Zhang Le, Chen L Leon, Deisboeck Thomas S
Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Math Comput Simul. 2009 Mar 1;79(7):2021-2035. doi: 10.1016/j.matcom.2008.09.007.
In advancing discrete-based computational cancer models towards clinical applications, one faces the dilemma of how to deal with an ever growing amount of biomedical data that ought to be incorporated eventually in one form or another. Model scalability becomes of paramount interest. In an effort to start addressing this critical issue, here, we present a novel multi-scale and multi-resolution agent-based in silico glioma model. While 'multi-scale' refers to employing an epidermal growth factor receptor (EGFR)-driven molecular network to process cellular phenotypic decisions within the micro-macroscopic environment, 'multi-resolution' is achieved through algorithms that classify cells to either active or inactive spatial clusters, which determine the resolution they are simulated at. The aim is to assign computational resources where and when they matter most for maintaining or improving the predictive power of the algorithm, onto specific tumor areas and at particular times. Using a previously described 2D brain tumor model, we have developed four different computational methods for achieving the multi-resolution scheme, three of which are designed to dynamically train on the high-resolution simulation that serves as control. To quantify the algorithms' performance, we rank them by weighing the distinct computational time savings of the simulation runs versus the methods' ability to accurately reproduce the high-resolution results of the control. Finally, to demonstrate the flexibility of the underlying concept, we show the added value of combining the two highest-ranked methods. The main finding of this work is that by pursuing a multi-resolution approach, one can reduce the computation time of a discrete-based model substantially while still maintaining a comparably high predictive power. This hints at even more computational savings in the more realistic 3D setting over time, and thus appears to outline a possible path to achieve scalability for the all-important clinical translation.
在将基于离散的计算癌症模型推进到临床应用的过程中，人们面临着如何处理数量不断增长的生物医学数据这一困境，这些数据最终应以某种形式纳入模型。模型的可扩展性变得至关重要。为了开始解决这个关键问题，在此我们提出一种新颖的基于多尺度和多分辨率代理的计算机模拟胶质瘤模型。“多尺度”是指利用表皮生长因子受体（EGFR）驱动的分子网络在微观 - 宏观环境中处理细胞表型决策，而“多分辨率”则通过将细胞分类为活跃或不活跃空间簇的算法来实现，这些簇决定了它们被模拟的分辨率。目的是在对维持或提高算法预测能力最为重要的地点和时间，将计算资源分配到特定的肿瘤区域和特定时间。我们使用先前描述的二维脑肿瘤模型，开发了四种不同的计算方法来实现多分辨率方案，其中三种方法旨在对作为对照的高分辨率模拟进行动态训练。为了量化算法的性能，我们通过权衡模拟运行中不同的计算时间节省与方法准确重现对照高分辨率结果的能力来对它们进行排名。最后，为了证明基本概念的灵活性，我们展示了将排名最高的两种方法相结合的附加价值。这项工作的主要发现是，通过采用多分辨率方法，可以大幅减少基于离散模型的计算时间，同时仍保持相当高的预测能力。这暗示随着时间推移，在更现实的三维环境中可能会节省更多计算资源，因此似乎勾勒出了一条实现至关重要的临床转化可扩展性的可能途径。
