Abstract
BackgroundGene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN.ResultsIn this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs.ConclusionBy taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.
Highlights
Gene regulatory network (GRN) is a fundamental topic in systems biology
We proposed a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution
network deconvolution (ND) does not consider time delays, which are essential in gene interactions
Summary
Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. While many methods have been introduced to reconstruct first-order gene regulation (e.g. DBN-MCMC [1,2,3], dynamic RandomForest [4]), there are only a few methods for inferring time-delayed GRN. In 2010, a dynamic version of ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) was introduced to infer time-delayed dependencies among genes [5]. Their method, called TimeDelay ARACNE (or TDARACNE), is able to reconstruct time-delayed dependencies effectively. Mundra et al proposed a method for inferring time-delayed GRN based on cross-correlation and LASSO [8] This method has been tested on reallife yeast pathways in G1 phase to show its effectiveness in identifying time-delayed regulation among genes. The performance of inferring time-delayed genetic regulation is yet to be further improved
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