Abstract
The gene regulatory network (GRN) is a complex control system and plays a fundamental role in the physiological and development processes of living cells. Focusing on the ordinary differential equation (ODE) modeling approach, we propose a novel pipeline for constructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. A five-step procedure, i.e., detection of temporally differentially expressed genes, clustering genes into functional modules, identification of network structure, parameter estimate refinement and functional enrichment analysis, is developed, combining a series of cutting-edge statistical techniques to efficiently reduce the dimension of the problem and to account for the correlations between measurements from the same gene. In the key step of identifying the network structure, we employ the advanced parameter estimation and statistical inference methods to perform model selection for the ODE models. The proposed pipeline is a computationally efficient data-driven tool bridging the experimental data and the mathematical modeling and statistical analysis. The application of the pipeline to the time course gene expression data from influenza-infected mouse lungs has led to some interesting findings of the immune process in mice and also illustrated the usefulness of the proposed methods.
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Acknowledgements
This research was partially supported by the NIH grants HHSN 272201000055C, AI087135, and the University of Rochester CTSI pilot award (UL1RR024160) from the National Center For Research Resources.
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Wu, S., Liu, ZP., Qiu, X., Wu, H. (2013). High-Dimensional Ordinary Differential Equation Models for Reconstructing Genome-Wide Dynamic Regulatory Networks. In: Hu, M., Liu, Y., Lin, J. (eds) Topics in Applied Statistics. Springer Proceedings in Mathematics & Statistics, vol 55. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7846-1_15
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DOI: https://doi.org/10.1007/978-1-4614-7846-1_15
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