The Bolouri lab is interested in developing and applying computational systems immunology methods to better understand dysregulated immune cell development and differentiation.
Our approach is inherently multi- and cross-disciplinary. We integrate biological and immunological knowledge with data from high-throughput assays using machine-learning, neural networks and other Data Science methods derived from computer science, engineering, mathematics, statistics and physics.
To get a more complete picture of the system of interest, we typically integrate data from diverse assays (e.g. single-cell RNAseq, CITE-seq, single-cell ATAC-seq, flow and mass cytometry, etc.) with patient clinical data and curated data from databases and publications.
Hamid Bolouri, PhD
Bias-aware data integration
Inflammatory signaling in pediatric Acute Myeloid Leukemia
Regulation of blood homeostasis
Computer Science Reveals Possible Drug Target for Deadly Childhood Leukemia
A Revolutionary Way to Study the Immune System
One goal is to find markers that identify why some people with the virus don’t have symptoms while others get fatally ill. The sickest patients tend to have multiple health issues, which makes it hard to pinpoint the factors related to COVID-19.
The Man Behind the Data: Hamid Bolouri, PhD
Dr. Bolouri joined BRI’s growing Systems Immunology Division this year. Much of his work explores why some people have one immune response to an external or internal event, while other people have a different response. He and his systems immunology colleagues examine the big picture.