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Introduction of Hypermatrix and Operator Notation into a Discrete Mathematics Simulation Model of Malignant Tumour Response to Therapeutic Schemes In Vivo. Some Operator Properties

机译:在体内对治疗方案的恶性肿瘤反应的离散数学仿真模型中,引入了超矩阵和算符符号。一些操作员属性

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The tremendous rate of accumulation of experimental and clinical knowledge pertaining to cancer dictates the development of a theoretical framework for the meaningful integration of such knowledge at all levels of biocomplexity. In this context our research group has developed and partly validated a number of spatiotemporal simulation models of in vivo tumour growth and in particular tumour response to several therapeutic schemes. Most of the modeling modules have been based on discrete mathematics and therefore have been formulated in terms of rather complex algorithms (e.g. in pseudocode and actual computer code). However, such lengthy algorithmic descriptions, although sufficient from the mathematical point of view, may render it difficult for an interested reader to readily identify the sequence of the very basic simulation operations that lie at the heart of the entire model. In order to both alleviate this problem and at the same time provide a bridge to symbolic mathematics, we propose the introduction of the notion of hypermatrix in conjunction with that of a discrete operator into the already developed models. Using a radiotherapy response simulation example we demonstrate how the entire model can be considered as the sequential application of a number of discrete operators to a hypermatrix corresponding to the dynamics of the anatomic area of interest. Subsequently, we investigate the operators’ commutativity and outline the “summarize and jump” strategy aiming at efficiently and realistically address multilevel biological problems such as cancer. In order to clarify the actual effect of the composite discrete operator we present further simulation results which are in agreement with the outcome of the clinical study RTOG 83–02, thus strengthening the reliability of the model developed.
机译:与癌症有关的实验和临床知识的大量积累,决定了在所有生物复杂性水平上有效整合此类知识的理论框架的发展。在这种情况下,我们的研究小组已经开发并部分验证了体内肿瘤生长,尤其是对几种治疗方案的肿瘤反应的许多时空模拟模型。大多数建模模块是基于离散数学的,因此是根据相当复杂的算法(例如伪代码和实际计算机代码)制定的。然而,尽管从数学的观点来看,这样冗长的算法描述虽然足够,但可能会使感兴趣的读者难以轻易识别出位于整个模型核心的非常基本的仿真操作的顺序。为了减轻这个问题并同时为符号数学提供桥梁,我们建议将超矩阵的概念与离散算符的概念一起引入已经开发的模型中。使用放疗响应模拟示例,我们演示了如何将整个模型视为与感兴趣的解剖区域的动力学相对应的多个离散算符对超矩阵的顺序应用。随后,我们调查了运营商的可交换性,并概述了“总结和跳跃”策略,旨在有效,现实地解决癌症等多层次生物学问题。为了阐明复合离散算子的实际效果,我们提供了进一步的模拟结果,这些结果与RTOG 83-02临床研究的结果一致,从而增强了所开发模型的可靠性。

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