TY - JOUR
T1 - Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models
AU - Wang, Zhihui
AU - Deisboeck, Thomas S.
AU - Cristini, Vittorio
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2014.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
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U2 - 10.1049/iet-syb.2013.0026
DO - 10.1049/iet-syb.2013.0026
M3 - Article
C2 - 25257020
AN - SCOPUS:84907700291
SN - 1751-8849
VL - 8
SP - 191
EP - 197
JO - IET Systems Biology
JF - IET Systems Biology
IS - 5
ER -