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.

 描述（由申请人提供）：由于高通量技术的成本显著降低，在最近的HIV/AIDS研究和其他生物医学项目中，除了时间过程细胞水平和纵向表型反应数据之外，还经常收集时间过程全基因组基因表达数据。然而，由于缺乏统计学方法来重建高维动态模型，使得在转录组学和蛋白质组学水平上有效利用高通量时程数据来研究动态响应和网络特征受到阻碍。在这个更新项目中，我们打算填补这一空白，并提出以下具体目标：1）开发更有效的高维常微分方程（ODE）模型的参数估计方法。目的1旨在发展更有效的统计方法来估计高维ODE模型参数，为在基因、蛋白质和分子水平上重建生物网络提供基础。2)开发新的统计方法和实现程序，用于高维ODE变量的选择，以重建动态网络。我们结合联合收割机新的统计估计方法的ODE模型和正则化为基础的变量选择技术，以确定ODE网络的边缘。统计方法和理论的理由将建立拟议的基于ODE的网络模型。3)利用计算机模拟和艾滋病毒/艾滋病研究的真实的数据分析，评价和验证目标1-2中制定的方法。重要的是要仔细评估目标1-2中开发的高维ODE变量选择和参数估计方法，并与现有方法进行比较以供实际使用。特别是，它是必要的，适用于从艾滋病毒/艾滋病研究的实验数据，以证明所提出的方法，以解决科学问题的有用性的建议方法。4)为拟议的方法开发并向更广泛的研究界传播有效的计算算法和用户友好的软件工具。开发高效的计算算法并将计算源代码共享/传播给一般研究社区是非常重要的。