基于Logistic回归模型的时变结构基因调控网络构建

Time-Varying Gene Regulatory Networks Construction Based on Logistic Regression

目前,由大多数基因调控网络的重构方法推导出的网络结构是静态的,即不随时间改变.但在细胞周期或一个有机体的不同生长阶段,调控网络的拓扑结构会发生显著变化.这为深入了解基因调控的时空机制带来困难.因此,文中提出一种基于时延互信息和核权重l1正则化Logistic回归模型学习时变结构基因调控网络的算法.将其应用于两种生物情景数据:黑腹果蝇在不同阶段的肌肉发育和酿酒酵母苯菌灵中毒后的反应.实验结果显示,该方法能反映不同细胞状态对基因间相互作用的影响,有效获取基因调控网络随时间变化的动态效应.

At present, network structures derived from most gene regulatory network reconstruction methods are static, which do not change with time. However, in the cell cycle or different growth stages of an organismat, the topology of regulatory network changes significantly, which makes it difficult to understand the spatial-temporal mechanism of gene regulation. Therefore, an algorithm for the network is proposed based on time lagged Mutual Information （TLMI） and a kernel-reweighted l^-regularized logistic regression model. Two biological scenarios, the developmental stages of Drosophila melanogaster and the response of Saccharomyces cerevisiae to benomyl poisoning, are analyzed. The experimental results show that the proposed method reflects the impact of different cell states of interaction between genes and effectively acquires the dynamic effect of gene regulatory networks changing with time.