A Dynamical method to estimate gene regulatory networks using time‐series data. Issue 2 (13th August 2014)

• Record Type: Journal Article
• Authors: Tu, Chengyi
• Is Part Of: Complexity. Volume 21:Issue 2(2015)
• Page Start: 134
• Page End: 144
• Publication Date: 2014-08-13
• Subjects: discrete dynamical system -- gene regulatory network -- topology estimation theory -- predict the expression rate; Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1002/cplx.21585
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : We propose an improved discrete dynamical system model to reconstruct the gene regulatory network (GRN), then estimate the variable topology using discrete‐time autosynchronization and predict the expression rate under the same condition. Although our method adopts a small number of sample time points to estimate the GRN, it could discern not only the role of the activator or repressor for each specific regulator, but also the regulatory ability of the regulator to the transcription rate of the target gene. Several examples are illustrated to verify that this method is feasible and effective for modeling the GRN and predicting the expression profile in the next cell cycle, the expression profile in the interval between two sample time points or the deficiency data. Additionally, this method provides a general tool for topology estimation of discrete‐time dynamical networks. © 2014 Wiley Periodicals, Inc. Complexity 21: 134–144, 2015