Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland; Swiss Institute of Bioinformatics (SIB), Mattenstrasse 26, Basel 4058, Switzerland.
J Theor Biol. 2019 Nov 21;481:233-248. doi: 10.1016/j.jtbi.2018.12.002. Epub 2018 Dec 7.
Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is typically noisy and non-quantitative. In addition, these models can have a high computational cost, limiting the number of feasible simulations, and therefore rendering most traditional parameter estimation methods unsuitable. In this paper, we present a pipeline that uses Gaussian process learning to estimate biological parameters from noisy, non-quantitative image data when the model has a high computational cost. This approach is first successfully tested on a parametric function with the goal of retrieving the original parameters. We then apply it to estimating parameters in a biological setting by fitting artificial in-situ hybridization (ISH) data of the developing murine limb bud. We expect that this method will be of use in a variety of modeling scenarios where quantitative data is missing and the use of standard parameter estimation approaches in biological modeling is prohibited by the computational cost of the model.
参数估计是生物过程计算建模的主要挑战。在基于图像的建模中尤其如此，因为模型的固有定量输出是根据图像数据进行测量的，而图像数据通常是嘈杂的且是非定量的。此外，这些模型的计算成本可能很高，这限制了可行模拟的数量，从而使大多数传统的参数估计方法变得不合适。在本文中，我们提出了一种使用高斯过程学习从具有高计算成本的嘈杂、非定量图像数据中估计生物参数的流水线。该方法首先成功地对具有检索原始参数目标的参数函数进行了测试。然后，我们通过拟合发育中的鼠肢芽的人工原位杂交 (ISH) 数据将其应用于生物环境中的参数估计。我们期望该方法将在各种建模场景中得到应用，在这些场景中，定量数据缺失，并且由于模型的计算成本，生物建模中标准的参数估计方法是不允许的。
