Abstract
Background: The reconstruction of gene regulatory networks (GRN) using gene expression data can gain new insights into the causality of transcriptional and cellular processes that make a complex living system. Dynamic Bayesian network (DBN) modeling has been increasingly used to reconstruct GRN for the temporal pattern of transcriptional interactions in a time course, but this approach requires expression data measured at even time intervals. In practice, time points at which gene expression is recorded are usually uneven-spaced, determined on the basis of distinct phases of biological processes. We reform DBN modeling to accommodate to any possible irregularity and sparsity of time course microarray data.
Results: The model is implemented with functional clustering that classifies dynamic genes into distinct clusters by adaptively fitting mean expression curves for each cluster, followed by a step of interpolating expression data at missing time points. The model is also equipped with unique power to integrate data from multiple expression experiments. We analyze two data sets of time course gene expression measured for vein bypass grafts in rabbits that receive two distinct treatments, high and low blood flow. The similarity and difference in the structure and organization of genetic networks can be identified under high and low flow, providing new insights into the mechanisms of how genes regulate each other to determine final phenotypic formation. Extensive simulation studies have been conducted to demonstrate the performance and property of the new model.
Conclusions: The results demonstrate that our adaptive Dynamic Bayesian Network model provides an unprecedented tool to elucidate a comprehensive picture of GRN. By analyzing real data sets from a surgical study and through extensive simulation studies, the new model has been well demonstrated for its usefulness and utility.
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Acknowledgements
This research was supported by National Institute of Health grants 1U10HL098115, 5U01HL119178 and 5UL1TR000127. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Institutes of Health.
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Wang, Y., Berceli, S.A., Garbey, M., Wu, R. (2019). Inference of Gene Regulatory Network Through Adaptive Dynamic Bayesian Network Modeling. In: Zhang, L., Chen, DG., Jiang, H., Li, G., Quan, H. (eds) Contemporary Biostatistics with Biopharmaceutical Applications. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-15310-6_5
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