Literature DB >> 15142752

Modelling cell population growth with applications to cancer therapy in human tumour cell lines.

Britta Basse1, Bruce C Baguley, Elaine S Marshall, Graeme C Wake, David J N Wall.   

Abstract

In this paper we present an overview of the work undertaken to model a population of cells and the effects of cancer therapy. We began with a theoretical one compartment size structured cell population model and investigated its asymptotic steady size distributions (SSDs) (On a cell growth model for plankton, MMB JIMA 21 (2004) 49). However these size distributions are not similar to the DNA (size) distributions obtained experimentally via the flow cytometric analysis of human tumour cell lines (data obtained from the Auckland Cancer Society Research Centre, New Zealand). In our one compartment model, size was a generic term, but in order to obtain realistic steady size distributions we chose size to be DNA content and devised a multi-compartment mathematical model for the cell division cycle where each compartment corresponds to a distinct phase of the cell cycle (J. Math. Biol. 47 (2003) 295). We then incorporated another compartment describing the possible induction of apoptosis (cell death) from mitosis phase (Modelling cell death in human tumour cell lines exposed to anticancer drug paclitaxel, J. Math. Biol. 2004, in press). This enabled us to compare our model to flow cytometric data of a melanoma cell line where the anticancer drug, paclitaxel, had been added. The model gives a dynamic picture of the effects of paclitaxel on the cell cycle. We hope to use the model to describe the effects of other cancer therapies on a number of different cell lines. Copyright 2004 Elsevier Ltd.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15142752     DOI: 10.1016/j.pbiomolbio.2004.01.017

Source DB:  PubMed          Journal:  Prog Biophys Mol Biol        ISSN: 0079-6107            Impact factor:   3.667


  7 in total

1.  In vitro ovine articular chondrocyte proliferation: experiments and modelling.

Authors:  L Mancuso; M I Liuzzo; S Fadda; M Pisu; A Cincotti; M Arras; G La Nasa; A Concas; G Cao
Journal:  Cell Prolif       Date:  2010-04-14       Impact factor: 6.831

2.  Analysis of radiation-induced changes to human melanoma cultures using a mathematical model.

Authors:  B Basse; W R Joseph; E S Marshall; B C Baguley
Journal:  Cell Prolif       Date:  2010-04       Impact factor: 6.831

3.  Experimental analysis and modelling of in vitro proliferation of mesenchymal stem cells.

Authors:  L Mancuso; M I Liuzzo; S Fadda; M Pisu; A Cincotti; M Arras; E Desogus; F Piras; G Piga; G La Nasa; A Concas; G Cao
Journal:  Cell Prolif       Date:  2009-07-10       Impact factor: 6.831

4.  Implications of a simple mathematical model to cancer cell population dynamics.

Authors:  A L Garner; Y Y Lau; D W Jordan; M D Uhler; R M Gilgenbach
Journal:  Cell Prolif       Date:  2006-02       Impact factor: 6.831

5.  In silico simulation of the effect of hypoxia on MCF-7 cell cycle kinetics under fractionated radiotherapy.

Authors:  Adrian S Remigio
Journal:  J Biol Phys       Date:  2021-09-17       Impact factor: 1.560

6.  Heterogeneous structure of stem cells dynamics: statistical models and quantitative predictions.

Authors:  Paul Bogdan; Bridget M Deasy; Burhan Gharaibeh; Timo Roehrs; Radu Marculescu
Journal:  Sci Rep       Date:  2014-04-28       Impact factor: 4.379

7.  Cell cycle times of short-term cultures of brain cancers as predictors of survival.

Authors:  C E Furneaux; E S Marshall; K Yeoh; S J Monteith; P J Mews; C A Sansur; R J Oskouian; K J Sharples; B C Baguley
Journal:  Br J Cancer       Date:  2008-10-14       Impact factor: 7.640

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.