Suppr超能文献

酵母细胞周期模型的随机Petri网扩展

Stochastic Petri Net extension of a yeast cell cycle model.

作者信息

Mura Ivan, Csikász-Nagy Attila

机构信息

The Microsoft Research-University of Trento, Centre for Computational and Systems Biology, Piazza Manci 17, Povo-Trento I-38100, Italy.

出版信息

J Theor Biol. 2008 Oct 21;254(4):850-60. doi: 10.1016/j.jtbi.2008.07.019. Epub 2008 Jul 24.

Abstract

This paper presents the definition, solution and validation of a stochastic model of the budding yeast cell cycle, based on Stochastic Petri Nets (SPN). A specific family of SPNs is selected for building a stochastic version of a well-established deterministic model. We describe the procedure followed in defining the SPN model from the deterministic ODE model, a procedure that can be largely automated. The validation of the SPN model is conducted with respect to both the results provided by the deterministic one and the experimental results available from literature. The SPN model catches the behavior of the wild type budding yeast cells and a variety of mutants. We show that the stochastic model matches some characteristics of budding yeast cells that cannot be found with the deterministic model. The SPN model fine-tunes the simulation results, enriching the breadth and the quality of its outcome.

摘要

本文基于随机Petri网(SPN),介绍了出芽酵母细胞周期随机模型的定义、求解和验证。选择了特定的SPN族来构建一个成熟的确定性模型的随机版本。我们描述了从确定性常微分方程模型定义SPN模型所遵循的过程,该过程在很大程度上可以自动化。SPN模型的验证是针对确定性模型提供的结果以及文献中可得的实验结果进行的。SPN模型捕捉了野生型出芽酵母细胞和各种突变体的行为。我们表明,随机模型匹配了确定性模型无法发现的出芽酵母细胞的一些特征。SPN模型对模拟结果进行了微调,丰富了其结果的广度和质量。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验