Wang Zhihui, Deisboeck Thomas S, Cristini Vittorio
Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
Department of Radiology, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA.
IET Syst Biol. 2014 Oct;8(5):191-7. doi: 10.1049/iet-syb.2013.0026.
There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale 'in silico' cancer models. The first is increased computational intensity, since a multiscale cancer model generally takes longer to run than does a scale-specific model. The second problem is the lack of a best GSA method that fits all types of models, which implies that multiple methods and their sequence need to be taken into account. In this study, the authors therefore propose a sampling-based GSA workflow consisting of three phases - pre-analysis, analysis and post-analysis - by integrating Monte Carlo and resampling methods with the repeated use of analysis of variance; they then exemplify this workflow using a two-dimensional multiscale lung cancer model. By accounting for all parameter rankings produced by multiple GSA methods, a summarised ranking is created at the end of the workflow based on the weighted mean of the rankings for each input parameter. For the cancer model investigated here, this analysis reveals that extracellular signal-regulated kinase, a downstream molecule of the epidermal growth factor receptor signalling pathway, has the most important impact on regulating both the tumour volume and expansion rate in the algorithm used.
研究人员在对多尺度“计算机模拟”癌症模型进行全局敏感性分析（GSA）时面临两个挑战。第一个挑战是计算强度增加，因为多尺度癌症模型通常比特定尺度模型运行时间更长。第二个问题是缺乏适用于所有类型模型的最佳GSA方法，这意味着需要考虑多种方法及其顺序。因此，在本研究中，作者通过将蒙特卡罗方法和重采样方法与重复使用方差分析相结合，提出了一个基于采样的GSA工作流程，该流程包括三个阶段——预分析、分析和后分析；然后他们使用二维多尺度肺癌模型对该工作流程进行了举例说明。通过考虑多种GSA方法产生的所有参数排名，在工作流程结束时基于每个输入参数排名的加权平均值创建一个汇总排名。对于此处研究的癌症模型，该分析表明，细胞外信号调节激酶（表皮生长因子受体信号通路中的一个下游分子）在所用算法中对调节肿瘤体积和扩展速率具有最重要的影响。
