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

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

SUBMITTER: Kim Y 

PROVIDER: S-EPMC3031040 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature