ArticleTheoretical biology & medical modelling2011
Developing a multiscale, multi-resolution agent-based brain tumor model by graphics processing units.
Article in Theoretical biology & medical modelling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- A Review of the Application of Spatial Transcriptomics in Neuroscience.Interdisciplinary sciences, computational life sciences · 2024Review
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction.Acta pharmaceutica Sinica. B · 2021Article
- High-Performance Agent-Based Modeling Applied to Vocal Fold Inflammation and Repair.Frontiers in physiology · 2018Article
- Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.International journal of molecular sciences · 2017Article
- Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke.Neural plasticity · 2017Review
- Article
- Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.Cellular and molecular bioengineering · 2015Article
- Simulating cancer growth with multiscale agent-based modeling.Seminars in cancer biology · 2015Review
- Spatial pattern dynamics of 3D stem cell loss of pluripotency via rules-based computational modeling.PLoS computational biology · 2013Article
- High-Performance Host-Device Scheduling and Data-Transfer Minimization Techniques for Visualization of 3D Agent-Based Wound Healing Applications.PDPTA '19 : proceedings of the 2019 International Conference on Parallel & Distributed Processing Techniquess & Applications. International Conference on Parallel and Distributed Processing Techniques and Applications (2019 : Las Vegas,...Article
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Authors and funding
7 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.