What can we learn from global sensitivity analysis of biochemical systems?
What can we learn from global sensitivity analysis of biochemical systems?
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DOI：
10.1371/journal.pone.0079244
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Mendes P
中科院分区：
文献类型：
--
作者：
Kent E;Neumann S;Kummer U;Mendes P
大多数中等大小的生物模型，可能还有所有大型模型，都需要科普其许多参数值未知的事实。此外，可能无法根据实验数据明确确定这些值。这就提出了一个问题，使用这种模型进行预测的可靠性如何。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，这种分析的结果可能依赖于一组精确的参数值。为了缓解这个问题，全局灵敏度分析技术被用来计算参数的灵敏度在一个更广泛的参数空间。我们应用全局敏感性分析选择五个信号和代谢模型，其中几个纳入实验确定的参数。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如何变化。我们的研究结果表明，与生理参数值计算的参数灵敏度不一定是最经常观察到的随机抽样下，即使在一个小的间隔周围的生理值。经常观察到多峰分布。不出所料，可能的灵敏度系数值的范围增加的参数不确定性的水平，虽然控制模式能够改变的参数不确定性的量不同的模型分析。我们建议，这种水平的不确定性可以作为一个全球性的衡量模型的鲁棒性。最后，不同的全球敏感性分析技术的比较表明，如果高吞吐量的计算资源是可用的，那么随机抽样实际上可能是最合适的技术。
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.
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