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。这些数据通常以图形的形式呈现，其中节点表示蛋白质，边缘表示具有实验支持的蛋白质之间的联系。这种表现揭示了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提供了很少的见解。该项目的目标是开发通用方法，以人类血小板细胞为靶点，通过整合各种信息源，探索PPI网络的动态，最终目标是能够识别人类血小板中的新信号通路和网络。在这个项目中，将开发一个新的图形模型，以以下方式捕捉蛋白质-蛋白质相互作用的动态特性。(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，并且每个节点都标有表示应该观察相互作用的上下文的术语。利用氨基酸序列相似性和协同进化为基础的光谱聚类作为映射工具，将每个交互节点映射到一个固定维的实向量上。(b)基于基因本体，交互节点组将以节点之上的超边缘形式呈现。(c)推断相互作用的问题将转化为给每个节点分配标签的问题。(d)标签分配问题将作为标签排序问题和标签数量预测问题的结合来解决。(e)该方法将使用人类细胞和人类血小板数据进行湿实验室实验验证。计算工具将利用迈阿密大学计算科学中心的高性能计算设施进行开发。将积极招收来自代表性不足群体的本科生和高中生。计算生物学和化学生物学课程将在该大学开发和教授。

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.