title: Structural systems identification of genetic regulatory networks.
abstract: BACKGROUND
  Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit.
  
  
  RESULTS
  In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints.
  
  
  BACKGROUND
  The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.
authors.affiliation: Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA. hxiong@cs.tamu.edu
authors.forename: Hao
authors.initials: H
authors.lastname: Xiong
authors.forename: Yoonsuck
authors.initials: Y
authors.lastname: Choe
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004260:DNA Repair / Q000235:genetics; D004926:Escherichia coli / Q000235:genetics*; D053263:Gene Regulatory Networks