A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.
BACKGROUND
Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian network (DBN) is an important approach for predicting the gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time.


RESULTS
In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in the expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time-series expression data measured during the yeast cell cycle. The results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.
Department of Medicine, 5841 South Maryland Avenue, University of Chicago, Chicago, IL 60637, USA.
Min
M
Zou
Suzanne D
SD
Conzen
D000465:Algorithms; D001499:Bayes Theorem; D018797:Cell Cycle Proteins / Q000378:metabolism; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors; D014157:Transcription Factors / Q000378:metabolism*
D018797:Cell Cycle Proteins; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors