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.
|Chen, Iris; Kelkar, Yogeshwar D; Gu, Yu et al. (2017) High-dimensional linear state space models for dynamic microbial interaction networks. PLoS One 12:e0187822|
|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|
|Zand, Martin S; Wang, Jiong; Hilchey, Shannon (2015) Graphical Representation of Proximity Measures for Multidimensional Data: Classical and Metric Multidimensional Scaling. Math J 17:|
|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|
|Miao, Hongyu; Wu, Hulin; Xue, Hongqi (2014) Generalized Ordinary Differential Equation Models. J Am Stat Assoc 109:1672-1682|
|Wu, Hulin; Lu, Tao; Xue, Hongqi et al. (2014) Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling. J Am Stat Assoc 109:700-716|
|Wu, Shuang; Liu, Zhi-Ping; Qiu, Xing et al. (2014) Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations. PLoS One 9:e95276|
Showing the most recent 10 out of 29 publications