Current Position:
Assistant Professor in the Department of Biochemistry and Biophysics
Institution:
University of California, San Francisco (Previously at The Rockefeller University)
Discipline:
Genetics & Genomics
Recognized for: Unique computational and experimental approaches to examine regulatory networks in cancer biology
Areas of Research Interest and Expertise: Cancer Metastasis; Systems Biology; Post-Transcriptional Regulation; Genomic Tool Development
Biography:
PhD, Molecular Biology, Princeton University
BS, Biotechnology, University of Tehran, Iran
Dr. Hani Goodarzi is a postdoctoral fellow in Sohail Tavazoie's Laboratory of Systems Cancer Biology at The Rockefeller University. He received his Ph.D. under the mentorship of Saeed Tavazoie in Molecular Biology from Princeton University. As a graduate student, he generated the first map of regulatory perturbations across different human cancers, providing a systematic approach for understanding the cancer transcriptome.
At The Rockefeller University, Dr. Goodarzi focused on studying the molecular basis of cancer metastasis. With an interdisciplinary background in computational and experimental cancer biology, he implemented an approach to determine what causes cancer cells to metastasize. He developed an algorithm that identifies structural regulatory elements in RNA sequences that independently of their coding capacity also impact the RNA life-cycle through interactions with key regulators in the cell. Applying this computational platform to data collected from cells with differing metastatic propensities, he identified a novel regulatory pathway that contributes to breast cancer metastasis.
More recently, he also played a pivotal role in discovering and characterizing a novel class of small regulatory RNAs known as tRNA-derived RNA fragments that play roles in cancer progression. Dr. Goodarzi’s goal is to identify and characterize the regulatory networks that are co-opted by aggressive cancer cells.
“My overarching goal is to combine large-data analytical frameworks with multi-scale modeling and rigorous experimentation to arrive at a more detailed and realistic picture of regulatory networks and their roles in human disease.”
Key publications