Multi-scale modeling of gene regulatory networks via integration of temporal and topological biological data

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2012; pp. 1242 - 1245
• Main Authors: Dimitrakopoulos, G., Sgarbas, K., Dimitrakopoulou, K., Dragomir, A., Bezerianos, A., Maraziotis, I. A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Algorithms; Bioinformatics; Blood Cells - physiology; Cystic Fibrosis - blood; Cystic Fibrosis - genetics; Databases, Genetic; Educational institutions; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Humans; Immune system; Interferon-beta - genetics; Models, Genetic; Protein Interaction Maps; Proteins; Systems Biology - methods; Training
• ISBN: 1424441196; 9781424441198
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/EMBC.2012.6346162

Regulome is the dynamic network representation of the regulatory interplay among genes, proteins and other cellular components that control cellular processes. Reconstruction of gene regulatory networks (GRN) delineates one of the main objectives of Systems Biology towards understanding the organization of regulome. Significant progress has been reported the last years regarding GRN reconstruction methods, but the majority of them either consider information originating solely from gene expression data or/and are applied on a small fraction of the experimental dataset. In this paper, we will describe an integrative method, utilizing both temporal information arriving from time-series gene expression profiles, as well as topological properties of protein networks. The proposed methodology detects relations among either groups of genes or specific genes depending on the level of abstraction or resolution requested. Application on real data proved the ability of the method to extract relations in accordance with current biological knowledge as well as discriminate between different experimental conditions.