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.
