Parameter Identifiability in PDE Models of Fluorescence Recovery After Photobleaching
Parameter Identifiability in PDE Models of Fluorescence Recovery After Photobleaching
复制标题
DOI：
10.1007/s11538-024-01266-4
复制
发表时间：
2024-04-01
影响因子：
3.5
通讯作者：
Sandstede，Bjorn
中科院分区：
文献类型：
--
作者：
Ciocanel，Maria-Veronica;Ding，Lee;Sandstede，Bjorn
识别描述生物数据的数学模型的唯一参数可能具有挑战性，并且通常是不可能的。细胞生物学中的偏微分方程模型的参数可识别性是特别困难的，因为许多已建立的蛋白质动力学的体内测量平均出空间维度。在这里，我们的动机是最近的实验中的RNA结合蛋白PTBP3在青蛙卵母细胞的RNP颗粒的结合动力学的基础上荧光恢复后光漂白（FRAP）测量。FRAP是用于探测活细胞中蛋白质动力学的广泛使用的实验技术，并且通常使用蛋白质动力学的简单反应扩散模型来建模。我们发现，目前的结构和实际参数可识别性的方法提供了有限的见解，这些PDE模型和空间平均FRAP数据的动力学参数的可识别性。因此，我们提出了一个管道，用于评估参数的可识别性和学习参数组合的基础上重新参数化和配置文件的似然分析。我们表明，这种方法是能够恢复合成FRAP数据集的参数组合，并探讨其应用到真实的实验数据。
Identifying unique parameters for mathematical models describing biological data can be challenging and often impossible. Parameter identifiability for partial differential equations models in cell biology is especially difficult given that many established in vivo measurements of protein dynamics average out the spatial dimensions. Here, we are motivated by recent experiments on the binding dynamics of the RNA-binding protein PTBP3 in RNP granules of frog oocytes based on fluorescence recovery after photobleaching (FRAP) measurements. FRAP is a widely-used experimental technique for probing protein dynamics in living cells, and is often modeled using simple reaction-diffusion models of the protein dynamics. We show that current methods of structural and practical parameter identifiability provide limited insights into identifiability of kinetic parameters for these PDE models and spatially-averaged FRAP data. We thus propose a pipeline for assessing parameter identifiability and for learning parameter combinations based on re-parametrization and profile likelihoods analysis. We show that this method is able to recover parameter combinations for synthetic FRAP datasets and investigate its application to real experimental data.