Upcoming Event: Oden Institute Seminar

An Integrative Approach to Predictive Modeling of Translation

Can Cenik, Associate Professor, UT Austin MBS

Abstract

We have compiled measurements of translation from more than 3,500 experiments and introduced the concept of translation efficiency covariation (TEC), revealing that transcripts associated with shared biological functions and those that are members of the same protein complexes exhibit TEC. We leveraged our expansive compendium of translation efficiency measurements to develop a deep neural network model, called RiboNN, capable of predicting mRNA translation rates across numerous cell types based solely on the full-length mRNA sequence. RiboNN is the most accurate predictive tool of translation efficiency, offering substantial improvements over existing methods. RiboNN can evaluate the impact of genetic variants in the human population, providing insight into diseases driven by abnormal mRNA translation. This breakthrough has implications for bioengineering applications, genetic diagnostics as well as the design and optimization of mRNA therapies.

Biography

Upon graduating magna cum laude with highest honors in applied mathematics from Harvard College, I pursued a PhD in genetics and genomics. My PhD work identified the role of 5’UTR introns in a novel mRNA export pathway and involved developing computational approaches to determine the targets of RNA binding proteins. During my postdoctoral research at Stanford University, I characterized variation in translation and its genetic determinants among human populations. I started my independent research group at UT Austin in August 2018 as a recipient of a CPRIT Recruitment Award. My lab’s long-term goal is to develop the necessary computational and experimental framework for predictive models that explain how cells determine their protein abundance.