Literature DB >> 8768220

Growth of necrotic tumors in the presence and absence of inhibitors.

H M Byrne1, M A Chaplin.   

Abstract

A mathematical model is presented for the growth of a multicellular spheroid that comprises a central core of necrotic cells surrounded by an outer annulus of proliferating cells. The model distinguishes two mechanisms for cell loss: apoptosis and necrosis. Cell loss due to apoptosis is defined to be programmed cell death, occurring, for example, when a cell exceeds its natural lifespan, whereas cell death due to necrosis is induced by changes in the cell's microenvironment, occurring, for example, in nutrient-depleted regions. Mathematically, the problem involves tracking two free boundaries, one for the outer tumor radius, the other for the inner necrotic radius. Numerical simulations of the model are presented in an inhibitor-free setting and an inhibitor-present setting for various parameter values. The effects of nutrients and inhibitors on the existence and stability of the time-independent solutions of the model are studied using a combination of numerical and asymptotic techniques.

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Year:  1996        PMID: 8768220     DOI: 10.1016/0025-5564(96)00023-5

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


  25 in total

1.  Initial/boundary-value problems of tumor growth within a host tissue.

Authors:  Andrea Tosin
Journal:  J Math Biol       Date:  2013-01       Impact factor: 2.259

2.  An Adaptive Multigrid Algorithm for Simulating Solid Tumor Growth Using Mixture Models.

Authors:  S M Wise; J S Lowengrub; V Cristini
Journal:  Math Comput Model       Date:  2011-01-01

Review 3.  Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Authors:  Sandeep Sanga; Hermann B Frieboes; Xiaoming Zheng; Robert Gatenby; Elaine L Bearer; Vittorio Cristini
Journal:  Neuroimage       Date:  2007-06-07       Impact factor: 6.556

4.  Bridging the gap between individual-based and continuum models of growing cell populations.

Authors:  Mark A J Chaplain; Tommaso Lorenzi; Fiona R Macfarlane
Journal:  J Math Biol       Date:  2019-06-10       Impact factor: 2.259

5.  Stochastic tunnels in evolutionary dynamics.

Authors:  Yoh Iwasa; Franziska Michor; Martin A Nowak
Journal:  Genetics       Date:  2004-03       Impact factor: 4.562

6.  Computer simulation of glioma growth and morphology.

Authors:  Hermann B Frieboes; John S Lowengrub; S Wise; X Zheng; Paul Macklin; Elaine L Bearer; Vittorio Cristini
Journal:  Neuroimage       Date:  2007-03-23       Impact factor: 6.556

7.  Spatial invasion dynamics on random and unstructured meshes: implications for heterogeneous tumor populations.

Authors:  V S K Manem; M Kohandel; N L Komarova; S Sivaloganathan
Journal:  J Theor Biol       Date:  2014-01-23       Impact factor: 2.691

8.  Multiscale modelling and nonlinear simulation of vascular tumour growth.

Authors:  Paul Macklin; Steven McDougall; Alexander R A Anderson; Mark A J Chaplain; Vittorio Cristini; John Lowengrub
Journal:  J Math Biol       Date:  2008-09-10       Impact factor: 2.259

9.  Nonlinear simulations of solid tumor growth using a mixture model: invasion and branching.

Authors:  Vittorio Cristini; Xiangrong Li; John S Lowengrub; Steven M Wise
Journal:  J Math Biol       Date:  2008-09-12       Impact factor: 2.259

Review 10.  The mathematics of cancer: integrating quantitative models.

Authors:  Philipp M Altrock; Lin L Liu; Franziska Michor
Journal:  Nat Rev Cancer       Date:  2015-12       Impact factor: 60.716

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