Cellular organelles are inherently complex, characterized by intrinsic stochasticity and regulated by diverse molecular machineries across different scales. Therefore, to comprehensively understand organelles, it is crucial to approach problems from various angles, rather than being confined to a single perspective or tool.

In this dissertation, we present our contributions to stochastic and statistical computational methods, super-resolution imaging, and single particle tracking, aimed at a better understanding of organelles in vivo, in vitro, and in silico.

The first project examines the cellular projection known as the airineme in zebrafish, focusing on its shape in relation to its potential functional optimization objective. By analyzing in vivo image data using a statistical analysis pipeline and integrating these findings with a mathematical model, we found that the shapes of zebrafish airinemes are optimal for contacting target cells. Additionally, we discovered that there is a trade-off between directional sensing and target cell contact.

The second project introduces an imaging technique we developed, named Proximity Labeling Expansion Microscopy (PL-ExM). This technique facilitates the visualization of the interactome landscape with super-resolution. PL-ExM not only offers multi-color visualization of interactome structures and specific proteins with super-resolution, but also serves as a tool for assessing the labeling radius and efficiency of various proximity labeling techniques in vitro.

In the third project, we have developed a comprehensive pipeline to enhance our understanding of how the transport of signaling molecules is regulated within the primary cilium. We established a mathematical model and conducted in silico experiments to make predictions based on various parameter perturbations. Subsequently, we cultured primary cilia in vitro and performed single particle tracking of signaling molecules within them. Finally, we developed an analysis algorithm using Bayesian statistics. This algorithm utilizes tracking data to identify the molecular and biophysical factors that govern the transport of these signaling molecules.

The success of all three projects presented here was made possible through the synergistic integration of diverse scientific approaches, demonstrating the power of mathematical, computational, and systems biology.