Raul Rabadan

Raul Rabadan


Associate Professor, Department of Systems Biology


Department of Systems Biology
Center for Computational Biology and Bioinformatics
Center for Cancer Systems Therapeutics
Department of Biomedical Informatics
JP Sulzberger Columbia Genome Center


(212) 851-5141

Raul Rabadan is an associate professor in the Department of Systems Biology and the Department of Biomedical Informatics. He leads an interdisciplinary team that develops and implements mathematical and computational tools to extract biologically and clinically relevant information from large data sets, with interests in infectious diseases and cancer. The Rabadan Lab is developing tools to analyze genomic data to improve understanding of the molecular biology, population genetics, evolution, and epidemiology of viruses, particularly influenza viruses. He is also using next-generation sequencing technologies to identify somatic mutations that contribute to the development of cancerous tumors. Studies to date have focused on liquid tumors including B-cell lymphomas and lymphoblastic leukemias.

Dr. Rabadan is also co-principal investigator for the Columbia University Center for Topology of Cancer Evolution and Heterogeneity.

More News


Columbia Awarded NCI Center for Cancer Systems Biology
The Center for Cancer Systems Therapeutics (CaST) is developing a framework that can account for the dynamic nature of cancer and use this knowledge to disrupt the programs that maintain tumor survival.
Study of Glioblastoma Tumor Evolution Reveals Strategies Against Advanced Disease
Genetically distinct populations of cells appear to drive malignancy before and after therapy. The findings provide insights into GBM drug resistance and how it might be overcome.
Graduate Students Invent Technique for Reprogramming Translation
MD/PhD students Andrew Anzalone and Sakellarios Zairis engineered RNA motifs capable of inducing ribosomal frameshifting. Their method could offer new opportunities for synthetic biology.
Columbia Investigators Awarded New NCI Physical Sciences in Oncology Center
The Center for Topology of Cancer Evolution and Heterogeneity will combine new mathematical approaches and single-cell experimental technologies to study cellular diversity in solid tumors.
Connections Found between Mendelian Diseases and Cancer
Using electronic health records representing over 110 million patients, investigators in the Rabadan Lab show that comorbidity of Mendelian diseases and cancer can be tied to genetic changes seen in both diseases.
Rare, Deadly Lymphoma Demystified
The first systematic study of genomes of patients with ALK-negative anaplastic large cell lymphoma, an aggressive form of non-Hodgkin’s lymphoma, shows that many cases are driven by alterations in the JAK/STAT3 cell signaling pathway.
Distinguishing Patterns of Tumor Evolution in Chronic Lymphocytic Leukemia
A new computational method developed in the laboratory of Raul Rabadan has made it possible to determine the order in which mutations occur in cancer, providing insights into disease progression.
A Topological Approach to Modeling Evolution
Charles Darwin visualized the differentiation of species like the branches of a tree, but recent genomic studies suggest that this model is insufficient to describe evolution at the molecular level.
Study Reveals Genes That Drive Glioblastoma
Using a new statistical method developed in the lab of Raul Rabadan, a Columbia University team identified 18 new genes responsible for this aggressive form of brain cancer. Some of these genes could be targeted with existing FDA-approved drugs.
Study Identifies a Genetic Cause of Glioblastoma
A paper published by Columbia University Medical Center researchers in the journal Science has determined that some cases of glioblastoma, the most aggressive form of primary brain cancer, result from the fusion of the genes FGFR and TACC.
Tracking the H1N1 Pandemic Virus
A group led by Raul Rabadan has been studying the evolution of influenza viruses and the origins of flu pandemics by analyzing large data sets that contain genomic information.