Miller Ely F, Mallela Abhishek, Neumann Jacob, Lin Yen Ting, Hlavacek William S, Posner Richard G
Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States of America.
Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
ArXiv. 2025 Aug 26:arXiv:2508.19420v1.
Data generated in studies of cellular regulatory systems are often qualitative. For example, measurements of signaling readouts in the presence and absence of mutations may reveal a rank ordering of responses across conditions but not the precise extents of mutation-induced differences. Qualitative data are often ignored by mathematical modelers or are considered in an manner, as in the study of Kocieniewski and Lipniacki (2013) [ : 035006], which was focused on the roles of MEK isoforms in ERK activation. In this earlier study, model parameter values were tuned manually to obtain consistency with a combination of qualitative and quantitative data. This approach is not reproducible, nor does it provide insights into parametric or prediction uncertainties. Here, starting from the same data and the same ordinary differential equation (ODE) model structure, we generate formalized statements of qualitative observations, making these observations more reusable, and we improve the model parameterization procedure by applying a systematic and automated approach enabled by the software package PyBioNetFit. We also demonstrate uncertainty quantification (UQ), which was absent in the original study. Our results show that PyBioNetFit enables qualitative data to be leveraged, together with quantitative data, in parameterization of systems biology models and facilitates UQ. These capabilities are important for reliable estimation of model parameters and model analyses in studies of cellular regulatory systems and reproducibility.
在细胞调节系统研究中产生的数据往往是定性的。例如，在有突变和无突变情况下对信号读出的测量可能揭示不同条件下反应的排序，但不是突变诱导差异的精确程度。定性数据常常被数学建模者忽略，或者以一种[参考文献：035006，Kocieniewski和Lipniacki（2013）的研究]方式被考虑，该研究聚焦于MEK亚型在ERK激活中的作用。在这项早期研究中，手动调整模型参数值以获得与定性和定量数据组合的一致性。这种方法不可重复，也无法深入了解参数或预测的不确定性。在这里，从相同的数据和相同的常微分方程（ODE）模型结构出发，我们生成定性观察的形式化陈述，使这些观察更具可重用性，并通过应用软件包PyBioNetFit实现的系统自动化方法改进模型参数化过程。我们还展示了原始研究中缺乏的不确定性量化（UQ）。我们的结果表明，PyBioNetFit能够在系统生物学模型的参数化中利用定性数据和定量数据，并促进不确定性量化。这些能力对于细胞调节系统研究中模型参数的可靠估计、模型分析和可重复性很重要。
