AF: Small: Collaborative Research: Cell Signaling Hypergraphs: Algorithms and Applications

活细胞中的蛋白质以复杂的方式相互作用。图表作为一种自然的方式来表示这些交互。在传统的图表示中，一个节点表示一个蛋白质，一条边表示两个蛋白质之间的相互作用。尽管这样的图表已经被广泛使用了很多年，但它们并不能准确地捕捉到蛋白质相互作用的重要特征，例如以称为复合物的组进行操作的蛋白质，涉及这种复合物的反应可能有两种以上的反应物和产物，以及其他蛋白质的影响，这些蛋白质的存在可以调节反应。该项目将开发一种称为信号超图的新表示，自然地描述多组蛋白质之间的关系，如复合物、反应物、产物和调节剂。此外，该项目将为信号超图分析中的基本计算挑战开发新的算法，并将这种新的表示和这些算法应用于广泛使用的细胞反应数据库。该项目将通过弗吉尼亚理工大学最大限度地提高学生多样性计划和弗吉尼亚理工大学计算机科学本科生研究计划招募本科生，积极参与研究。学生将参与多个学期的研究，最终目标是领导他们自己的个人项目，并在出版物中获得合著者身份。通过这种方式，该项目将使学生了解计算思维如何在现代分子生物学中发挥重要作用，从而实现STEM教育的重要目标。这个项目的重点是开发用于分析细胞信号超图的算法。目标1侧重于通过信号超图找到短路径来有效生成产品的方法，同时考虑反馈回路和反应调节器。目标2开发用于发现信号通路中缺失的蛋白质、复合物和反应的算法。最后，Aim 3将发布开源软件，实现本项目开发的信令超图算法。

Proteins in the living cell interact with each other in complex ways. Graphs have emerged as a natural way to represent these interactions. In a conventional graph representation, a node represents a protein and an edge represents an interaction between two proteins. Although such graphs have been in widespread use for many years, they do not accurately capture important features of protein interactions, such as proteins that operate in groups called complexes, reactions involving such complexes that may have more than two reactants and products, as well as the influence of other proteins whose presence can regulate reactions. This project will develop a new representation called signaling hypergraphs that naturally describes the relationships between multiple groups of proteins as complexes, reactants, products, and regulators. Furthermore, the project will develop novel algorithms for fundamental computational challenges in the analysis of signaling hypergraphs, and apply this new representation and these algorithms to widely-used databases of cellular reactions. The project will actively involve undergraduate students in research by recruiting them through the Virginia Tech Initiative to Maximize Student Diversity, and the Virginia Tech Undergraduate Research in Computer Science program. Students will engage in multiple semesters of research with the goal of ultimately leading their own individual projects, and obtaining co-authorship in publications. In this way the project will expose students to how computational thinking plays a major role in modern molecular biology, thereby meeting an important goal of STEM education. This project focuses on developing algorithms for the analysis of cell signaling hypergraphs. Aim 1 focuses on methods for generating products efficiently by finding short paths through signaling hypergraphs, while accounting for feedback loops and reaction regulators. Aim 2 develops algorithms for discovering missing proteins, complexes, and reactions in a signaling pathway. Finally, Aim 3 will release open-source software implementing the algorithms for signaling hypergraphs developed in this project.