Noman Nasimul, Iba Hitoshi
Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan.
Genome Inform. 2005;16(2):205-14.
This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene expression using decoupled S-system formalism. We employed Trigonometric Differential Evolution (TDE) as the optimization engine of our algorithm for capturing the dynamics in gene expression data. A more effective fitness function for attaining the sparse structure, which is the hallmark of biological networks, has been applied. Experiments on artificial genetic network show the power of the algorithm in constructing the network structure and predicting the regulatory parameters. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data.
本文提出了一种改进的进化方法，用于使用解耦S-系统形式从基因表达的时间序列数据构建基础网络结构并推断有效的动力学参数。我们采用三角微分进化（TDE）作为算法的优化引擎，以捕捉基因表达数据中的动态变化。应用了一种更有效的适应度函数来获得稀疏结构，这是生物网络的标志。在人工遗传网络上的实验表明了该算法在构建网络结构和预测调控参数方面的能力。该方法用于利用基因表达数据评估大肠杆菌SOS信号通路中基因之间的相互作用。
