title: Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles.
abstract: Reverse engineering of biomolecular regulatory networks such as gene regulatory networks, protein interaction networks, and metabolic networks has received an increasing attention as more high-throughput time-series measurements become available. In spite of various approaches developed from this motivation, it still remains as a challenging subject to develop a new reverse engineering scheme that can effectively uncover the functional interaction structure of a biomolecular network from given time-series expression profiles (TSEPs). We propose a new reverse engineering scheme that makes use of phase portraits constructed by projection of every two TSEPs into respective phase planes. We introduce two measures of a slope index (SI) and a winding index (WI) to quantify the interaction properties embedded in the phase portrait. Based on the SI and WI, we can reconstruct the functional interaction network in a very efficient and systematic way with better inference results compared to previous approaches. By using the SI, we can also estimate the time-lag accompanied with the interaction between molecular components of a network.
authors.affiliation: College of Medicine, Seoul National University, Jongno-gu, Republic of Korea. ckh-sb@snu.ac.kr
authors.forename: Kwang-Hyun
authors.initials: KH
authors.lastname: Cho
authors.forename: Jeong-Rae
authors.initials: JR
authors.lastname: Kim
authors.forename: Songjoon
authors.initials: S
authors.lastname: Baek
authors.forename: Hyung-Seok
authors.initials: HS
authors.lastname: Choi
authors.forename: Sang-Mok
authors.initials: SM
authors.lastname: Choo
mesh_terms: D000818:Animals; D004023:Dictyostelium / Q000235:genetics / Q000378:metabolism; D004926:Escherichia coli / Q000235:genetics; D020869:Gene Expression Profiling; D018384:Oxidative Stress