Topology of gene expression networks as revealed by data mining and modeling
Topology of gene expression networks as revealed by data mining and modeling
复制标题
DOI：
10.1093/bioinformatics/btg333
复制
发表时间：
2003-10-12
期刊：
影响因子：
5.8
通讯作者：
Fuchs, R
中科院分区：
文献类型：
--
作者：
Lukashin, AV;Lukashev, ME;Fuchs, R
动机：高通量基因表达谱的解释需要一个知识的设计原则的网络，维持细胞机器。最近提出了一种基于网络拓扑研究的新方法。这种方法已被证明是有用的分析各种生物系统，包括代谢网络，蛋白质-蛋白质相互作用的网络，和基因网络，可以从基因表达数据。在本论文中，我们专注于几个重要的问题，尚未得到充分study.Results的基因表达网络的拓扑结构相关的：来自基因表达谱的时间序列实验在酵母和扰动实验在细胞系的网络进行了研究。我们证明，独立于实验生物体（酵母细胞系）和实验类型（时间进程与扰动），提取的网络具有相似的拓扑特征，这表明与其他共同原则的生物网络的结构组织的结果。提出了一种新的网络增长的计算模型，再现所观察到的网络的基本设计原则。该模型的优点是，它提供了一个通用的机制，通过几个参数的变化，生成不同类型的拓扑结构的网络。我们调查的鲁棒性的网络结构的随机损害和故意删除系统的最重要的部分，并显示出令人惊讶的容忍基因表达网络的两种干扰。
Motivation: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data. In the present paper, we focus on several important issues related to the topology of gene expression networks that have not yet been fully studied.Results: The networks derived from gene expression profiles for both time series experiments in yeast and perturbation experiments in cell lines are studied. We demonstrate that independent from the experimental organism (yeast versus cell lines) and the type of experiment (time courses versus perturbations) the extracted networks have similar topological characteristics suggesting together with the results of other common principles of the structural organization of biological networks. A novel computational model of network growth that reproduces the basic design principles of the observed networks is presented. Advantage of the model is that it provides a general mechanism to generate networks with different types of topology by a variation of a few parameters. We investigate the robustness of the network structure to random damages and to deliberate removal of the most important parts of the system and show a surprising tolerance of gene expression networks to both kinds of disturbance.