GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models
GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models
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DOI：
10.1093/bioinformatics/btp162
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发表时间：
2009-06-01
期刊：
影响因子：
5.8
通讯作者：
Bois, Frederic Y.
中科院分区：
文献类型：
--
作者：
Bois, Frederic Y.
关于复杂模型的参数值的统计推断，例如系统生物学中经常开发的模型，可以通过贝叶斯数值技术有效地执行。在这个框架中，先验信息和多层次的不确定性可以无缝集成。GNU MCSim正是为了在一般的非线性微分环境中实现这些目标而开发的。从版本5.3.0开始，GNU MCSim读取并模拟系统生物学标记语言模型。马尔可夫链蒙特卡罗模拟可以用来从模型参数的联合后验分布中生成样本，给定一个数据集和先验分布。可以使用分层统计模型。实验的优化设计也可以进行研究。
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.