Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae
| dc.contributor.author | Jalihal, Amogh Prabhav | en |
| dc.contributor.committeechair | Tyson, John J. | en |
| dc.contributor.committeechair | Murali, T. M. | en |
| dc.contributor.committeemember | Kraikivski, Pavel | en |
| dc.contributor.committeemember | Hauf, Silke | en |
| dc.contributor.committeemember | Chen, Jing | en |
| dc.contributor.department | Genetics, Bioinformatics, and Computational Biology | en |
| dc.date.accessioned | 2020-07-09T08:01:01Z | en |
| dc.date.available | 2020-07-09T08:01:01Z | en |
| dc.date.issued | 2020-07-08 | en |
| dc.description.abstract | In eukaryotes, distinct nutrient signals are integrated in order to produce robust cellular responses to fluctuations in the environment. This process of signal integration is attributed to the crosstalk between nutrient specific signaling pathways, as well as the large degree of overlap between their regulatory targets. In the budding yeast Saccharomyces cerevisiae, these distinct pathways have been well characterized. However, the significant overlap between these pathways confounds the interpretation of the overall regulatory logic in terms of nutrient-dependent cell state determination. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling pathway in budding yeast, focussing on carbon and nitrogen signaling. We build a computational model of this pathway to reconcile the available experimental data with our proposed molecular mechanism. We evaluate the robustness of the model fit to data with respect to the variations in the values of kinetic parameters used to calibrate the model. Finally, we use the model to make novel, experimentally testable predictions of transcription factor activities in mutant strains undergoing complex nutrient shifts. We also propose a novel framework, called BoolODE for utilizing published Boolean models to generate synthetic datasets used to benchmark the performance of algorithms performing gene regulatory network inference from single cell RNA sequencing data. | en |
| dc.description.abstractgeneral | An important problem in biology is how organisms sense and adapt to ever changing environments. A good example of an environmental cue that affects animal behavior is the availability of food; scarcity of food forces animals to search for food-rich habitats, or go into hibernation. At the level of single cells, a range of behaviors are observed depending on the amount of food, or nutrients present in the environment. Moreover, different types of nutrients are important for different biological functions in single cells, and each different nutrient type will have to be available in the right quantities to support cellular growth. At the subcellular level, intricate molecular machineries exist which sense the amounts of each nutrient type, and interpret this information in order to make a decision on how best to respond. This interpretation and integration of nutrient information is a complex, poorly understood process even in a simple unicellular organism like the budding yeast. In order to understand this process, termed nutrient signaling, we propose a mathematical model of how yeasts respond to nutrient availability in the environment. Our model advances the state of knowledge by presenting the first comprehensive mathematical model of the nutrient signaling machinery, accounting for a variety of experimental observations from the last three decades of yeast nutrient signaling. We use our model to make predictions on how yeasts might behave when supplied with different combinations of nutrients, which can be verified by experiments. Finally, the cellular machinery that helps yeasts respond to nutrient availability in the environment is very similar to the machinery in cancer cells that causes them to grow rapidly. Our proposed model can serve as a stepping stone towards the construction of a model of cancer's responses to its nutritional environment. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:26301 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99306 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Yeast | en |
| dc.subject | Signaling | en |
| dc.subject | Mathematical Modeling | en |
| dc.subject | Boolean Models | en |
| dc.subject | RNAseq | en |
| dc.title | Mathematical modeling of macronutrient signaling in Saccharomyces cerevisiae | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Genetics, Bioinformatics, and Computational Biology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |