使用潜在连续变量的定性观测进行贝叶斯推断。
Bayesian inference using qualitative observations of underlying continuous variables.
机构信息
Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
出版信息
Bioinformatics. 2020 May 1;36(10):3177-3184. doi: 10.1093/bioinformatics/btaa084.
MOTIVATION
Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization.
RESULTS
We formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling.
AVAILABILITY AND IMPLEMENTATION
The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
最近的工作已经证明了使用非数值、定性数据来参数化数学模型的可行性。然而，由于缺乏对优化中使用的目标函数的统计解释，这种参数化模型的不确定性量化 (UQ) 仍然具有挑战性。
结果
我们制定了似然函数，这些函数适用于使用潜在连续变量的定性观测或定性和定量数据的组合来进行贝叶斯 UQ。为了展示由此产生的 UQ 能力，我们使用合成定性和定量数据集分析了发表的免疫球蛋白 E (IgE) 受体信号传导模型。值得注意的是，从定性数据得出的参数值估计值与从较低通量定量数据得出的估计值一样与假定的真实参数值一致。这些结果为在生物建模中利用定性数据提供了进一步的动力。
可用性和实现
这里提出的似然函数在 PyBioNetFit 的新版本中实现，这是一个用于分析系统生物学标记语言和 BioNetGen 语言格式模型的开源应用程序，可在线获得，网址为 www.github.com/lanl/PyBNF。
补充信息
补充数据可在生物信息学在线获得。
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