Statistical Methods for ODE Models in AIDS Research

艾滋病研究中 ODE 模型的统计方法

基本信息

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
项目类别：
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

项目摘要

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（由适用提供）：由于高通量技术的大量成本降低，经常在最近的HIV/AIDS研究和其他生物医学项目中收集了时间课程水平和纵向表型响应数据外，经常会收集到时间课程全基因组基因表达数据。但是，在转录组和蛋白质组学水平上有效使用高通量时间过程来研究动态响应和网络特征通常由于缺乏重建高维动态模型的统计方法而受到阻碍。在这个续订项目中，我们打算填补这一空白并提出以下特定目的：1）为高维的普通微分方程（ODE）模型开发更有效的参数估计方法。 AIM 1打算开发更有效的统计方法来估计高维ODE模型参数，以为重建基因，蛋白质和分子水平的生物网络提供基础。 2）开发新的统计方法和实现程序，以重建高维ode变量选择，以重建动态网络。我们结合了ODE模型的新统计估计方法和基于调节的可变选择技术，以识别ODE网络边缘。将为拟议的基于ODE的网络模型建立统计方法和理论理由。 3）使用计算机模拟和艾滋病毒/艾滋病研究中的实际数据分析来评估和验证目标1-2中开发的方法。仔细评估AIMS 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，这一点很重要。特别是，有必要将提出的方法应用于来自HIV/AIDS研究的实验数据，以证明提出的方法解决科学问题的有用性。 4）开发和传播有效的计算算法和用户友好的软件工具，以向更广泛的研究社区提出的方法。开发有效的计算算法并将计算源代码共享/传播到一般研究社区非常重要。