<p>The cell cycle is one of the fundamental processes in all living organisms, and all cells arise from the division of existing cells. To better understand the regulation of the cell cycle, synchrony experiments are widely used to monitor cellular dynamics during this process. In such experiments, a large population of cells is generally arrested or selected at one stage of the cycle, and then released to progress through subsequent division stages. Measurements are then taken in this population at a variety of time points after release to provide insight into the dynamics of the cell cycle. However, due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, the time-series measurements from the synchronized cell populations do not accurately reflect the underlying dynamics of cell-cycle processes.</p><p>In this thesis, we introduce a deconvolution algorithm that learns a more accurate view of cell-cycle dynamics, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise, and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Though it can be applied to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote <italic>Saccharomyces cerevisiae</italic>. We show that our method more sensitively detects cell-cycle-regulated transcription, and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for both mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in the early G1 in a daughter-specific manner.</p><p>In addition to the cell-cycle deconvolution algorithm, we introduce <italic>DOMAIN</italic>, a protein-protein interaction (PPI) network alignment method, which employs a novel <italic>direct-edge-alignment</italic> paradigm to detect conserved functional modules (e.g., protein complexes, molecular pathways) from pairwise PPI networks. By applying our approach to detect protein complexes conserved in yeast-fly and yeast-worm PPI networks, we show that our approach outperforms two widely used approaches in most alignment performance metrics. We also show that our approach enables us to identify conserved cell-cycle-related functional modules across yeast-fly PPI networks.</p> / Dissertation

The proper packaging of the genome in eukaryotic nuclei is essential for proper gene expression and cell function. Chromatin at the large scale is divided into two major compartments heterochromatin and euchromatin. Heterochromatin compromises the transcriptionally inactive tightly packaged regions of chromatin, while euchromatin is the transcriptionally active region of chromatin. The Heterochromatin Protein family (HP1) proteins are epigenetic hallmarks of constitutive heterochromatin. Recent evidence suggests human HP1α undergoes liquid-liquid phase separation suggesting a role for HP1 phase separation in the formation of compacted heterochromatin within HP1 droplets. Phase separation is a biophysical property of proteins with intrinsically disordered domains which are protein domains that lack a defined secondary structure and have the ability to undertake multiple conformations. In this thesis, I investigated the ability of C. elegans HP1 homologs HPL-2A and HPL-1 to phase separate utilizing directed mutations to elucidate the intermolecular interactions that govern this phenomenon and different assays to assess their phase separation. I concluded that HPL-2A is a bona fide phase separating protein that selectively condenses chromatin. HPL-2A’s phase separation depends on specific interactions, mainly dimerization and the presence of lysine and arginine residues in the hinge region. HPL-2A has a specific IDR that drives its phase separation which is the hinge region as the CTE and NTE are not essential for its phase separation.