This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)
Reconstruct gene regulatory network using slice pattern model
- Equal contributors
1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, PR China
2 Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
3 Center for Medical Genomics, India University School of Medicine, Indianapolis, Indiana, 46202, USA
4 Division of Biostatistics Department of Medicine, Indiana University of School of Medicine, Indianapolis, Indiana, 46202, USA
5 Harvard Medical School, Harvard University, P.O. Box 400888, Cambridge, Massachusetts, 02115, USA
6 SpecPro Inc, Vicksburg, Mississippi 39180, USA
7 Department of Biological Science, University of Southern Mississippi, Hattiesburg, Mississippi, 39406, USA
BMC Genomics 2009, 10(Suppl 1):S2 doi:10.1186/1471-2164-10-S1-S2Published: 7 July 2009
Gene 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.
In 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.
With 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.