title: Investigating noise tolerance in an efficient engine for inferring biological regulatory networks.
abstract: Biological systems are composed of biomolecules such as genes, proteins, metabolites, and signaling components, which interact in complex networks. To understand complex biological systems, it is important to be capable of inferring regulatory networks from experimental time series data. In previous studies, we developed efficient numerical optimization methods for inferring these networks, but we have yet to test the performance of our methods when considering the error (noise) that is inherent in experimental data. In this study, we investigated the noise tolerance of our proposed inferring engine. We prepared the noise data using the Langevin equation, and compared the performance of our method with that of alternative optimization methods.
authors: Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Fukuoka 8128582, Japan.
authors: Asako
authors: A
authors: Komori
authors: Yukihiro
authors: Y
authors: Maki
authors: Isao
authors: I
authors: Ono
authors: Masahiro
authors: M
authors: Okamoto
mesh_terms: D019295:Computational Biology; D049490:Systems Biology / Q000379:methods*
keywords: S-system formalism; gene regulatory networks; real-coded genetic algorithm; system identification; systems biology