Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA.
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
Bull Math Biol. 2019 Jun;81(6):1853-1866. doi: 10.1007/s11538-019-00590-4. Epub 2019 Mar 4.
Data-driven model validation across dimensions in mathematical and computational biology assumptions are often made (e.g., symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purpose of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.
在数理生物学和计算生物学中，通常会做出一些数据驱动的模型验证假设（例如对称性），以便将问题从三维空间（3D）简化到二维（2D）。然而，一些实验数据集，例如通过流式细胞术获得的细胞计数，代表整个三维生物对象。出于模型校准和验证的目的，有时需要将这些生物数据集与模型输出进行比较。我们提出了一种将 2D 模型输出缩放以与 3D 实验数据集进行比较的方法，并讨论了该方法在两个示例中的应用：肉芽肿形成和骨骼肌组织的基于代理的模型。该方法的准确性在人为生成的场景中进行了评估。
