Literature DB >> 18593664

A computational framework for modelling solid tumour growth.

Bryn A Lloyd1, Dominik Szczerba, Markus Rudin, Gábor Székely.   

Abstract

The biology of cancer is a complex interplay of many underlying processes, taking place at different scales both in space and time. A variety of theoretical models have been developed, which enable one to study certain components of the cancerous growth process. However, most previous approaches only focus on specific aspects of tumour development, largely ignoring the influence of the evolving tumour environment. In this paper, we present an integrative framework to simulate tumour growth, including those model components that are considered to be of major importance. We start by addressing issues at the tissue level, where the phenomena are modelled as continuum partial differential equations. We extend this model with relevant components at the cellular or even sub-cellular level in a vertical fashion. We present an implementation of this framework, covering the major processes and treat the mechanical deformation due to growth, the biochemical response to hypoxia, blood flow, oxygenation and the explicit development of a vascular system in a coupled way. The results demonstrate the feasibility of the approach and its applicability to in silico studies of the influence of different treatment strategies (like the usage of novel anti-cancer drugs) for more effective therapy design.

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Year:  2008        PMID: 18593664     DOI: 10.1098/rsta.2008.0092

Source DB:  PubMed          Journal:  Philos Trans A Math Phys Eng Sci        ISSN: 1364-503X            Impact factor:   4.226


  7 in total

Review 1.  Multiscale imaging and computational modeling of blood flow in the tumor vasculature.

Authors:  Eugene Kim; Spyros Stamatelos; Jana Cebulla; Zaver M Bhujwalla; Aleksander S Popel; Arvind P Pathak
Journal:  Ann Biomed Eng       Date:  2012-05-08       Impact factor: 3.934

2.  A novel medical image data-based multi-physics simulation platform for computational life sciences.

Authors:  Esra Neufeld; Dominik Szczerba; Nicolas Chavannes; Niels Kuster
Journal:  Interface Focus       Date:  2013-04-06       Impact factor: 3.906

3.  Nonlinear modelling of cancer: bridging the gap between cells and tumours.

Authors:  J S Lowengrub; H B Frieboes; F Jin; Y-L Chuang; X Li; P Macklin; S M Wise; V Cristini
Journal:  Nonlinearity       Date:  2010

4.  Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity.

Authors:  Muriel Mescam; Marek Kretowski; Johanne Bezy-Wendling
Journal:  IEEE Trans Med Imaging       Date:  2009-09-15       Impact factor: 10.048

5.  A bioimage informatics based reconstruction of breast tumor microvasculature with computational blood flow predictions.

Authors:  Spyros K Stamatelos; Eugene Kim; Arvind P Pathak; Aleksander S Popel
Journal:  Microvasc Res       Date:  2013-12-14       Impact factor: 3.514

6.  Multiscale modelling of vascular tumour growth in 3D: the roles of domain size and boundary conditions.

Authors:  Holger Perfahl; Helen M Byrne; Tingan Chen; Veronica Estrella; Tomás Alarcón; Alexei Lapin; Robert A Gatenby; Robert J Gillies; Mark C Lloyd; Philip K Maini; Matthias Reuss; Markus R Owen
Journal:  PLoS One       Date:  2011-04-13       Impact factor: 3.240

7.  A Validated Multiscale In-Silico Model for Mechano-sensitive Tumour Angiogenesis and Growth.

Authors:  Vasileios Vavourakis; Peter A Wijeratne; Rebecca Shipley; Marilena Loizidou; Triantafyllos Stylianopoulos; David J Hawkes
Journal:  PLoS Comput Biol       Date:  2017-01-26       Impact factor: 4.475

  7 in total

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