title: Collocation-based sparse estimation for constructing dynamic gene networks.
abstract: One of the open problems in systems biology is to infer dynamic gene networks describing the underlying biological process with mathematical, statistical and computational methods. The first-order difference equation-based models such as dynamic Bayesian networks and vector autoregressive models were used to infer time-lagged relationships between genes from time-series microarray data. However, two primary problems greatly reduce the effectiveness of current approaches. The first problem is the tacit assumption that time lag is stationary. The second is the inseparability between measurement noise and process noise (unmeasured disturbances that pass through time process). To address these problems, we propose a stochastic differential equation model for inferring continuous-time dynamic gene networks under the situation in which both of the process noise and the observation noise exist. We present a collocation-based sparse estimation for simultaneous parameter estimation and model selection in the model. The collocation-based approach requires considerably less computational effort than traditional methods in ordinary stochastic differential equation models. We also incorporate various biological knowledge easily to refine the estimation accuracy with the proposed method. The results using simulated data and real time-series expression data of human primary small airway epithelial cells demonstrate that the proposed approach outperforms competing approaches and can provide significant genes influenced by gefitinib.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. shima@ims.u-tokyo.ac.jp.
authors.forename: Teppei
authors.initials: T
authors.lastname: Shimamura
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Masao
authors.initials: M
authors.lastname: Nagasaki
authors.forename: Mai
authors.initials: M
authors.lastname: Yamauchi
authors.forename: Rui
authors.initials: R
authors.lastname: Yamaguchi
authors.forename: André
authors.initials: A
authors.lastname: Fujita
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Tamada
authors.forename: Noriko
authors.initials: N
authors.lastname: Gotoh
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology; D003198:Computer Simulation; D004847:Epithelial Cells / Q000166:cytology / Q000378:metabolism; D005189:False Positive Reactions; D000077156:Gefitinib; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011799:Quinazolines / Q000494:pharmacology; D012372:ROC Curve; D013269:Stochastic Processes; D013997:Time Factors; D014158:Transcription, Genetic
chemical_list: D000970:Antineoplastic Agents; D011799:Quinazolines; D000077156:Gefitinib