EAGER: An integrative approach to exploration of dynamic protein-protein interaction networks in human platelets

最近的技术进步产生了大量的蛋白质-蛋白质相互作用（PPI）数据集。 这些数据通常以图表的形式呈现，其中节点代表蛋白质，边表示蛋白质之间的联系，并有实验支持。 这种表示表明PPI网络是非随机组织的，但不幸的是，它对细胞内蛋白质相互作用的动态性质几乎没有提供任何见解。 本项目的目标是开发以人类血小板细胞为靶细胞，通过整合各种信息源，探索PPI网络动态的通用方法，最终能够识别人类血小板中的新型信号通路和网络。本项目将开发一种新的图形模型，用于捕获蛋白质-蛋白质相互作用的动态性质。 (a)每个节点不是一个单独的蛋白质，而是一个蛋白质-蛋白质对，每个节点都用代表应该观察到相互作用的上下文的术语标记。以氨基酸序列相似性和协同进化为基础，谱聚类作为映射工具，将每个相互作用节点映射到一个固定维数的真实的向量上。(b)基于基因本体，将交互节点组表示为节点上的超边。(c)推断交互作用的问题将被转化为为给每个节点分配标签的问题。(d)标签分配问题将被解决为一个标签排序问题与标签数量预测问题相结合。(e)该方法将使用人类细胞和人类血小板数据的湿实验室实验进行验证。将利用迈阿密大学计算科学中心的高性能计算设施开发计算工具。 将积极招募代表性不足群体的本科生和高中生。 该大学将开设和教授计算生物学和化学生物学课程。

Recent technological advances have produced large data sets of protein-protein interactions (PPI). These data are often presented as graphs where nodes represent proteins and edges indicate the linkage between proteins with experimental support. This representation reveals that PPI networks are non-randomly organized, but unfortunately offers little insights on the dynamic nature of protein interactions inside the cell. The goal of this project is to develop generic methods for exploring the dynamics of PPI networks using human platelet cells as target and by integrating various information sources, with the eventual goal of being able to identify novel signaling pathways and networks in human platelets.In this project a new graph model will be developed for capturing the dynamic nature of protein-protein interaction in the following manner. (a) Each node is not an individual protein but a protein-protein pair and each node is labeled with terms representing contexts in which the interaction should be observed. Each interaction node will be mapped to a real vector of fixed dimension using amino-acid sequence similarity and co-evolution as basis spectral clustering as a mapping tool. (b) Based on gene ontology, groups of interaction nodes will be presented as hyper-edges over the nodes. (c) The problem of inferring interaction will be translated as the problem of assigning labels to each node. (d) The label assignment problem will be solved as a label-ranking problem combined with a label number predicting problem. (e) The method will be validated using wet-lab experiments with human cells and human platelet data. Computational tools will be developed using the high-performance computing facility in the Center for Computational Science at the University of Miami. Undergraduate and high school students from the underrepresented groups will be actively recruited. Courses on computational biology and on chemical biology will be developed and taught at the University.