Reconstruct gene regulatory network using slice pattern model

Yadong Wang,Jack Y Yang,Yunlong Liu,Guohua Wang,Bo Yang,Haijun Tao,Youping Deng

doi:10.1186/1471-2164-10-s1-s2

Abstract

BackgroundGene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory relationships have been omitted in many linear models. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory.ResultsIn this study, we present a model-based computational approach, Slice Pattern Model (SPM), to identify gene regulatory network from time series gene expression array data. In order to estimate performances of stability and reliability of our model, an artificial gene network is tested by the traditional linear model and SPM. SPM can handle the multiple transcriptional time lags and more accurately reconstruct the gene network. Using SPM, a 17 time-series gene expression data in yeast cell cycle is retrieved to reconstruct the regulatory network. Under the reliability threshold, θ = 55%, 18 relationships between genes are identified and transcriptional regulatory network is reconstructed. Results from previous studies demonstrate that most of gene relationships identified by SPM are correct.ConclusionWith the help of pattern recognition and similarity analysis, the effect of noise has been limited in SPM method. At the same time, genetic algorithm is introduced to optimize parameters of gene network model, which is performed based on a statistic method in our experiments. The results of experiments demonstrate that the gene regulatory model reconstructed using SPM is more stable and reliable than those models coming from traditional linear model.

Highlights

Gene expression arrays, which measure mRNA expression levels of thousands of genes simultaneously, make it possible to understand the complexities of biological system
Slice pattern model to reconstruct the gene regulatory networks In order to solve the limitations of linear model, we propose a new method, slice pattern model (SPM), to reconstruct the gene regulatory networks from gene expression data mixed with noise
In order to evaluate the feasibility of Slice Pattern Model (SPM) on real gene expression array data, a yeast cell cycle gene network with nine specific genes is reconstructed by SPM, and verified by comparing with established relationships in previous investigations

Summary

Introduction

Gene expression arrays, which measure mRNA expression levels of thousands of genes simultaneously, make it possible to understand the complexities of biological system. Many approaches are proposed for gene regulatory networks modeling from gene expression data, such as Boolean network [3,4,5,6], linear model [7,8,9], Bayesian networks [10,11,12,13,14], neural networks [15,16], differential equations [17,18,19], models including stochastic components on the molecular level [20], and so on Those models can be classified into fine-grained and coarse grained approaches. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory

Methods

Results

Discussion

Conclusion