Inferring gene regulatory networks using a time-delayed mass action model.
This paper demonstrates a new time-delayed mass action model which applies a set of delay differential equations (DDEs) to represent the dynamics of gene regulatory networks (GRNs). The mass action model is a classical model which is often used to describe the kinetics of biochemical processes, so it is fit for GRN modeling. The ability to incorporate time-delayed parameters in this model enables different time delays of interaction between genes. Moreover, an efficient learning method which employs population-based incremental learning (PBIL) algorithm and trigonometric differential evolution (TDE) algorithm TDE is proposed to automatically evolve the structure of the network and infer the optimal parameters from observed time-series gene expression data. Experiments on three well-known motifs of GRN and a real budding yeast cell cycle network show that the proposal can not only successfully infer the network structure and parameters but also has a strong anti-noise ability. Compared with other works, this method also has a great improvement in performances.
* School of Information Science and Engineering, University of Jinan, Jinan, Shandong Province 250100, P. R. China.|† Shandong Provincial Key Laboratory of Network Based, Intelligent Computing, University of Jinan, Jinan, Shandong Province 250022, P. R. China.
Yaou
0000-0002-4134-2352
Y
Zhao
* School of Information Science and Engineering, University of Jinan, Jinan, Shandong Province 250100, P. R. China.
Mingyan
M
Jiang
† Shandong Provincial Key Laboratory of Network Based, Intelligent Computing, University of Jinan, Jinan, Shandong Province 250022, P. R. China.
Yuehui
Y
Chen
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D053263:Gene Regulatory Networks; D008957:Models, Genetic
Gene regulatory network; delay differential equations; population-based incremental learning algorithm; reverse engineering