Research Project Title:
Learning Discrete Stochastic Models of Eukaryotic Transcription
abstract:The goal of my project is to accurately predict how transcription factors configure chromatin and facilitate transcription across the genome. For background, transcription factors are sequence-specific DNA-binding proteins that regulate transcription, which is the process of turning a segment of DNA into RNA. This process is often modeled with discrete stochastic simulations whose parameters need to be learned. I aim to develop new methods for fitting the parameters of these discrete simulations, and then apply them to build a more accurate kinetic model of the effect of perturbing the amount of transcription factors in a cell, with applications to cellular programming.
I am participating in SuperUROP because I am very interested in new scientific computing techniques and their potential to accelerate scientific discovery. I am excited to apply my background in mathematics and computer science, and also to learn a lot about data science and computational biology.