Mathematical modelling is a widely used technique for describing the temporal behaviour of biological systems. One of the most challenging topics in computational systems biology is the calibration of non-linear models; i.e. the estimation of their unknown parameters. The state-of-the-art methods in this field are the frequentist and Bayesian approaches. For both of them, the performance and accuracy of results greatly depend on the sampling technique employed. Here, the authors test a novel Bayesian procedure for parameter estimation, called conditional robust calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin hypercube sampling. CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. They apply CRC with both sampling strategies to the three ordinary differential equations (ODEs) models of increasing complexity. They obtain a more precise and reliable solution through logarithmically spaced samples.

中文翻译：

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在计算系统生物学中将条件稳健校准应用于常微分方程模型：两种采样策略的比较。

数学建模是一种广泛使用的技术，用于描述生物系统的时间行为。计算系统生物学中最具挑战性的课题之一是非线性模型的校准；即对其未知参数的估计。该领域最先进的方法是频率论和贝叶斯方法。对于它们两者，结果的性能和准确性在很大程度上取决于所采用的采样技术。在这里，作者测试了一种新的用于参数估计的贝叶斯程序，称为条件稳健校准 (CRC)，比较了两种不同的采样技术：均匀和对数拉丁超立方采样。CRC 是一种基于参数空间采样和参数密度函数估计的迭代算法。他们将具有两种采样策略的 CRC 应用于复杂度不断增加的三个常微分方程 (ODE) 模型。他们通过对数间隔的样本获得更精确和可靠的解决方案。