Learning Generative Models
of Microtubule Distributions
Generative Models, Microtubules, Simulated Images, Computational Biology, Bioimage Informatics
The field of location proteomics seeks to characterize the distributions of all proteins across all cell types and conditions over time. In order to further understand the behavior of proteins in cells (systems biology), we need cell simulations that take into account location information of the proteins (location proteomics). One way this gap can be bridged is by building models in a hierarchical, conditional manner so that models of all cell components can be constructed by automated learning from cell images.
Building on the work done by Zhao and Murphy  where models of cell, nuclear and object-type proteins were described, this thesis focuses on building models of microtubules. Microtubules are dynamic filamentous structures in cells that are important in many cellular processes such as cell division, motility and intracellular transport. Because of their small size and high density in cells, high throughput imaging technologies such as fluorescence microscopy make it harder to trace to extract information such as number and length. Because of this, one of major challenges for building automated methods that “learn” is the availability of limited or no ground truth data of the traces.
I developed a 3D generative model of microtubules and a model parameter estimation approach from confocal fluorescence microscopy images. The estimation approach is an indirect method that compares simulated with real images to estimate model parameters. The chapters in this thesis are organized based on the type of image data (2D vs 3D) and the cell preparation for imaging (fixed vs live cell preparations). Parameters are extracted from images of microtubules in the presence of nocodazole (a microtubule depolymerizing drug), showing the numbers and lengths to decrease over time, and from cell types of different lineages where their numbers and lengths are compared. Continuing on theme of building hierarchical conditional models, I describe a vesicle location model conditioned on a model of microtubules. The final chapter concludes with a summary with its implications and future work.