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.

• PMID: 27093908
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Journal Article | Research Support, Non-U.S. Gov't

Authors

Zhao Y, Jiang M, Chen Y

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