Oduola Wasiu Opeyemi, Li Xiangfang
Department of Electrical and Computer Engineering (ECE), Prairie View A&M University, Prairie View, TX, USA.
Cancer Inform. 2018 Jul 27;17:1176935118790262. doi: 10.1177/1176935118790262. eCollection 2018.
Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.
有效的癌症治疗策略需要在多个时间和空间尺度上理解癌症行为和发展。这导致人们对开发能够模拟癌症生长、发展以及对药物治疗反应的多尺度数学模型的兴趣日益浓厚。因此，本研究使用随机混合系统建模框架，研究整合药物药代动力学和药效学（PK/PD）信息的多尺度肿瘤建模。具体而言，（1）采用由微分方程建模的途径来描述分子水平的基因调控；（2）针对细胞和多细胞尺度提出细胞自动机（CA）模型。通过考虑基因表达水平、细胞周期和微环境，使用马尔可夫链对细胞行为进行建模。所提出的模型能够在给定的分子特性、微环境条件和药物PK/PD概况下预测肿瘤生长。模拟结果证明了所提方法的有效性，且结果与观察到的肿瘤行为一致。
