung
Journal:  J Math Biol       Date:  2013-08-30       Impact factor: 2.259

2.  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

3.  Growth curve analysis of asymptomatic and symptomatic meningiomas.

Authors:  Satoshi Nakasu; Yoko Nakasu; Tadateru Fukami; Junya Jito; Kazuhiko Nozaki
Journal:  J Neurooncol       Date:  2010-08-05       Impact factor: 4.130

4.  Multi-objective optimal chemotherapy control model for cancer treatment.

Authors:  S Algoul; M S Alam; M A Hossain; M A A Majumder
Journal:  Med Biol Eng Comput       Date:  2010-10-01       Impact factor: 2.602

5.  Chemotherapy in conjoint aging-tumor systems: some simple models for addressing coupled aging-cancer dynamics.

Authors:  Mitra S Feizabadi; Tarynn M Witten
Journal:  Theor Biol Med Model       Date:  2010-06-15       Impact factor: 2.432

6.  Accumulation of neutral mutations in growing cell colonies with competition.

Authors:  Ron Sorace; Natalia L Komarova
Journal:  J Theor Biol       Date:  2012-08-23       Impact factor: 2.691

7.  AN EVOLUTIONARY MODEL OF TUMOR CELL KINETICS AND THE EMERGENCE OF MOLECULAR HETEROGENEITY DRIVING GOMPERTZIAN GROWTH.

Authors:  Jeffrey West; Zaki Hasnain; Paul Macklin; Paul K Newton
Journal:  SIAM Rev Soc Ind Appl Math       Date:  2016-11-03       Impact factor: 10.780

8.  Nutrient supply, cell spatial correlation and Gompertzian tumor growth.

Authors:  P Castorina; D Carco'
Journal:  Theory Biosci       Date:  2021-05-14       Impact factor: 1.919

9.  Characterization and quantification of necrotic tissues and morphology in multicellular ovarian cancer tumor spheroids using optical coherence tomography.

Authors:  Feng Yan; Gokhan Gunay; Trisha I Valerio; Chen Wang; Jayla A Wilson; Majood S Haddad; Maegan Watson; Michael O Connell; Noah Davidson; Kar-Ming Fung; Handan Acar; Qinggong Tang
Journal:  Biomed Opt Express       Date:  2021-05-13       Impact factor: 3.732

10.  Optimal minimum variance-entropy control of tumour growth processes based on the Fokker-Planck equation.

Authors:  Maliheh Sargolzaei; Gholamreza Latif-Shabgahi; Mahdi Afshar
Journal:  IET Syst Biol       Date:  2020-12       Impact factor: 1.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.