APPLYING BIOINFORMATICS AND COMPUTATIONAL BIOLOGY TO STUDY HIV/AIDS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 该项目的重点是计算流行病学研究，重点是主要关注的健康差异疾病，即艾滋病毒/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（EPOA）：问题识别和问题管理/解决三联体是EPOA的基础。问题识别三元组由代理、主机和环境支柱组成。问题管理/解决方案三合一由治疗、预防/控制和健康维护/促进三大支柱组成。2)HIV/AIDS的微流行病学计算建模：基于HIV/AIDS的分子EPOA（上述第1项），在分子水平上对CD 4+内的动态和事件序列进行了审查，并收集了微分方程所需的六个步骤中的每一个步骤中的数据，如蛋白质合成速率、所涉及的酶等。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CD 4+和病毒载量之间的关系。4)人工智能技术在HAART疗效中的应用：使用神经网络，我们已经开发出了预测HIV/AIDS患者中CD 4+含量和病毒载量的初步模型。5)开发了一个艾滋病毒/艾滋病系统动力学模型，重点是非洲裔美国人艾滋病的宏观流行病学。

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.