APPLYING BIOINFORMATICS AND COMPUTATIONAL BIOLOGY TO STUDY HIV/AIDS

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。本项目侧重于计算流行病学的研究，重点关注一种主要关注的健康差距疾病--艾滋病毒/艾滋病。本项目年有三个具体目标。1)HIV/AIDS的分子流行病学问题导向方法论(EPOA)：问题识别和问题管理/解决方案三位一体是EPOA方法论的产物。问题识别三元组由主体、主体和环境三个支柱组成。问题管理/解决方案三合会由治疗、预防/控制和健康维护/促进支柱组成。2)HIV/AIDS的微观流行病学计算模型：回顾了基于HIV/AIDS分子EPOA的分子水平上的CD4+内动态和事件的序列(上文第1项)，并收集了微分方程式所需的六步中每一步的蛋白质合成速率、所涉及的酶等数据。3)艾滋病毒/艾滋病的HAART疗法：哪种组合最有效？我们使用SAS程序开发了各种统计模型。我们确定了时间对两个变量的影响。建立回归模型以确定CD4+与病毒载量之间的关系。4)人工智能技术在HAART疗效中的应用：利用神经网络，我们建立了预测HIV/AIDS患者CD4+含量和病毒载量的初步模型。5)开发了艾滋病毒/艾滋病系统动力学模型，重点放在非裔美国人艾滋病的宏观流行病学上。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This project focuses upon Research in Computational Epidemiology with an emphasis on a health disparity disease of major concern, that of HIV/AIDS. There are three specific aims addressed in this project year. 1) Molecular Epidemiologic Problem Oriented Approach (EPOA) methodology of HIV/AIDS: Problem Identification and Problem Management/solution triads are the back borne of EPOA. The Problem Identification Triad is composed of the agent, host and environment pillars. Problem Management/solution Triad is composed of therapeutics, prevention/control and the health maintenance/promotion pillars. 2) Microepidemiologic Computational Modeling of HIV/AIDS: Sequence of dynamics and events within the CD4+ at the molecular level based on molecular EPOA of HIV/AIDS (item 1 above) have been reviewed and data such as the process of protein synthesis rate, enzymes involved, etc. in each of the six steps necessary for the differential equation has been collected. 3) HAART Therapies for HIV/AIDS: Which combination is the most effective? We developed various statistical models using SAS procedures. We determined the effect of time on two variables. Regression models were developed to determine the relationship between CD4+ and viral load. 4) Use of Artificial Intelligence Techniques in HAART Efficacy: Using neural networks we have developed preliminary models that predict CD4+ contents and viral load in HIV/AIDS patients. 5) Development of a systems dynamics model for HIV/AIDS with a focus on the macro-epidemiology of AIDS in African Americans has been developed.