Signal transduction through phosphorylation of proteins is a fundamental process involved in most of the biological pathways in all living species. It is also a dynamic process: the phosphorylation status of proteins constantly rewires in different organisms, tissues, cell lineages, and in the same cell but under different conditions. While experimental approaches such as phosphoproteomics have greatly advanced the study of such dynamics, we have only recently begun to construct computational models to estimate the phosphorylation status of proteins under unseen perturbations. The goal of this project is to develop an accurate and efficient method for predicting protein phosphorylation dynamics, and to establish an on-line service system for researchers to upload their own data and predict new dynamics under unseen perturbations. The research will implement an on-line tool, which will have wide applications from prokaryotes to higher, complex organisms, under a variety of conditions. Local high-school students will be recruited to participate in the project, which may spin off for them to compete in national and international science project competitions.
Current models for reconstructing phosphorylation relationships between proteins using time-course data mainly rely on searching the large space of possible network structures, a process that is time-consuming and not directly applicable to predicting responses under unseen interventions. This research will investigate a fundamental solution, truncated singular value decomposition with graph partitioning, to estimate phosphorylation levels given new perturbations. This method will be orders of magnitude faster and more accurate than contemporary methods. These algorithms will be tested using in silico simulation as well as phosphoproteomics data. The research will implement an on-line tool, which will allow biology users from diverse research domains to upload their time-course phosphoproteomic data, retrieve the reconstructed phosphorylation relationships specific to their organism and cell type, and predict the phosphorylation levels under unseen perturbations. The results of the project can be found at http://guanlab.ccmb.med.umich.edu/research.
