Computational and mathematical models in biology rely heavily on the parameters that characterize them. However, robust estimates for their values are typically elusive and thus a large parameter space becomes necessary for model study, particularly to make translationally impactful predictions. Sampling schemes exploring parameter spaces for models are used for a variety of purposes in systems biology, including model calibration and sensitivity analysis. Typically, random sampling is used; however, when models have a high number of unknown parameters or the models are highly complex, computational cost becomes an important factor. This issue can be reduced through the use of efficient sampling schemes such as Latin hypercube sampling (LHS) and Sobol sequences. In this work, we compare and contrast three sampling schemes – random sampling, LHS, and Sobol sequences – for the purposes of performing both parameter sensitivity analysis and model calibration. In addition, we apply these analyses to different types of computational and mathematical models of varying complexity: a simple ODE model, a complex ODE model, and an agent-based model. In general, the sampling scheme had little effect when used for calibration efforts, but when applied to sensitivity analyses, Sobol sequences exhibited faster convergence. While the observed benefit to convergence is relatively small, Sobol sequences are computationally less expensive to compute than LHS samples and also have the benefit of being deterministic, which allows for better reproducibility of results.

生物学中的计算和数学模型在很大程度上依赖于表征它们的参数。然而，对其值的稳健估计通常难以捉摸，因此模型研究需要大参数空间，特别是做出具有转化影响力的预测。探索模型参数空间的采样方案在系统生物学中用于多种目的，包括模型校准和敏感性分析。通常采用随机抽样；然而，当模型具有大量未知参数或模型高度复杂时，计算成本就成为一个重要因素。通过使用有效的采样方案（例如拉丁超立方采样 (LHS) 和 Sobol 序列）可以减少此问题。在这项工作中，我们比较和对比了三种采样方案——随机采样、LHS 和 Sobol 序列——以执行参数敏感性分析和模型校准。此外，我们将这些分析应用于不同复杂度的不同类型的计算和数学模型：简单的 ODE 模型、复杂的 ODE 模型和基于代理的模型。一般来说，采样方案在用于校准工作时影响不大，但在应用于敏感性分析时，Sobol 序列表现出更快的收敛速度。虽然观察到的收敛益处相对较小，但 Sobol 序列的计算成本比 LHS 样本要低，而且还具有确定性的优点，从而可以实现更好的结果再现性。