基于马尔可夫链蒙特卡罗算法的谷氨酸棒杆菌代谢通量分布分析
Markov Chain Monte Carlo Algorithm based metabolic flux distribution analysis on Corynebacterium glutamicum.
作者信息
Kadirkamanathan Visakan, Yang Jing, Billings Stephen A, Wright Phillip C
机构信息
Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK.
出版信息
Bioinformatics. 2006 Nov 1;22(21):2681-7. doi: 10.1093/bioinformatics/btl445. Epub 2006 Aug 29.
MOTIVATION
Metabolic flux analysis via a (13)C tracer experiment has been achieved using a Monte Carlo method with the assumption of system noise as Gaussian noise. However, an unbiased flux analysis requires the estimation of fluxes and metabolites jointly without the restriction on the assumption of Gaussian noise. The flux distributions under such a framework can be freely obtained with various system noise and uncertainty models.
RESULTS
In this paper, a stochastic generative model of the metabolic system is developed. Following this, the Markov Chain Monte Carlo (MCMC) approach is applied to flux distribution analysis. The disturbances and uncertainties in the system are simplified as truncated Gaussian multiplicative models. The performance in a real metabolic system is illustrated by the application to the central metabolism of Corynebacterium glutamicum. The flux distributions are illustrated and analyzed in order to understand the underlying flux activities in the system.
AVAILABILITY
Algorithms are available upon request.
动机
通过（13）C示踪实验进行代谢通量分析已使用蒙特卡罗方法实现，该方法假设系统噪声为高斯噪声。然而，无偏通量分析需要联合估计通量和代谢物，而不受高斯噪声假设的限制。在这样的框架下，可以通过各种系统噪声和不确定性模型自由获得通量分布。
结果
本文开发了一种代谢系统的随机生成模型。随后，将马尔可夫链蒙特卡罗（MCMC）方法应用于通量分布分析。系统中的干扰和不确定性被简化为截断高斯乘法模型。通过应用于谷氨酸棒杆菌的中心代谢来说明在实际代谢系统中的性能。为了理解系统中潜在的通量活动，对通量分布进行了说明和分析。
可用性
可根据要求提供算法。
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