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  • Multi-agent modeling simulation of in-vitro T-cells for immunologic alternatives to cancer treatment

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    Estivill-Castro426427-Published.pdf (1.085Mb)
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    Author(s)
    Nettleton, DF
    Estivill-Castro, V
    Jiménez, EH
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2020
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    Abstract
    There is exciting news in recent developments suggesting the potential to treat some human cancers by stimulating the patients own immune system. However, there is still much to understand; therefore, modelling the battle between those cells that are constituents of the human immune system against tumorous cells can significantly provide insights as mathematical modelling has done regarding the immune system behaviour against virus infections. In this paper we innovate in two directions. First, we move the modelling of immune struggles from the sphere of ordinary-differential equation models to the modelling by multi-agent ...
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    There is exciting news in recent developments suggesting the potential to treat some human cancers by stimulating the patients own immune system. However, there is still much to understand; therefore, modelling the battle between those cells that are constituents of the human immune system against tumorous cells can significantly provide insights as mathematical modelling has done regarding the immune system behaviour against virus infections. In this paper we innovate in two directions. First, we move the modelling of immune struggles from the sphere of ordinary-differential equation models to the modelling by multi-agent simulations. We highlight the advantages of the multi-agent simulation, for example the consideration of elaborate spatial proximity interactions. Secondly, we move away from the realm of infectious diseases to the complex modelling of the stimulation of T-cells and their participation in fighting cancerous cell tumours.
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    Conference Title
    ICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
    Volume
    1
    DOI
    https://doi.org/10.5220/0008915601690178
    Copyright Statement
    © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial intelligence
    Tumour immunology
    Oncology and carcinogenesis
    Science & Technology
    Computer Science
    Simulation
    Publication URI
    http://hdl.handle.net/10072/399465
    Collection
    • Conference outputs

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