Research
Rhondene Wint
PhD Candidate
Dept. of Quantitative and Systems Biology
University of California-Merced
Interests: functional genomics, RNA, neurobiology, bioinformatics, machine learning
Biography
I am currently a 5th year PhD candidate in the Quantitative and Systems Biology department at the University of California-Merced under the co-mentorship of Dr. Michael Cleary and Dr. David Ardell . I hold a secondary affiliateship in the Fungal Genomics Group (lead PI Dr.Igor Grigoriev) at the Lawrence Berekely National Laboratory’s Joint Genome Institute. I earned my B.Sc. in Biological Sciences (magna cum laude, 2014) from Northern Caribbean University , where I also taught introductory biology labs from 2011-2014. From 2014-2017, I taught the equivalent of AP-level biology, physics and chemistry to post-secondary students.
Here is my guest interview on the podcast Impact Learning hosted by Maria Xenidou(PhD) where I discuss my education journey and the power of self-directed learning for future-proofing oneself.
Research: RNA on the Brain
I employ experimental and computational methods to study RNA biology at the developmental and evolutionary timescales.
Main Project: Codon optimality and tRNA dynamics during neural differentiation
A major goal of neurobiology is to unravel the molecular events that orchestrate neural cell-fate and regulation.
By using techniques from functional genomics and classical genetics, my research aims to unravel how the dynamic regulation of transfer RNAs (tRNAs) between neural progenitors and mature neurons contribute to cell-type specificity and function.
Evolution of Codon Optimality in Kingdom Fungi/Machine Learning for Codon Optimality
Studies in model fungi agree on codon usage bias as an adaptation for fine-tuning gene expression; however, such knowledge is lacking for most other fungi. In collaboration with the JGI Fungal Genomics group, I bioinformatically characterized the codon and tRNA usage of hundreds of species distributed across the 6 major phyla of Kingdom Fungi. I employed phylogenetic comparative methods to elucidate the mode and tempo of the macroevolution of codon usage patterns. As an application, I went on to develop a sequence-to-expression neural network tool, Codon2Vec , for predicting highly expressed mRNAs based on codon composition (accepted to Mol.Bio&Evo, Dec. 2021).
