Statistical inference of regulatory networks for circadian regulation
Statistical inference of regulatory networks for circadian regulation
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DOI：
10.1515/sagmb-2013-0051
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发表时间：
2014-06-01
影响因子：
0.9
通讯作者：
Grzegorczyk, Marco
中科院分区：
文献类型：
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作者：
Aderhold, Andrej;Husmeier, Dirk;Grzegorczyk, Marco
我们评估了各种最先进的统计和机器学习方法的准确性，以重建昼夜节律背景下的基因和蛋白质调控网络。我们的研究利用了拟南芥中关键生物钟成分的基因表达和蛋白质浓度时间序列的日益增加的可用性。此外，基因表达和蛋白质浓度时间序列是从最近发表的昼夜节律时钟调节网络模拟的，其中蛋白质和基因的相互作用由基于Michaelis-Menten动力学的马尔可夫跳跃过程描述。我们密切关注最近的实验方案，包括将幼苗携带到不同的光暗周期和敲除各种关键调控基因。我们的研究为关键的比较性能评估提供了相对网络重建精度分数，并揭示了一系列高度相关的问题：它量化了与未知蛋白质浓度和mRNA转录速率相关的系统缺失值的影响，它调查了性能对网络拓扑和重复性程度的依赖，它提供了更深入的了解非线性方法何时以及为什么未能优于线性方法，它为不同推理过程中的参数设置提供了改进的指导方针，并对拟南芥的中央昼夜节律基因调控网络结构提出了新的假设。
We assess the accuracy of various state-of-the-art statistics and machine learning methods for reconstructing gene and protein regulatory networks in the context of circadian regulation. Our study draws on the increasing availability of gene expression and protein concentration time series for key circadian clock components in Arabidopsis thaliana. In addition, gene expression and protein concentration time series are simulated from a recently published regulatory network of the circadian clock in A. thaliana, in which protein and gene interactions are described by a Markov jump process based on Michaelis-Menten kinetics. We closely follow recent experimental protocols, including the entrainment of seedlings to different light-dark cycles and the knock-out of various key regulatory genes. Our study provides relative network reconstruction accuracy scores for a critical comparative performance evaluation, and sheds light on a series of highly relevant questions: it quantifies the influence of systematically missing values related to unknown protein concentrations and mRNA transcription rates, it investigates the dependence of the performance on the network topology and the degree of recurrency, it provides deeper insight into when and why non-linear methods fail to outperform linear ones, it offers improved guidelines on parameter settings in different inference procedures, and it suggests new hypotheses about the structure of the central circadian gene regulatory network in A. thaliana.