Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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CaliPro：一种利用参数密度估计探索参数空间和校准复杂生物模型的校准协议

介绍

数学和计算建模在揭示机制和预测生物系统方面有着悠久的历史。然而，要创建一个可以提供相关定量预测的模型，必须首先通过概括该系统中现有的生物数据集来校准模型。当前的校准方法可能不适用于复杂的生物模型，因为：1）许多人试图仅概括实验数据的单个方面（例如中值趋势）或 2）贝叶斯技术需要指定参数先验和实验数据的可能性不能总是自信地分配。当当前方法不足时，需要一种新的校准协议来校准复杂的模型。

方法

在这里，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细化参数空间并校准到时间生物数据集。CaliPro 的一个重要方面是用户定义的传递集定义，它指定模型如何成功地概括实验数据。我们定义了适当的设置以使用 CaliPro。

结果

我们通过四个例子来说明 CaliPro 的有用性，包括捕食者-猎物、传染病传播和免疫反应模型。我们展示了 CaliPro 既适用于确定性的连续模型结构，也适用于随机的离散模型，并说明 CaliPro 可以跨越不同的校准目标。

结论

我们介绍了 CaliPro，这是一种将复杂生物模型校准为一系列实验结果的新方法。除了加快校准速度外，CaliPro 还可用于已校准的参数空间，以针对和隔离特定模型行为以进行进一步分析。