Statistical Methods for ODE Models in AIDS Research

艾滋病研究中 ODE 模型的统计方法

• 批准号: 9064752
• 负责人: Hulin Wu
• 金额: $ 34.65万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9064752
• 关键词: AIDS/HIV problem; Acquired Immunodeficiency Syndrome; Address; Algorithms; Area; Basic Science; Biological; Biomedical Research; Biomedical Technology; Blood; CD8B1 gene; Cell Line; Communities; Complex; Computational algorithm; Computer Simulation; Data; Data Analyses; Differential Equation; Disease Progression; Epidemic; Foundations; Funding Agency; Gene Expression; Gene Proteins; Genomics; HIV; HIV Infections; HIV vaccine; HIV-1; Health; Highly Active Antiretroviral Therapy; Immune system; Infection; Investments; Life; Macaca; Macaca mulatta; Messenger RNA; Metabolic Pathway; Methodology; Methods; MicroRNAs; Modeling; Molecular; Molecular Genetics; Network-based; Pathogenesis; Phenotype; Procedures; Proteins; Proteomics; Regulator Genes; Research; Research Personnel; SIV; Signal Transduction; Software Framework; Software Tools; Source Code; Statistical Methods; Systems Biology; T-Lymphocyte; Techniques; Therapeutic; Time; United States National Institutes of Health; Vaccination; Vaccines; Viral Load result; Virus; base; biological systems; cost; deep sequencing; design; genome-wide; high throughput screening; high throughput technology; immunogenicity; mathematical model; metabolomics; network models; new technology; nonhuman primate; novel; pathogen; research study; response; temporal measurement; time use; transcriptome sequencing; transcriptomics; user friendly software; vaccine development; vector vaccine

 DESCRIPTION (provided by applicant): 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.