ABI Innovation: Bridging the Gap between the Transcriptome and the Proteome to Study Inter-cellular Signaling

系统生物学促进沿着多条探究线同时研究生理过程。然而，获取某些类型数据（如基因表达水平）的技术比获取其他类型数据（如蛋白质水平）的方法更先进、更全面、更便宜。为了消除这一差距，本研究将开发一种原则良好的方法，根据转录实验收集的数据优先考虑昂贵且耗时的蛋白质组学实验。该团队将使用新颖而复杂的算法来驱动这些分析，以计算有向斯坦纳无环图。他们将用他们的方法来研究生物学中的一个基本问题：在组织和器官中发现的复杂环境中，不同类型的细胞是如何相互交流的？他们将使用一种新型的体外三维肝脏模拟物来解决这个问题，这种模拟物在分层结构中包含两种不同的肝细胞类型。这个项目的教育部分将通过对四名本科生的暑期研究机构“工程组织中计算驱动的实验生物学”，将计算思维注入到本科水平的生物学和工程学中，包括项目相关主题的讲座和一个单独的合作研究项目。让所有学生参与一个研究项目，将使他们接触到团队科学，并让他们了解计算机科学、组织工程和实验细胞生物学如何无缝地交织在一起，以研究细胞过程。通过开发研究细胞间信号的创新解决方案，该项目将为细胞间和器官水平系统生物学的进步铺平道路。该项目将通过整合新的蛋白质组学数据和现有的测量数据来开发一个结合计算和实验的管道，以完善我们的模型和算法。该方法、算法和软件将成为新兴工具集的宝贵补充，这些工具集使用计算分析来优先考虑新的实验。该项目的研究、软件和数据将在http://bioinformatics.cs.vt.edu/~murali上提供。

Systems biology promotes studying physiological processes along multiple lines of inquiry simultaneously. However, technologies to capture some types of data (e.g., gene expression levels) are more advanced, more comprehensive, and less expensive than methods to obtain other types of data (e.g., protein levels). To remove this gap, this research will develop a well-principled approach to prioritize expensive and time-consuming proteomic experiments based on data collected from transcriptional experiments. The team will drive these analyses using novel and sophisticated algorithms to compute directed Steiner acyclic graphs. They will use their methodology to study a fundamental question in biology: how do different cell types communicate with each other in the complex environment found in tissues and organs? They will address this question using a novel in vitro three-dimensional liver mimic that contains two different hepatic cell types in a layered configuration. The educational component of this project will infuse computational thinking into biology and engineering at the undergraduate level via a summer research institute on "Computationally-Driven Experimental Biology in Engineered Tissues" to four undergraduate students, consisting of lectures on project-related topics and a single collaborative research project. Involving all the students in a single research project will expose them to team science and give them an appreciation of how computer science, tissue engineering, and experimental cell biology can be seamlessly interwoven to study cellular processes. By developing innovative solutions to study inter-cellular signaling, this project will pave the way for advances in inter-cellular and organ-level systems biology. This project will develop a combined computational-experimental pipeline by integrating new proteomic data with existing measurements in order to refine our models and algorithms. The methodology, algorithms, and software will be a valuable addition to the emerging set of tools that use computational analysis to prioritize new experiments. The research, software, and data resulting from this project will be made available at http://bioinformatics.cs.vt.edu/~murali.