title: Coarse-grained reverse engineering of genetic regulatory networks.
abstract: We have modeled genetic regulatory networks in the framework of continuous-time recurrent neural networks. A method for determining the parameters of such networks, given expression level time series data, is introduced and evaluated using artificial data. The method is also applied to a set of actual expression data from the development of rat central nervous system.
authors.affiliation: Nordita, Blegdamsvej 17, 2100, Copenhagen, Denmark. mwahde@me.chalmers.se
authors.forename: M
authors.initials: M
authors.lastname: Wahde
authors.forename: J
authors.initials: J
authors.lastname: Hertz
mesh_terms: D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000254:growth & development / Q000378:metabolism; D015870:Gene Expression; D016571:Neural Networks, Computer; D051381:Rats