Kent Edward, Neumann Stefan, Kummer Ursula, Mendes Pedro
School of Computer Science and Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom.
PLoS One. 2013 Nov 14;8(11):e79244. doi: 10.1371/journal.pone.0079244. eCollection 2013.
Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique.
大多数中等规模的生物学模型，可能所有大规模模型，都需要应对许多参数值未知这一事实。此外，基于实验数据可能无法明确识别这些值。这就提出了一个问题，即使用此类模型做出的预测有多可靠。敏感性分析通常用于衡量每个模型参数对其变量的影响。然而，由于非线性，此类分析的结果可能取决于一组精确的参数值。为了缓解这个问题，全局敏感性分析技术被用于在更广泛的参数空间中计算参数敏感性。我们将全局敏感性分析应用于五个信号传导和代谢模型的选择，其中几个模型纳入了实验确定良好的参数。假设这些模型代表生理现实，我们探讨了在参数不确定性增加的情况下结果可能如何变化。我们的结果表明，即使在生理值周围的小范围内，用生理参数值计算的参数敏感性在随机抽样下不一定是最常观察到的。经常观察到多峰分布。不出所料，可能敏感性系数值的范围随着参数不确定性水平的增加而增加，尽管在所分析的模型中，能够改变控制模式的参数不确定性量有所不同。我们建议，这种不确定性水平可以用作模型稳健性的全局度量。最后，对不同全局敏感性分析技术的比较表明，如果有高通量计算资源，那么随机抽样实际上可能是最合适的技术。
