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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology
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ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology

机译:ML-Space:细胞生物学中基于规则的多层次模型的混合空间吉莱斯比和粒子模拟

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摘要

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.
机译:可以在不同的细节级别上模拟细胞过程的时空动力学,从(确定性)偏微分方程通过空间随机模拟算法跟踪单个粒子的布朗轨迹。我们提出了一种基于多层规则的模型的空间仿真方法,其中包括动态分层嵌套的细胞格和实体。我们的方法ML-Space结合了离散的隔室动力学,离散空间中的随机空间方法以及在连续空间中运动的粒子。 ML-Space的基于规则的规范语言支持简洁而紧凑的模型描述,并轻松适应模型的空间分辨率。

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