通过基于系统生物学标记语言（SBML）的完全集成格式实现大规模单细胞模型模拟的计算加速。
Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format.
作者信息
Mutsuddy Arnab, Erdem Cemal, Huggins Jonah R, Salim Misha, Cook Daniel, Hobbs Nicole, Feltus F Alex, Birtwistle Marc R
机构信息
Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
School of Computing, Clemson University, Clemson, SC, USA.
出版信息
Bioinform Adv. 2023 Mar 23;3(1):vbad039. doi: 10.1093/bioadv/vbad039. eCollection 2023.
SUMMARY
Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks.
AVAILABILITY AND IMPLEMENTATION
Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).
摘要
大规模和全细胞建模面临多重挑战，包括可扩展的模型构建以及模块通信瓶颈（例如在代谢、基因表达、信号传导等之间）。我们之前开发了一种用于增殖和死亡信号动力学大规模机制模型的开源、可扩展格式，但基因表达和蛋白质生物化学模块之间的通信瓶颈仍然存在。在此，我们开发了两种解决通信瓶颈的方法，对于混合随机 - 确定性模拟，可将模拟速度提高约4倍，对于完全确定性模拟，可提高100倍以上。完全确定性的加速有助于模型初始化、参数估计和敏感性分析任务。
可用性和实现方式
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