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基于群体蒙特卡罗方法的酶控制过程系统辨识

System Identification of Enzymatic Control Processes Using Population Monte Carlo Methods
课程网址: http://videolectures.net/pesb07_calderhead_sio/  
主讲教师: Ben Calderhead
开课单位: 伦敦大学学院
开课时间: 2007-04-04
课程语种: 英语
中文简介:
我们证明了群体蒙特卡罗技术优于标准的Metropolis Markov Chain Monte Carlo（MCMC）方法，用于在给定噪声实验数据的情况下推断生物过程的特定机械模型的最佳参数。随着我们对生物过程的理解的增加，用于描述它们的拟议模型变得更加复杂。利用这种潜在的大量方程和参数，手工挑选参数值并确保选择最合适的值已不再可行。蒙特卡罗方法正在被越来越广泛地用于估计参数值，但是我们表明标准的Metropolis MCMC方法甚至不能收敛于最简单模型的最优值，而且是非复杂方法，非马尔可夫种群蒙特卡罗，可以成功地用于产生一致和准确的结果。我们用拟南芥中昼夜节律遗传网络的最小模型说明了基本问题，该模型由3个连接的微分方程组成，其中包含总共6个参数，并附加了一个额外的噪声参数来估计数据中噪声的方差。与Mark Girolami共同合作。
课程简介: We demonstrate the superiority of Population Monte Carlo techniques over standard Metropolis Markov Chain Monte Carlo (MCMC) methods for inferring optimal parameters for a particular mechanistic model of a biological process given noisy experimental data. As our understanding of biological processes increases, the proposed models to describe them become more complex. With such potentially large numbers of equations and parameters, it is no longer feasible to hand-pick parameter values and be sure that the most appropriate values have been chosen. Monte Carlo methods are becoming more widely used for estimating parameter values, however we show that the standard Metropolis MCMC approach fails to converge on optimal values for even relatively simple models and that a more sophisticated method, in the form of non-Markovian Population Monte Carlo, may be successfully employed to produce consistent and accurate results. We illustrate the basic problem using the minimal model for the circadian genetic network in Arabidopsis thaliana, which consists of 3 linked differential equations containing a total of 6 parameters, with an additional noise parameter incorporated to estimate the variance of noise in the data. Joint work with Mark Girolami.
关 键 词: 群体蒙特卡罗技术; 噪声实验; 机械模型
课程来源: 视频讲座网
最后编审: 2019-09-13：lxf
阅读次数: 74

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