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.
Description
Inferring biomolecular regulatory networks from ph... [FEBS Lett. 2006] - PubMed result
%0 Journal Article
%1 Cho:2006:FEBS-Lett:16730002
%A Cho, K H
%A Kim, J R
%A Baek, S
%A Choi, H S
%A Choo, S M
%D 2006
%J FEBS Lett
%K correlation expression network
%N 14
%P 3511-3518
%R 10.1016/j.febslet.2006.05.035
%T Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles
%U http://www.ncbi.nlm.nih.gov/pubmed/16730002?dopt=AbstractPlus&holding=f1000,f1000m,isrctn
%V 580
%X 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.
@article{Cho:2006:FEBS-Lett:16730002,
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.},
added-at = {2009-11-19T22:13:30.000+0100},
author = {Cho, K H and Kim, J R and Baek, S and Choi, H S and Choo, S M},
biburl = {https://www.bibsonomy.org/bibtex/2b57e1bb785993b1132b68b69894c0faa/cchen63},
description = {Inferring biomolecular regulatory networks from ph... [FEBS Lett. 2006] - PubMed result},
doi = {10.1016/j.febslet.2006.05.035},
interhash = {2d60fb1a5b426ec56567d5949ff6cd9d},
intrahash = {b57e1bb785993b1132b68b69894c0faa},
journal = {FEBS Lett},
keywords = {correlation expression network},
month = Jun,
number = 14,
pages = {3511-3518},
pmid = {16730002},
timestamp = {2009-11-19T22:13:30.000+0100},
title = {Inferring biomolecular regulatory networks from phase portraits of time-series expression profiles},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16730002?dopt=AbstractPlus&holding=f1000,f1000m,isrctn},
volume = 580,
year = 2006
}