生物系统的数学和计算模型越来越复杂,通常由混合多尺度方法组成,如常微分方程,偏微分方程,基于代理和基于规则的模型,等。这些机械模型同时模拟整个主机分辨率的细节,多器官,器官,组织,细胞,分子,和基因组动力学。缺乏解析和数值方法,解决复杂的生物模型需要基于迭代参数采样的方法来建立适当的模型参数范围,以捕获相应的实验数据集。然而,这些模型通常包含大量参数,因此自由度很大。因此,随着时间和空间的推移,将这些模型拟合到多个实验数据集会带来重大挑战。在这项工作中,我们承担着审查的任务,测试,并在模型和数据集类型之间推进校准实践,以比较模型校准的方法。评估校准模型的过程包括权衡每种方法的强度和适用性以及标准化校准方法。我们的工作比较了我们的模型不可知校准协议(CaliPro)与近似贝叶斯计算(ABC)的性能,以突出优势,弱点,协同作用,以及这些方法之间的差异。我们还提供了CaliPro的下一代更新。我们探索了几种模型实现方式,并提出了选择校准方法以匹配数据集类型和建模约束的决策树。
Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.