Data-Driven Modeling of Morphological Dynamics and Intracellular Transport of Organelles
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
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.