Gonçalves Inês G, Hormuth David A, Prabhakaran Sandhya, Phillips Caleb M, García-Aznar José Manuel
Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Spain.
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, USA.
GigaByte. 2023 Feb 28;2023:gigabyte77. doi: 10.46471/gigabyte.77. eCollection 2023.
models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present , an open-source Python library tailored to create standardized calibration and optimization routines for models.
生物系统模型通常非常复杂，依赖大量描述物理和生物学特性的参数，而这些参数需要验证。因此，参数空间探索是计算模型开发的重要组成部分，以充分表征和验证模拟结果。实验数据也可用于约束参数空间（或进行模型校准），以增强模型参数的生物学相关性。数学生物学界广泛使用的一个计算平台是 ，它为不同时间和空间尺度的生物现象基于代理的模型提供了一种标准化方法。尽管如此， 的一个局限性是缺乏一种无需高性能计算访问即可运行的参数空间探索和校准的通用方法。在这里，我们展示了 ，这是一个开源的Python库，专门为 模型创建标准化的校准和优化例程。
