Statistical Applications in Genetics and Molecular Biology A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
Statistical Applications in Genetics and Molecular Biology A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
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遗传学和分子生物学中的统计应用具有顺序耦合交互参数的非齐次动态贝叶斯网络，用于系统和合成生物学中的应用
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发表时间：
2012
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通讯作者：
D. Husmeier
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作者：
M. Grzegorczyk;D. Husmeier
系统生物学中的一个重要且具有挑战性的问题是从短的非平稳时间序列的转录谱推断基因调控网络。一个流行的方法，已被广泛应用于这一目的是基于动态贝叶斯网络（DBN），虽然传统的同质DBN无法建模的非平稳性和随时间变化的性质的基因调控过程。因此，许多作者最近提出了结合DBN与多个变点过程，以获得时变动态贝叶斯网络（TV-DBN）。然而，TV-DBN并非没有问题。基因表达时间序列通常很短，这使得模型过于灵活，导致过度拟合或夸大的推理不确定性。在本文中，我们介绍了贝叶斯正则化方案，解决了这个困难。我们的方法是基于这样的基本原理，即在生物体的生命周期中或响应于不断变化的环境，基因调控过程中的变化逐渐出现，并且我们已经将这一概念整合到TV-DBN参数的先验分布中。我们已经在合成数据上广泛地测试了我们的正则化TV-DBN模型，在该模型中，我们模拟了从一个逐渐变化的系统产生的短的非均匀时间序列。然后，我们已经将我们的方法应用于真实世界的基因表达时间序列，在果蝇的生命周期中测量，在人工产生的恒定光照条件下，在拟南芥，并从一个综合设计的酿酒酵母菌株暴露于不断变化的环境。
An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.