Peter Koo

Resolving distinct biochemical interaction states by analyzing the diffusive behaviors of individual protein trajectories is challenging due to the limited statistics provided by short trajectories and experimental noise sources, which are intimately coupled into each protein’s localization. In the first part of this thesis defense, I will describe a novel, machine-learning based classification methodology, called perturbation expectation-maximization (pEM), which simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors which collectively result from distinct biochemical interactions. I will then discuss an experimental application of pEM to Rho GTPase, an integral regulator of cytoskeletal dynamics and cellular homeostasis, inside live cells. In the second part of the defense, I will describe optical tweezers measurements of isolated yeast nuclei, together with a novel imagining of yeast nuclei in living cells. These measurements, which were carried out on a suite of different yeast strains, reveal that the tethering of chromatin to the nuclear envelope is an important determinant of eukaryotic nuclear mechanical properties.