Systems Biology of Plasmodium falciparum: Building and Exploring Network Models

恶性疟原虫的系统生物学：构建和探索网络模型

• 批准号: 8131721
• 负责人: YUFENG WANG
• 金额: $ 23.81万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8131721
• 关键词: Animal Model; Antimalarials; Area; Artificial Intelligence; Beryllium; Bioinformatics; Biological; Biology; Cell Cycle Regulation; Cell physiology; Chromosome Mapping; Complex; Data; Data Set; Development; Digestion; Discipline; Drug resistance; Electrical Engineering; Elements; Engineering; Family; Gene Family; Gene Proteins; Genes; Genomics; Growth; Harvest; Hemoglobin; Imagery; Immune; Infection; Intervention; Life Cycle Stages; Location; Malaria; Maps; Mediating; Methods; Modeling; Morbidity - disease rate; Open Reading Frames; Parasites; Pattern; Peptide Hydrolases; Pharmaceutical Preparations; Phenotype; Plant Roots; Plasmodium; Plasmodium falciparum; Prevalence; Process; Property; Proteins; Proteomics; Research; Research Personnel; Resistance; Role; Rupture; Signal Transduction; Specialist; Staging; Statistical Models; System; Systems Biology; Techniques; Testing; Therapeutic; Transcription Factor 3; Vaccines; Work; base; biological systems; comparative genomics; computerized data processing; cost effective; data integration; data mining; design; driving force; experience; functional genomics; insight; member; mortality; network models; novel; pressure; programs; prototype; quantum; research study; response; stem; theories; trafficking; transcription factor; transcriptomics

DESCRIPTION (provided by applicant): There is an urgent need for novel anti-malaria interventions, due to the growing resistance of Plasmodium parasites to available drugs. The long-term objective of our research is to identify potential drug or vaccine targets using an efficient and cost-effective data-mining approach. This proposed study is aimed at the identification of biological networks as the first step toward a systems-level understanding of parasite biology. Compared to functional genomics, which requires a priori information about the identity or function of targets, a systems-level understanding of parasite biology will allow us to identify targets based on their key roles in networks. Specific aim 1 focuses on the harvest of data relevant to three gene families: the proteases, transporters, and transcription factors that are essential components in cellular networks for parasite growth and infection. Our project will encompass genomic data, expression data, and data derived from other high throughput experiments and bioinformatic and statistical analyses. Specific aim 2 focuses on the integration and encapsulation of these data in the form of network models. The models will be inferred using probabilistic graphical methods drawn from the field of engineering. Specific aim 3 focuses on the exploration of these models, particularly in the light of data from wet lab gene mapping and drug resistance experiments. Our "biological maps" will enable the visualization of evolutionary forces driving changes in the parasite's phenotype and should allow us to identify network design weaknesses in the parasite--potential vulnerabilities that could result in new malarial control strategies.

描述（由申请人提供）：由于疟原虫寄生虫对可用药物的耐药性日益增强，迫切需要新的抗疟疾干预措施。我们研究的长期目标是使用有效且具有成本效益的数据挖掘方法来确定潜在的药物或疫苗靶点。这项拟议的研究旨在识别生物网络，作为对寄生虫生物学的系统级理解的第一步。功能基因组学需要关于目标的身份或功能的先验信息，与之相比，对寄生虫生物学的系统级理解将使我们能够根据它们在网络中的关键作用来识别目标。具体目标1侧重于收获与三个基因家族相关的数据：蛋白酶，转运蛋白和转录因子，它们是寄生虫生长和感染的细胞网络中的重要组成部分。我们的项目将包括基因组数据，表达数据，以及来自其他高通量实验和生物信息学和统计分析的数据。具体目标2侧重于以网络模型的形式集成和封装这些数据。这些模型将使用来自工程领域的概率图形方法来推断。具体目标3侧重于这些模型的探索，特别是根据湿实验室基因定位和耐药性实验的数据。我们的“生物地图”将使驱动寄生虫表型变化的进化力量可视化，并使我们能够识别寄生虫网络设计的弱点-可能导致新的疟疾控制策略的潜在漏洞。