Systems Biology of Plasmodium falciparum: Building and Exploring Network Models

描述（由申请人提供）：疟疾是世界上最具破坏性的传染病之一。由于疟疾寄生虫疟原虫的快速进化和耐药性的传播，迫切需要开发新的抗疟策略。本项目的长期目标是建立对寄生虫寄生、发病机制和耐药性的分子基础的系统级理解。我们将实施结合机器学习、概率建模和全基因组关联分析的方法，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、发病机制、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的增加。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：我们无法对恶性疟原虫基因组中60%以上的预测基因产物进行功能注释。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断。2. 推断蜂窝网络的拓扑结构和动态相互作用。将开发健壮的模型来重建基因调控网络、信号级联和代谢途径，这些途径定义了疾病表型的遗传基础。3. 通过全基因组关联研究（GWAS）识别网络模型的进化特征。GWAS包括单核苷酸多态性（SNP）筛选具有不同表型的多个菌株，将作为高通量湿实验室验证网络响应药物治疗的有效手段。这样的网络是系统级病原体生物学观点的基石，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。

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.