Owing to the significant cost reduction of high-throughput technologies, frequent time course genome-wide gene expression data, in addition to time course cellular level and longitudinal phenotype response data, are often collected in recent HIV/AIDS studies and other biomedical projects. However, the effective use of the high-throughput time course data at transcriptomic and proteomics levels to study dynamic responses and network features is often hindered by lacking of statistical methods to reconstruct high-dimensional dynamic models. In this renewal project, we intend to fill this gap and propose the following specific aims: 1) Develop more efficient parameter estimation methods for high-dimensional ordinary differential equation (ODE) models.
Aim 1 intends to develop more efficient statistical methods to estimate high-dimensional ODE model parameters to provide a foundation for reconstructing biological networks at gene, protein and molecular levels. 2) Develop novel statistical methods and implementation procedures for high-dimensional ODE variable selection to reconstruct the dynamic networks. We combine new statistical estimation methods for ODE models and regularization-based variable selection techniques to identify ODE network edges. Statistical methodologies and theoretical justifications will be established for the proposed ODE-based network models. 3) Evaluate and validate the methodologies developed in Aims 1-2 using computer simulations and real data analysis from HIV/AIDS studies. It is important to carefully evaluate the high-dimensional ODE variable selection and parameter estimation methods developed in Aims 1-2, and perform comparisons with existing methods for practical use. In particular, it is necessary to apply the proposed methods to experimental data from HIV/AIDS studies in order to demonstrate the usefulness of the proposed methodologies to address scientific questions. 4) Develop and disseminate efficient computational algorithms and user-friendly software tools for the proposed methods to the broader research community. It is very important to develop efficient computing algorithms and share/disseminate the computational source codes to the general research community.
This project intends to develop novel statistical methods and efficient implementation algorithms as well as theoretical justifications for ODE-based network construction, motivated by HIV/AIDS studies so that we can fill the gap and address the critical methodology barrier in systems biology research, which is one of the important research areas in HIV/AIDS and essential in ending AIDS epidemics.
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|Sun, Xiaodian; Hu, Fang; Wu, Shuang et al. (2016) Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human. Infect Dis Model 1:52-70|
|Carey, Michelle; Wu, Shuang; Gan, Guojun et al. (2016) Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans. Infect Dis Model 1:28-39|
|Qiu, Xing; Wu, Shuang; Hilchey, Shannon P et al. (2015) Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice. PLoS One 10:e0138110|
|Wu, Hulin; Miao, Hongyu; Xue, Hongqi et al. (2015) Quantifying Immune Response to Influenza Virus Infection via Multivariate Nonlinear ODE Models with Partially Observed State Variables and Time-Varying Parameters. Stat Biosci 7:147-166|
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|Luque, Amneris E; Orlando, Mark S; Leong, U-Cheng et al. (2014) Hearing function in patients living with HIV/AIDS. Ear Hear 35:e282-90|
|Ding, A Adam; Wu, Hulin (2014) Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression. Stat Sin 24:1613-1631|
|Qiu, Xing; Hu, Rui; Wu, Zhixin (2014) Evaluation of bias-variance trade-off for commonly used post-summarizing normalization procedures in large-scale gene expression studies. PLoS One 9:e99380|
|Linel, Patrice; Wu, Shuang; Deng, Nan et al. (2014) Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches. J Pharmacokinet Pharmacodyn 41:509-21|
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