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Open Access Research

Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units

Le Zhang12*, Beini Jiang1, Yukun Wu3, Costas Strouthos4, Phillip Zhe Sun5, Jing Su6 and Xiaobo Zhou6*

Author Affiliations

1 Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA

2 College of Computer and Information Science, Southwest University, Chongqing, 400715, China

3 Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, MD 21201, USA

4 Computation-based Science and Technology Research Center, The Cyprus Institute, 1645 Nicosia, Cyprus

5 Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

6 Department of Pathology, the Methodist Hospital, Research Institute & Weill Cornell Medical College, 6565 Fannin St, Houston, Texas, USA

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Theoretical Biology and Medical Modelling 2011, 8:46  doi:10.1186/1742-4682-8-46

Published: 16 December 2011

Abstract

Multiscale agent-based modeling (MABM) has been widely used to simulate Glioblastoma Multiforme (GBM) and its progression. At the intracellular level, the MABM approach employs a system of ordinary differential equations to describe quantitatively specific intracellular molecular pathways that determine phenotypic switches among cells (e.g. from migration to proliferation and vice versa). At the intercellular level, MABM describes cell-cell interactions by a discrete module. At the tissue level, partial differential equations are employed to model the diffusion of chemoattractants, which are the input factors of the intracellular molecular pathway. Moreover, multiscale analysis makes it possible to explore the molecules that play important roles in determining the cellular phenotypic switches that in turn drive the whole GBM expansion. However, owing to limited computational resources, MABM is currently a theoretical biological model that uses relatively coarse grids to simulate a few cancer cells in a small slice of brain cancer tissue. In order to improve this theoretical model to simulate and predict actual GBM cancer progression in real time, a graphics processing unit (GPU)-based parallel computing algorithm was developed and combined with the multi-resolution design to speed up the MABM. The simulated results demonstrated that the GPU-based, multi-resolution and multiscale approach can accelerate the previous MABM around 30-fold with relatively fine grids in a large extracellular matrix. Therefore, the new model has great potential for simulating and predicting real-time GBM progression, if real experimental data are incorporated.