IEEE Trans Biomed Eng. 2014 Mar;61(3):966-74. doi: 10.1109/TBME.2013.2294469.
An important problem in the study of cancer is the understanding of the heterogeneous nature of the cell population. The clonal evolution of the tumor cells results in the tumors being composed of multiple subpopulations. Each subpopulation reacts differently to any given therapy. This calls for the development of novel (regulatory network) models, which can accommodate heterogeneity in cancerous tissues. In this paper, we present a new approach to model heterogeneity in cancer. We model heterogeneity as an ensemble of deterministic Boolean networks based on prior pathway knowledge. We develop the model considering the use of qPCR data. By observing gene expressions when the tissue is subjected to various stimuli, the compositional breakup of the tissue under study can be determined. We demonstrate the viability of this approach by using our model on synthetic data, and real-world data collected from fibroblasts.
癌症研究中的一个重要问题是理解细胞群体的异质性。肿瘤细胞的克隆进化导致肿瘤由多个亚群组成。每个亚群对任何给定的治疗反应都不同。这就需要开发新的（调控网络）模型，这些模型可以适应癌组织的异质性。在本文中，我们提出了一种新的方法来模拟癌症中的异质性。我们将异质性建模为基于先前通路知识的确定性布尔网络的集合。我们在考虑使用 qPCR 数据的情况下开发了该模型。通过观察组织在受到各种刺激时的基因表达情况，可以确定研究组织的组成性分裂。我们通过在合成数据和从成纤维细胞收集的真实世界数据上使用我们的模型来证明这种方法的可行性。
