Sverchkov Yuriy, Ho Yi-Hsuan, Gasch Audrey, Craven Mark
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA,
Department of Genetics, University of Wisconsin-Madison, Madison, WI, USA.
Res Comput Mol Biol. 2018 Apr;10812:194-210. doi: 10.1007/978-3-319-89929-9_13. Epub 2018 Apr 18.
Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common approach to uncovering biological networks involves performing perturbations on elements of the network, such as gene knockdown experiments, and measuring how the perturbation affects some reporter of the process under study. In this paper, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effect models (NEMs). The main contribution of this work is that CSNEMs explicitly model the participation of a gene in multiple , meaning that a gene can appear in multiple places in the network. Biologically, the representation of regulators in multiple contexts may indicate that these regulators have distinct roles in different cellular compartments or cell cycle phases. We present an evaluation of the method on simulated data as well as on data from a study of the sodium chloride stress response in .
系统生物学的进展已明确了网络模型对于获取基因调控、新陈代谢及细胞信号传导中复杂关系知识的重要性。揭示生物网络的一种常用方法是对网络元件进行扰动，比如基因敲除实验，并测量这种扰动如何影响所研究过程的某些报告指标。在本文中，我们开发了上下文特定嵌套效应模型（CSNEMs），这是一种用于推断此类网络的方法，它推广了嵌套效应模型（NEMs）。这项工作的主要贡献在于，CSNEMs明确地对基因在多个过程中的参与进行建模，这意味着一个基因可以在网络中的多个位置出现。从生物学角度来看，调控因子在多个上下文中的表现可能表明这些调控因子在不同的细胞区室或细胞周期阶段具有不同的作用。我们展示了该方法在模拟数据以及来自一项关于[具体物种]中氯化钠应激反应研究的数据上的评估。
