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GNU MCSim:用于SBML编码的系统生物学模型的贝叶斯统计推断

GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models.

作者信息

Bois Frédéric Y

机构信息

Direction des Risques Chroniques, INERIS, Parc ALATA, BP2, F-60550, Verneuil en Halatte, France.

出版信息

Bioinformatics. 2009 Jun 1;25(11):1453-4. doi: 10.1093/bioinformatics/btp162. Epub 2009 Mar 20.

Abstract

SUMMARY

Statistical inference about the parameter values of complex models, such as the ones routinely developed in systems biology, is efficiently performed through Bayesian numerical techniques. In that framework, prior information and multiple levels of uncertainty can be seamlessly integrated. GNU MCSim was precisely developed to achieve those aims, in a general non-linear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates Systems Biology Markup Language models. Markov chain Monte Carlo simulations can be used to generate samples from the joint posterior distribution of the model parameters, given a dataset and prior distributions. Hierarchical statistical models can be used. Optimal design of experiments can also be investigated.

AVAILABILITY AND IMPLEMENTATION

The GNU GPL source is available at (http://savannah.gnu.org/projects/mcsim). A distribution package is at (http://www.gnu.org/software/mcsim). GNU MCSim is written in standard C and runs on any platform supporting a C compiler. Supplementary Material is available online at (http://www.gnu.org/software/mcsim).

摘要

摘要

通过贝叶斯数值技术可以有效地对复杂模型(如系统生物学中常规开发的模型)的参数值进行统计推断。在此框架下,先验信息和多个层次的不确定性可以无缝整合。GNU MCSim正是为在一般非线性微分环境中实现这些目标而开发的。从5.3.0版本开始,GNU MCSim可以读取并模拟系统生物学标记语言模型。给定数据集和先验分布,马尔可夫链蒙特卡罗模拟可用于从模型参数的联合后验分布中生成样本。可以使用分层统计模型。还可以研究实验的最优设计。

可用性与实现

GNU GPL源代码可在(http://savannah.gnu.org/projects/mcsim)获取。发行包可在(http://www.gnu.org/software/mcsim)获取。GNU MCSim用标准C编写,可在任何支持C编译器的平台上运行。补充材料可在网上(http://www.gnu.org/software/mcsim)获取。

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