@article {Frumkin044339, author = {Jesse P. Frumkin and Biranchi N. Patra and Anthony Sevold and Kumkum Ganguly and Chaya Patel and Stephanie Yoon and Molly B. Schmid and Animesh Ray}, title = {Modeling Chromosome Maintenance as a Property of Cell Cycle in Saccharomyces cerevisiae}, elocation-id = {044339}, year = {2016}, doi = {10.1101/044339}, publisher = {Cold Spring Harbor Laboratory}, abstract = {A healthy cell must maintain chromosome integrity during each phase of the cell cycle. Yet during the cell cycle, defects in DNA repair, DNA synthesis, and chromosome transmission can cause chromosome instability. Here, we build a cyclical model for chromosome maintenance by assaying the genetic interactions of pairs of genes that each normally functions to maintain chromosomes. First, we confirm that 19 genes affect chromosome maintenance by overexpressing the respective genes using an assay for chromosome instability. We then introduce plasmids containing these 19 genes into 18 deletion mutant strains each of which is known to cause chromosome maintenance defect. By conditionally overexpressing each of the 19 genes in each of the 18 deletion strains, we observe 34 cases of synthetic dosage lethality, 58 of synthetic dosage sickness, and 26 of suppression (out of 349 combinations tested). Ordinarily, given a large matrix of genetic interactions, it is computationally intractable (and NP hard) to compute all permutations of the arrangements of the rows and columns, which is necessary for finding an optimum arragement of entries for a given pattern of functional significance. Here, using our small yet dense matrix we rearrange the matrix into an optimal cycle. By optimally reordering the matrix representation of these genetic interactions using integer linear programming, we find that the optimal order of the functions of the genes approximates the temporal order of processes during the yeast cell cycle. To further validate this theoretical approach, we computed in silico genetic interactions derived from a predetermined cyclical model and confirmed that the predicted cycle was consistent. These results suggest that matrices from other genetic experiments could be computationally rearranged using this method to reveal an underlying cycle that is biological meaningful.}, URL = {https://www.biorxiv.org/content/early/2016/03/17/044339}, eprint = {https://www.biorxiv.org/content/early/2016/03/17/044339.full.pdf}, journal = {bioRxiv} }