Joslyn Louis R, Kirschner Denise E, Linderman Jennifer J
Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 48109-2136 USA.
Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI 48109-5620 USA.
Cell Mol Bioeng. 2020 Sep 15;14(1):31-47. doi: 10.1007/s12195-020-00650-z. eCollection 2021 Feb.
Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short.
Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro.
We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals.
We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.
数学和计算建模在揭示生物系统机制及进行预测方面有着悠久的历史。然而，要创建一个能够提供相关定量预测的模型，首先必须通过重现该系统现有的生物学数据集来对模型进行校准。当前的校准方法可能不适用于复杂的生物模型，原因如下：1）许多方法仅试图重现实验数据的一个方面（如中位数趋势）；2）贝叶斯技术需要指定参数先验和与实验数据的似然性，但这些往往无法自信地确定。当现有方法不足时，需要一种新的校准协议来校准复杂模型。
在此，我们开发了CaliPro，这是一种迭代的、与模型无关的校准协议，它利用参数密度估计来优化参数空间并校准到时间生物学数据集。CaliPro的一个重要方面是用户定义的通过集定义，它指定了模型成功重现实验数据的方式。我们定义了使用CaliPro的适当设置。
我们通过四个例子说明了CaliPro的有用性，包括捕食者 - 猎物、传染病传播和免疫反应模型。我们表明，CaliPro对于确定性的连续模型结构以及随机的离散模型都能很好地工作，并说明CaliPro可以适用于各种校准目标。
我们提出了CaliPro，这是一种将复杂生物模型校准到一系列实验结果的新方法。除了加快校准速度外，CaliPro在已校准的参数空间中可能也很有用，可用于定位和分离特定的模型行为以进行进一步分析。
