On the equivalence between Agent-Based and Continuum models for cell population modeling. Application to glioblastoma evolution in microfluidic devices

Raquel B. Arroyo-Vazquez, Marina Perez-Aliacar, Jacobo Ayensa-Jimenez, Manuel Doblare
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Abstract

Mathematical models are invaluable tools for understanding the mechanisms and interactions that control the behavior of complex systems. Modeling a problem as cancer evolution includes many coupled phenomena being therefore impossible to obtain sufficient experimental results to fully evaluate all possible conditions. In this work, we focus on Agent-Based Models (ABMs), as these models allow to obtain more complete and interpretable information at the individual level than other types of in silico models. However, ABMs, need many parameters, requiring more information at the cellular and environmental levels to be calibrated. To overcome this problem we propose a complementary approach to traditional calibration methods. We used existent continuum models able to reproduce experimental data, validated and with fitted parameters, to establish relationships between parameters of both, continuum and agent-based models, to simplify and improve the process of adjusting the parameters of the ABM. With this approach, it is possible to bridge the gap between both kinds of models, allowing to work with them simultaneously and take advantage of the benefits of each of them. To illustrate this methodology, the evolution of glioblastoma (GB) is modeled as an example of application. The resulting ABM obtains very similar results to those previously obtained with the continuum model, replicating the main histopathological features (the formation of necrotic cores and pseudopalisades) appearing in several different in vitro experiments in microfluidic devices, as we previously obtained with continuum models. However, ABMs have additional advantages: since they also incorporates the inherent random effects present in Biology, providing a more natural explanation and a deeper understanding of biological processes. Moreover, additional relevant phenomena can be easily incorporated, such as the mechanical interaction between cells or with the environment, angiogenic processes and cell concentrations far from the continuum requirement as happens, for intance, with immune cells.
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基于代理的细胞群体建模与连续模型之间的等效性。微流控设备中胶质母细胞瘤演化的应用
数学模型是了解控制复杂系统行为的机制和相互作用的宝贵工具。像癌症进化这样的建模问题包括许多耦合现象,因此不可能获得足够的实验结果来全面评估所有可能的条件。在这项工作中,我们将重点放在基于代理的模型(ABMs)上,因为与其他类型的硅学模型相比,这些模型可以在个体水平上获得更完整、更可解释的信息。然而,ABM 需要很多参数,需要更多细胞和环境层面的信息来校准。为了克服这个问题,我们提出了一种与传统校准方法互补的方法。我们使用能够重现实验数据、经过验证并具有拟合参数的现有连续性模型,建立连续性模型和基于代理的模型参数之间的关系,以简化和改进人工智能模型参数的调整过程。有了这种方法,就有可能弥合这两种模型之间的差距,从而可以同时使用这两种模型,并利用它们各自的优势。为了说明这种方法,我们以胶质母细胞瘤(GB)的演变为应用实例进行建模。由此产生的 ABM 得到的结果与之前用连续体模型得到的结果非常相似,复制了在微流控设备中进行的几项不同体外实验中出现的主要组织病理学特征(坏死核心和假脓肿的形成),就像我们之前用连续体模型得到的结果一样。然而,ABM 还具有额外的优势:因为它们还包含了生物学中固有的随机效应,提供了更自然的解释,并加深了对生物过程的理解。此外,其他相关现象也很容易纳入其中,例如细胞之间或与环境之间的机械相互作用、血管生成过程以及远离连续性要求的细胞浓度,例如免疫细胞。
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