Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units
Name
1742-4682-8-46.pdf
Size
888.39 KB
Format
Adobe PDF
Checksum (MD5)
db5430ab0ddc2d926311a7f2a909398c
Author(s)
Zhang, Le
Jiang, Beini
Wu, Yukun
Strouthos, Costas
Su, Jing
Zhou, Xiaobo
Sun, Phillip Zhe
Date Issued
December 2011
Journal
Theoretical Biology and Medical Modelling
Publisher
BioMed Central Ltd
Citation
Theoretical Biology and Medical Modelling. 2011 Dec 16;8(1):46
Version
Final published version
Abstract
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.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/1742-4682-8-46
