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: 8266807
• 负责人: YUFENG WANG
• 金额: $ 36.75万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8266807
• 关键词: Address; Algorithms; Antimalarials; Biological; Biology; Cell Cycle; Cessation of life; Characteristics; Child; Clinical; Communicable Diseases; Complement; Complex; Data; Data Analyses; Development; Disease; Drug Delivery Systems; Drug resistance; Erythrocytes; Evolution; Funding; Gene Duplication; Gene Proteins; Genes; Genetic; Genome; Genome Components; Genomics; Goals; Grant; Horizontal Gene Transfer; Immune; Infection; Knowledge; Lead; Life Cycle Stages; Life Style; Machine Learning; Malaria; Measures; Metabolic Pathway; Methods; Modeling; Molecular; Open Reading Frames; Parasites; Pathogenesis; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Plant Roots; Plasmodium; Plasmodium falciparum; Population; Process; Protein Family; Proteins; Regulator Genes; Research; Resistance; Role; Screening procedure; Signal Transduction; Single Nucleotide Polymorphism; Solutions; Statistical Models; System; Systems Biology; Testing; Validation; Virulence; Work; base; comparative genomics; data mining; disease phenotype; drug development; genome wide association study; insight; interest; mortality; network models; novel; novel therapeutics; parasite genome; parasitism; pathogen; pressure; prevent; research study; response; transcription factor; transcriptomics; vaccine development

DESCRIPTION (provided by applicant): Malaria is one of the most devastating infectious diseases in the world. Development of novel antimalarial strategies is urgently needed due to the rapid evolution and spread of drug resistance in malaria parasites Plasmodium. The long term goal of this proposed project is to develop a systems-level understanding of the molecular basis of parasitism, pathogenesis, and drug resistance. We will implement approaches that combine machine learning, probabilistic modeling, and genome-wide association analysis to develop more robust computational solutions and identify a comprehensive set of genes or gene products in biological networks that show an increase in genetic variability that can be associated with drug resistance, pathogenesis, virulence, responses to environmental challenges, or with other interesting phenotypes. The three specific aims are: 1. To identify network components using effective remote homology based methods. We will address a critical barrier in malaria research: our inability to assign functional annotation to over 60% of the predicted gene products in the genome of Plasmodium falciparum. We will use a machine learning approach to detect evolutionarily conserved characteristics of the genes/proteins for network inference. 2. To infer the topology and dynamic interplay of cellular networks. Robust models will be developed to reconstruct the gene regulatory networks, signaling cascades and metabolic pathways that define the genetic basis for disease phenotypes. 3. To identify evolutionary signatures of network models by genome-wide association studies (GWAS). GWAS including Single Nucleotide Polymorphism (SNP) screening of multiple strains with varying phenotypes will serve as an effective means for high throughput wet-lab validations of networks in response to drug treatment. Such networks are the cornerstones of a systems-level view of pathogen biology, a view that will allow us to transform disparate types of data into biological insights for drug development. PUBLIC HEALTH RELEVANCE: Malaria remains one of the most important infectious diseases in the world today, infecting 300-500 million people yearly and resulting in 1-2 million deaths, primarily of young children. This study will develop computational solutions to problems that hitherto have prevented us from gaining a global view of how infection by the malaria parasite leads to the development of the disease. This global overview will help us develop specific solutions to the problems of preventing and treating malaria.

描述(申请人提供)：疟疾是世界上最具破坏性的传染病之一。由于疟疾寄生虫中抗药性的快速进化和传播，迫切需要开发新的抗疟疾策略。这项拟议项目的长期目标是发展对寄生虫、发病机制和耐药性的分子基础的系统水平的理解。我们将实施将机器学习、概率建模和全基因组关联分析相结合的方法，以开发更强大的计算解决方案，并在生物网络中识别一组全面的基因或基因产物，这些基因或基因产物显示出与耐药性、致病机理、毒力、对环境挑战的反应或其他有趣的表型相关的遗传变异性增加。这三个具体目标是：1.使用有效的基于远程同源的方法来识别网络组件。我们将解决疟疾研究中的一个关键障碍：我们无法为恶性疟原虫基因组中超过60%的预测基因产物分配功能注释。我们将使用机器学习方法来检测基因/蛋白质的进化保守特征，以进行网络推理。2.推断蜂窝网络的拓扑结构和动态相互作用。将开发强大的模型来重建定义疾病表型遗传基础的基因调控网络、信号级联和代谢途径。3.通过全基因组关联研究来识别网络模型的进化特征。包括对具有不同表型的多个菌株的单核苷酸多态(SNP)筛选将成为高通量湿实验室验证网络对药物治疗的反应的有效手段。这样的网络是病原体生物学系统级观点的基石，这种观点将使我们能够将不同类型的数据转化为药物开发的生物学见解。 与公共卫生相关：疟疾仍然是当今世界最重要的传染病之一，每年感染3亿至5亿人，并导致100万至200万人死亡，主要是幼儿。这项研究将为迄今为止阻碍我们获得疟疾寄生虫感染如何导致疾病发展的全球视角的问题开发计算解决方案。这一全球概览将帮助我们制定预防和治疗疟疾问题的具体解决方案。