Context-dependent transcriptional regulations between signal transduction pathways

• Published in: BMC bioinformatics Vol. 12; no. 1; p. 19
• Main Authors: Hwang, Sohyun, Kim, Sangwoo, Shin, Heesung, Lee, Doheon
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.01.2011; BioMed Central Ltd; BMC
• Subjects: Algorithms; Apoptosis; Bioinformatics; Biomedical and Life Sciences; Cellular signal transduction; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Dendritic Cells - metabolism; Gene expression; Gene Expression Regulation; Gene Regulatory Networks; Genetic regulation; Genetic transcription; Humans; Immune system; Immunology; Life Sciences; Methods; Microarrays; Networks analysis; Proteins; Research Article; Signal Transduction; Transcription, Genetic; Expression Score; Start Node; Cellular Context; Dendritic Cell Maturation; KEGG Pathway
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-12-19

Background Cells coordinate their metabolism, proliferation, and cellular communication according to environmental cues through signal transduction. Because signal transduction has a primary role in cellular processes, many experimental techniques and approaches have emerged to discover the molecular components and dynamics that are dependent on cellular contexts. However, omics approaches based on genome-wide expression analysis data comparing one differing condition (e.g. complex disease patients and normal subjects) did not investigate the dynamics and inter-pathway cross-communication that are dependent on cellular contexts. Therefore, we introduce a new computational omics approach for discovering signal transduction pathways regulated by transcription and transcriptional regulations between pathways in signaling networks that are dependent on cellular contexts, especially focusing on a transcription-mediated mechanism of inter-pathway cross-communication. Results Applied to dendritic cells treated with lipopolysaccharide, our analysis well depicted how dendritic cells respond to the treatment through transcriptional regulations between signal transduction pathways in dendritic cell maturation and T cell activation. Conclusions Our new approach helps to understand the underlying biological phenomenon of expression data (e.g. complex diseases such as cancer) by providing a graphical network which shows transcriptional regulations between signal transduction pathways. The software programs are available upon request.