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Postdoctoral Fellow
Research group |?RNA Biology and translational regulation
Main supervisor |?Eivind Valen
Co-supervisor |?-
Affiliation |?Department of Biosciences, UiO
Contact |?update email?
Short bio
I completed my bachelor’s degree in Physics with a minor in Biology, followed by a PhD at the Indian Institute of Science (IISc). During my doctoral research, I developed dynamical and stochastic models to study various aspects of positive-sense RNA virus infections, including viral growth, coinfection dynamics, and host–virus interactions.
I am currently a postdoctoral researcher in the Valen Lab at the University of Oslo (UiO) under the DSTrain MSCA programme. In my postdoctoral work, I focus on understanding ribosome dynamics during protein synthesis. Using statistical techniques and large-scale datasets such as ribosome profiling, I aim to decipher how protein-coding sequences and cellular factors influence ribosome movement, translational efficiency, and cellular responses under stress and disease conditions.
Research interests and hobbies
I enjoy working on cross-disciplinary problems at the interface of biology and quantitative sciences. More broadly, I am interested in how mathematical models and computational tools can be used to uncover the mechanisms underlying biological phenomena.
During my PhD, I worked primarily with mechanistic, differential equation-models to study viral infection processes. In my postdoctoral research, I am expanding both the modeling paradigm and the biological scale of my work by focusing on data-driven statistical and machine-learning approaches to study the fundamental process of protein synthesis and its regulation. I am particularly interested in applying concepts from engineering mathematics and statistical learning to analyze large biological datasets and infer underlying biological mechanisms.
Outside of research, I enjoy creating scientific illustrations, trekking, and participating in sports such as swimming and hockey.
DSTrain project
Deciphering the "grammar" of ribosomes: Leveraging statistical models to understand translational regulation
Translation is a tightly regulated and dynamic process where ribosomes synthesize proteins from mRNA transcripts. While the structure and function of ribosomes are well-characterized, the regulatory mechanisms governing ribosome dynamics and translational efficiency remain incompletely understood. Ribosome profiling (RiboSeq) has revolutionized our ability to study translation at codon-level resolution and has revealed that translation is vastly more pervasive than previously believed and that ribosomes traverse mRNAs and stochastically initiate at weaker initiation contexts, gain the capacity to reinitiate after translating shorter regions, ignores start codons through ‘leaky scanning’ and translate overlapping out-of-frame regions. Together, these phenomena form a complex translation landscape in the 5’ UTR region of most genes extending into the coding region. The general principles of this have been studied, but the “grammar” governing the complexity of ribosomal traversal is not well understood.
Here, we propose to use machine learning and deep learning approaches to uncover the implicit “grammar” of translation—the underlying principles governing ribosome behavior across coding and non-coding regions. First, we clean the noisy biological datasets, correcting technical biases to extract features that dictate ribosome progression, stalling, and non-termination events to reveal global patterns in translation dynamics. We will not only focus on the annotated genes but also explore the vast number of translational events in the “dark transcriptome” to identify novel translated regions and cryptic peptides that are involved in cellular regulation and disease progression.
Publications
DSTrain publications
?Google scholar Harsh Chhajer
?ORCHID Harsh Chhajer
Previous publications
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