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

Yongsoo Kim, Taek Kyun Kim, Yungu Kim, Jiho Yoo, Sungyong You, Inyoul Lee, George Carlson, Leroy Hood, Seungjin Choi, Daehee Hwang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Motivation: 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.

Original languageEnglish
Article numberbtq670
Pages (from-to)391-398
Number of pages8
JournalBioinformatics
Volume27
Issue number3
DOIs
Publication statusPublished - 1 Feb 2011

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