CAREER: Image-Based Computational Methods for Understanding Spatiotemporal Dynamics of Cellular Processes

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The inner environment of the cell is highly heterogeneous and dynamic yet exquisitely organized. Successful completion of cellular processes within this environment requires the right molecule or molecular complex to function at the right place at the right time. Understanding dynamic spatiotemporal behaviors of cellular processes is therefore essential to understanding at the systems level their molecular mechanisms. The research objective of this project is to extract mechanistic knowledge of cellular processes from images of their spatiotemporal dynamics by developing and applying required computational analysis and modeling methods. The technological development will be driven and guided by fundamental and specific biological questions regarding the spatiotemporal regulation of axonal cargo transport in neuronal cells as well as microtubule and motor translocation in mitotic spindles, which are representatives of cellular processes that are one-dimensional (1D) and two-dimensional (2D) in space, respectively. The specific research aims are: 1) To characterize spatiotemporal cell dynamics by combining single particle tracking with super-resolution imaging techniques; 2) To represent microscopic and macroscopic spatiotemporal dynamics of 1D cellular processes using hidden Markov models and spatiotemporal point process statistics, respectively, and to combine with biophysical modeling to understand patterned axonal cargo movement; and 3) To represent spatiotemporal dynamics of 2D cellular processes via non-rigid shape registration and uniform lattice space partitioning, and to combine with biophysical modeling to understand organized spindle microtubule and motor translocation.Advances in biology over the past half a century have made it possible to identify all the molecular components of a cell. How these components function and interact in space and time to drive specific cellular processes and what general principles govern these processes are fundamental questions to biology. This project will provide computational methods and software that are required to address these questions in a broad range of biological studies. The methods and software will be made freely available to the broader research community. In the meantime, the project will advance and promote teaching and training of computational analysis and understanding of biological images (also referred to as bioimage informatics) at Carnegie Mellon University (CMU), especially for students from engineering, computer science, and biology programs, and from the CMU-University of Pittsburgh joint PhD program in computational biology. Research and educational resources made possible by this project will be used for teaching and training of graduate and undergraduate students and for participating in outreach programs organized by the Department of Biomedical Engineering and the School of Computer Science at CMU.