Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection
Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection
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DOI：
10.1093/bioinformatics/bti226
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发表时间：
2005-04-15
期刊：
影响因子：
5.8
通讯作者：
Töpfer, S
中科院分区：
文献类型：
--
作者：
Guthke, R;Möller, U;Töpfer, S
动机：对细菌感染的免疫反应是一个复杂的动态基因和蛋白质相互作用的网络。我们提出了一种优化的逆向工程策略，旨在重建这类交互网络。提出的方法是基于微阵列数据和现有的生物学知识。结果：通过基因表达谱（时间序列）的模糊聚类确定了免疫反应的主要动力学。采用各种评价标准对聚类数量进行优化。根据现有的生理知识，为每个聚类选择一个具有高模糊隶属度的代表性基因。然后，通过寻找常微分方程组来确定假设的网络结构，其模拟动力学可以拟合集群代表基因的基因表达谱。对于假设网络结构的构建，本文比较了基于奇异值分解（SVD）的方法和新引入的启发式网络生成方法。结果表明，该方法可以找到更稀疏的网络，并能更好地拟合实验数据。
Motivation: The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge.Results: The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data.