Integrating Gene Expression and Protein Interaction Data for Signaling Pathway Prediction of Alzheimer’s Disease

• Published in: Computational and mathematical methods in medicine Vol. 2014; no. 2014; pp. 1 - 7
• Main Authors: Kong, Wei, Zhang, Jingmao, Mou, Xiaoyang, Yang, Yang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014; Hindawi Publishing Corporation
• Subjects: Algorithms; Alzheimer Disease - metabolism; Amyloid beta-Protein Precursor - metabolism; Computational Biology - methods; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Humans; Inflammation - metabolism; Linear Models; NF-kappa B - metabolism; Signal Transduction; Software
• ISSN: 1748-670X; 1748-6718
• DOI: 10.1155/2014/340758

Discovering the signaling pathway and regulatory network would provide significant advance in genome-wide understanding of pathogenesis of human diseases. Despite the rich transcriptome data, the limitation for microarray data is unable to detect changes beyond transcriptional level and insufficient in reconstructing pathways and regulatory networks. In our study, protein-protein interaction (PPI) data is introduced to add molecular biological information for predicting signaling pathway of Alzheimer’s disease (AD). Combining PPI with gene expression data, significant genes are selected by modified linear regression model firstly. Then, according to the biological researches that inflammation reaction plays an important role in the generation and deterioration of AD, NF-κB (nuclear factor-kappa B), as a significant inflammatory factor, has been selected as the beginning gene of the predicting signaling pathway. Based on that, integer linear programming (ILP) model is proposed to reconstruct the signaling pathway between NF-κB and AD virulence gene APP (amyloid precursor protein). The results identify 6 AD virulence genes included in the predicted inflammatory signaling pathway, and a large amount of molecular biological analysis shows the great understanding of the underlying biological process of AD.