Department of Information Technology, Uppsala University, Uppsala, Sweden.
BMC Bioinformatics. 2022 Jan 31;23(1):55. doi: 10.1186/s12859-022-04575-4.
Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments' results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case.
We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS' focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases.
CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.
基于细胞的模型在发育生物学中的应用越来越受欢迎。然而，这些模型的模拟精度和效率受数值选择的影响，这一影响很少被细致地测试。如果没有具体的研究来区分实体模型结论和数值伪影，模型构建者就有可能被他们实验结果所误导。大多数基于细胞的建模框架提供了功能丰富的环境，提供了广泛的生物组件，但不太适合数值研究。因此，需要专门针对这种用例的软件。
我们提出了 CBMOS，这是一个用于基于中心或细胞中心模型模拟的 Python 框架。与其他实现不同，CBMOS 的重点是通过提供对多个常微分方程求解器和力函数的访问，通过灵活、用户友好的界面，并通过 GPU 加速实现快速测试，来促进基于中心模型的数值研究。我们通过说明两个常见工作流程来展示其潜力：（1）在 Jupyter 笔记本中比较两个求解器的数值特性，（2）在高性能计算集群上测量两个求解器的平均壁时间。更具体地说，我们确认，虽然对于中等精度水平，后向 Euler 方法允许比常用的前向 Euler 方法更大的时间步长，但由于它是隐式方法，其额外的计算成本禁止将其用于实际测试案例。
CBMOS 是基于中心模型的灵活、易用的 Python 实现，向用户公开了基本模型假设和数值组件。它在 GitHub 和 PyPI 上以 MIT 许可证提供。CBMOS 通过使用 NumPy 在中央处理器上快速进行原型设计，对于小型系统可以在几秒钟内模拟多达 10000 个细胞的细胞群体。因此，它将大大降低任何模型构建者检查模型结论是否独立于数值问题这一关键假设的时间投入。
