Data-Driven Modeling of Morphological Dynamics and Intracellular Transport of Organelles

Data-driven modeling is essential to understanding complex cellular processes. In this thesis, we present a series of studies of analyzing morphological dynamics and intracellular transport of organelles using techniques of mathematical modeling, image processing and machine learning. We first characterized the morphology of organelles, focusing specifically on mitochondria. We developed a morphological data processing pipeline. Using this pipeline, we discovered a bi-modal distribution of mitochondrial sizes, with a stable mean value in each mode. We then developed a data-driven model to investigate how fusion/fission of mitochondria modulates their sizes. For further analysis of morphology of mitochondria as well as other cellular components, we developed a general purpose machine learning algorithm, which we refer to as shape component analysis (SCA). We used it for dimension reduction and classification of mitochondrial morphology and protein geometry. In addition to studying the morphological dynamics of cellular organelles using data-driven modeling, we investigated the intracellular transport of organelles. We first proposed a probabilistic model for studying the relation between mitochondrial size and the velocity of their active transport. The proposed model not only explained the relation between mitochondrial size and velocity observed in experiments under normal conditions but also suggested a novel relation under changed conditions. Further analysis of the proposed model also suggested a way to evaluate the binding/unbinding rates of motors carrying the mitochondria. We further studied the global organization of organelle transport. We proposed an image processing framework to characterize the spatiotemporal dynamics of intracellular transport in terms of the spatial localization of stationary organelles and the spatiotemporal patterns of organelle movement, respectively. We used this framework to analyze time-lapse images of Lamp1 transport and found different global transport patterns. Overall, our studies produced both computational modeling methods and specific biological results for quantitative and systems-level understanding the complex behavior of intracellular organelles. Acknowledgments Firstly, I would like to express my gratitude to my advisor Ge Yang for the continuous support of my Ph.D. study and related research. I would like to thank the rest of my thesis committee Christopher Langmead, Jelena Kovačević and Robert Tilton for their insightful comments and encouragement, but also for the hard question which motivated me to widen my research from various perspectives. I would also like to thank my fellows in the lab for helpful support and inspiring discussion. Many thanks to Yiyi Yu and Jackie Chen for kindly data sharing and technical support. This thesis would be an impossible mission without their help. A special thank goes to Huan-Kai Peng for his suggestion on presentation and endless trash talking. I would also like to thank my parents, my brother and sister. They were always supporting me to pursue my dream with their best wishes. The work was supported by US NSF grants MCB-1052660, DBI-1149494 and DBI-1052925, and a Ji-Dian Liang Graduate Research Fellowship and a Bertucci Graduate Research Fellowship from the Carnegie Institute of Technology, Carnegie Mellon University.