Department of Distributed and Dependable Systems, Charles University, Prague, Czech Republic.
Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
BMC Bioinformatics. 2024 May 24;25(1):199. doi: 10.1186/s12859-024-05815-5.
Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial.
We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.
These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.
随着实验技术的进步，能够查询导致感兴趣表型的机制细节的系统生物学计算模型变得越来越重要。特别是，布尔模型非常适合描述信号网络的复杂性，同时又足够简单，可以扩展到非常多的组件。随着布尔模型推理技术的进步，该领域正在从构建中等规模模型的手工艺方式转变为更自动化的方式，从而导致非常大的模型。在这种情况下，适应模拟软件以应对这种复杂性的增加至关重要。
我们在连续时间布尔模拟器中提出了两个新的发展：MaBoSS.MPI，这是 MaBoSS 的并行实现，可以利用大型 CPU 集群的计算能力，以及 MaBoSS.GPU，可以使用 GPU 加速器来执行这些模拟。
这些实现使非常大的模型的行为模拟和探索成为可能，因此成为系统生物学社区的有价值的分析工具。
