Event Title

Exploring gene-level controls of protein expression dynamics

Research Mentor(s)

Dan Pollard

Description

Genetic variation among individuals can influence the four components of protein expression dynamics: rates of transcription, mRNA decay, translation, and protein decay. This contributes to trait variation. Much research has centered on how genetic variation influences rates of transcription and mRNA decay, though relatively little has focused on how genetic variation influences the other two components of protein expression dynamics, i.e. the rates of translation and protein decay. I hope to provide insight by studying 22 strains of Saccharomyces cerevisiae, where I model how variation in each gene explains between-strain differences in its rates of protein translation and decay. This will lay a foundation for similar studies in other organisms. A better understanding of how genetic variants influence the rates of protein translation and decay will be useful in piecing together evolutionary history, in engineering genes, in unraveling the genetic basis of health, and in learning more about disease. The linear mixed-effects regression model I hope to build will predict gene-specific rates of translation and protein decay as a function of different measures of codon bias and mRNA secondary structure stability.

Document Type

Event

Start Date

15-5-2019 9:00 AM

End Date

15-5-2019 5:00 PM

Location

Carver Gym (Bellingham, Wash.)

Department

Biology

Genre/Form

student projects, posters

Type

Image

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Language

English

Format

application/pdf

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May 15th, 9:00 AM May 15th, 5:00 PM

Exploring gene-level controls of protein expression dynamics

Carver Gym (Bellingham, Wash.)

Genetic variation among individuals can influence the four components of protein expression dynamics: rates of transcription, mRNA decay, translation, and protein decay. This contributes to trait variation. Much research has centered on how genetic variation influences rates of transcription and mRNA decay, though relatively little has focused on how genetic variation influences the other two components of protein expression dynamics, i.e. the rates of translation and protein decay. I hope to provide insight by studying 22 strains of Saccharomyces cerevisiae, where I model how variation in each gene explains between-strain differences in its rates of protein translation and decay. This will lay a foundation for similar studies in other organisms. A better understanding of how genetic variants influence the rates of protein translation and decay will be useful in piecing together evolutionary history, in engineering genes, in unraveling the genetic basis of health, and in learning more about disease. The linear mixed-effects regression model I hope to build will predict gene-specific rates of translation and protein decay as a function of different measures of codon bias and mRNA secondary structure stability.