Saul Vega Sauceda
Eric and Wendy Schmidt Center Funded Research and Innovation Scholar
Improving Gene Regulatory Network Inference from Single-Cell RNA Data Using Data Diffusion-Based Imputation
Electrical Engineering and Computer Science
- Computational Biology
Gene regulatory network inference (GRNi) can help predict regulatory interactions responsible for cellular degeneration and disease. The goal of the project will be to regress the absolute RNA abundances of transcription factors (TFs) on the transcriptional state of any given cell. Our research will analyze transcriptomic data collected from brain tissue to propose a TF cocktail to treat Alzheimer’s disease. We will evaluate current computational approaches for GRNi and either modify them or develop a new algorithm, incorporating our previous work of imputation of scRNA-seq counts of TFs. We will explore the proposed GRNs in Alzheimer’s to search for transcriptional perturbations that we can target.
I am participating in SuperUROP to deeply explore modern challenges in medicine and gain extensive research experience before graduate school. I have researched in other MIT labs on related projects in chemoinformatics and bioinformatics. I am excited to learn more modern methods in computational biology to develop a tool for an impactful project that has the potential to be critical in finding markers or targets of neurodegenerative diseases.