Glioblastoma Multiforme (GBM), the most malignant human tumour, can be defined by the evolution of growing bio-nanomachine networks within an interplay between self-renewal (Grow) and invasion (Go) potential of mutually exclusive phenotypes of transmitter and receiver cells. Herein, we present a mathematical model for the growth of GBM tumour driven by molecule-mediated inter-cellular communication between two populations of evolutionary bio-nanomachines representing the Glioma Stem Cells (GSCs) and Glioma Cells (GCs). The contribution of each subpopulation to tumour growth is quantified by a voxel model representing the end to end inter-cellular communication models for GSCs and progressively evolving invasiveness levels of glioma cells within a network of diverse cell configurations. Mutual information, information propagation speed and the impact of cell numbers and phenotypes on the communication output and GBM growth are studied by using analysis from information theory. The numerical simulations show that the progression of GBM is directly related to higher mutual information and higher input information flow of molecules between the GSCs and GCs, resulting in an increased tumour growth rate. These fundamental findings contribute to deciphering the mechanisms of tumour growth and are expected to provide new knowledge towards the development of future bio-nanomachine-based therapeutic approaches for GBM.

中文翻译：

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破译分子通讯在多形性胶质母细胞瘤生长中作用的体素模型

多形性胶质母细胞瘤(GBM) 是人类最恶性的肿瘤，可以通过不断增长的生物纳米机器网络的进化来定义，这种网络在发射器和接收器细胞的相互排斥表型的自我更新 (Grow) 和入侵 (Go) 潜力之间相互作用。在此，我们提出了一种由代表胶质瘤干细胞 (GSC) 和胶质瘤细胞 (GC) 的两个进化生物纳米机器群之间分子介导的细胞间通讯驱动的 GBM 肿瘤生长的数学模型。每个亚群对肿瘤生长的贡献通过体素模型量化，该模型代表 GSCs 的端到端细胞间通信模型，并在不同细胞配置的网络中逐渐发展神经胶质瘤细胞的侵袭性水平。相互信息，利用信息论的分析，研究了信息传播速度以及细胞数量和表型对通信输出和GBM生长的影响。数值模拟表明，GBM的进展与GSCs和GCs之间更高的互信息和更高的分子输入信息流直接相关，导致肿瘤生长速率增加。这些基本发现有助于破译肿瘤生长的机制，并有望为未来基于生物纳米机器的 GBM 治疗方法的开发提供新知识。数值模拟表明，GBM的进展与GSCs和GCs之间更高的互信息和更高的分子输入信息流直接相关，导致肿瘤生长速率增加。这些基本发现有助于破译肿瘤生长的机制，并有望为未来基于生物纳米机器的 GBM 治疗方法的开发提供新知识。数值模拟表明，GBM的进展与GSCs和GCs之间更高的互信息和更高的分子输入信息流直接相关，导致肿瘤生长速率增加。这些基本发现有助于破译肿瘤生长的机制，并有望为未来基于生物纳米机器的 GBM 治疗方法的开发提供新知识。