Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

TitlePrincipal network analysis: identification of subnetworks representing major dynamics using gene expression data
Publication TypeJournal Article
Year of Publication2011
AuthorsKim Y, Kim TK, Yoo J, You S, Lee I, Carlson G, Hood L, Choi S, Hwang D
JournalBioinformatics
Volume27
Pagination391-8
Date PublishedFeb 1
PubMed Central IDPMC3031040
PMID21193522
AbstractMOTIVATION: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. RESULTS: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. AVAILABILITY: The web-based software is available at: http://sbm.postech.ac.kr/pna. CONTACT: dhhwang@postech.ac.kr; seungjin@postech.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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