title: Reverse engineering of gene regulatory networks.
abstract: Systems biology is a multi-disciplinary approach to the study of the interactions of various cellular mechanisms and cellular components. Owing to the development of new technologies that simultaneously measure the expression of genetic information, systems biological studies involving gene interactions are increasingly prominent. In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways. Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments as typical tools for measuring levels of messenger ribonucleic acid (mRNA) expression. In particular, the inference methods are classified according to the required input information, and the main idea of each method is elucidated by comparing its advantages and disadvantages with respect to the other methods. In addition, recent developments in this field are introduced and discussions on the challenges and opportunities for future research are provided.
authors.affiliation: College of Medicine, Seoul National University, Jongnogu, Seoul 110-799, South Korea. ckh-sb@snu.ac.kr
authors.forename: K H
authors.initials: KH
authors.lastname: Cho
authors.forename: S M
authors.initials: SM
authors.lastname: Choo
authors.forename: S H
authors.initials: SH
authors.lastname: Jung
authors.forename: J R
authors.initials: JR
authors.lastname: Kim
authors.forename: H S
authors.initials: HS
authors.lastname: Choi
authors.forename: J
authors.initials: J
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome