title: Toward a complete in silico, multi-layered embryonic stem cell regulatory network.
abstract: Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.
authors.affiliation: Department of Gene and Cell Medicine and The Black Family Stem Cell Institute, Mount Sinai School of Medicine, New York, NY 10029, USA.
authors.forename: Huilei
authors.initials: H
authors.lastname: Xu
authors.forename: Christoph
authors.initials: C
authors.lastname: Schaniel
authors.forename: Ihor R
authors.initials: IR
authors.lastname: Lemischka
authors.forename: Avi
authors.initials: A
authors.lastname: Ma'ayan
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D053595:Embryonic Stem Cells / Q000378:metabolism*; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism; D010766:Phosphorylation; D034622:RNA Interference; D012333:RNA, Messenger / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger