In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

中文翻译：

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在系统生物学模型的参数识别中使用定性和定量数据。

在系统生物学中，定性数据通常会生成，但很少用于参数化模型。我们演示了一种方法，其中可以将定性和定量数据进行组合以进行参数识别。在这种方法中，定性数据被转换为对模型输出施加的不平等约束。这些不等式与定量数据点一起使用，以构造一个说明两个数据集的单个标量目标函数。为了说明这种方法，我们估算了描述Raf激活的简单模型的参数。然后，我们将该技术应用于表征酵母细胞周期调控的更精细的模型。我们并入了119个突变酵母菌株的定量时间过程（561个数据点）和定性表型（1647个不等式），以执行153个模型参数的自动识别。我们使用轮廓似然法来量化参数不确定性。我们的结果表明结合定性和定量数据来参数化系统生物学模型的价值。