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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
7R01AI087135-06
Application #
9064752
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Gezmu, Misrak
Project Start
2010-01-15
Project End
2020-04-30
Budget Start
2016-05-01
Budget End
2017-04-30
Support Year
6
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77225
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
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
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
Zand, Martin S; Wang, Jiong; Hilchey, Shannon (2015) Graphical Representation of Proximity Measures for Multidimensional Data: Classical and Metric Multidimensional Scaling. Math J 17:
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
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
Ding, A Adam; Wu, Hulin (2014) Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression. Stat Sin 24:1613-1631
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

Showing the most recent 10 out of 27 publications