Predicting the translation efficiency of messenger RNA in mammalian cells
Vikram Agarwal
Head of mRNA Platform Design Data Science
mRNA Center of Excellence
Sanofi
Tuesday, September 9, 2025
11:00 a.m.
In-person: C2-361
Abstract: How translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we generated a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types from 3,819 ribosomal profiling datasets. We developed RiboNN, a state-of-the-art multitask deep convolutional neural network, and classic machine learning models to improve TE prediction in hundreds of cell types from sequence-encoded mRNA features.
Vikram Agarwal completed his Ph.D. in Dr. David Bartel's lab at MIT, and his post-doc in Dr. Jay Shendure's lab at the University of Washington. There, he applied deep learning methods and massively parallel reporter assays to investigate the mechanisms of transcriptional gene regulation, further building upon these approaches at Calico Life Sciences. He is currently the Head of mRNA Platform Design Data Science at the mRNA Center of Excellence at Sanofi, where he is applying machine learning and deep learning methods towards the design of enhanced mRNA therapeutics.