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.
Highlights
Glioblastoma multiforme (GBM) is the most common and aggressive brain cancer [1,2]
The results presented in this paper demonstrate that the graphics processing unit (GPU)-based multi-resolution Multiscale agent-based modeling (MABM) has certain novel features that can help cancer scientists to explore the mechanism of GBM cancer progression
Recently, a variety of cancer research reports have indicated that the epidermal growth factor receptor (EGFR) pathway plays an important role in the directional motility [40,41,42], mitogenic signaling [43,44] and phenotypic switching of cancer cells [20,45]
Summary
Glioblastoma multiforme (GBM) is the most common and aggressive brain cancer [1,2]. Statistics show that it has the worst prognosis of all central nervous system malignancies [3,4]. The GPU-based parallel computing algorithm can model the diffusion of chemoattractants in a large ECM with relatively fine grids in real time, and process computing queries concerning the intracellular signaling pathways of millions of cancer cells in a real cancer progression system. Since in the multi-resolution approach the intracellular molecular pathway is computed only for cells belonging to heterogeneous clusters to determine phenotypic switches, the overall computation time required for the simulation is significantly less than in the MABM. In a realistic cancer progression system, even the heterogeneous clusters of the multi-resolution approach will consist of millions of cells, implying that an enormous computational resource is required to process the cells’ intracellular molecular. Speeding up the computation of the intracellular EGFR molecular pathway module A GPU-based parallel ODE solver (Figure 5) was developed to process intensive computing queries from tens of thousands of GBM cells during simulations of tumor expansion. This research employed PSGMG to accelerate the diffusion solver of MABM
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