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Inferring biomolecular regulatory networks from phase portraits of time‐series expression profiles
Author(s) -
Cho Kwang-Hyun,
Kim Jeong-Rae,
Baek Songjoon,
Choi Hyung-Seok,
Choo Sang-Mok
Publication year - 2006
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/j.febslet.2006.05.035
Subject(s) - inference , computer science , series (stratigraphy) , phase portrait , reverse engineering , gene regulatory network , expression (computer science) , index (typography) , scheme (mathematics) , projection (relational algebra) , data mining , artificial intelligence , algorithm , mathematics , biology , gene expression , gene , physics , genetics , mathematical analysis , nonlinear system , quantum mechanics , world wide web , bifurcation , programming language , paleontology
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.