Abstract
Cells interact with their environments by sensing, processing, and responding to stimuli. This thesis models budding yeast cells as signal processors to understand the mechanisms of cellular decision making. Conditions for the cell at all levels are in flux, from the environment, inside the cell, and in the cell’s evolved mechanisms. This uncertainty contributes to noise in the cell’s signal processing and its decision-making processes. Post-transcriptional control of noise is demonstrated by fluorescent quantification of fusion proteins expressed with and without their 3’ untranslated regions and by CRISPR-enabled mutation of putative autoregulatory recognition motifs.
Noise was quantified at the population level by flow cytometry and time resolved by live timelapse microscopy. Feedback loops governing abundance dynamics were simulated by mathematical models, with and without feedback. Post-transcriptional regulation represents a layer of control, where the increasing list of conventional and unconventional RNA-binding proteins can tune gene expression sensitive to the requirements of the cell. Such feedback is an evolved regulatory characteristic that enables the cell to interact with uncertainty, rather than being merely subject to it.