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Matthew Mulé

First Name
Matthew
Last Name
Mulé
Photo
Matthew Mulé
Category
Computational Biology
Research Interest

Computational systems biology, Immunology

Scholar Type
NIH Cambridge Scholar MD/PhD
Degrees

B.S. Biology, Tufts University, 2014
Medical student at University of North Carolina-Chapel Hill (In progress)

Student's Research

Matt is an MD-PhD student in UNC Chapel Hill’s Medical Scientist Training Program. Prior to medical school, Matt worked at NIH under the mentorship of Dr. Christopher Hourigan to develop biomarkers of residual disease and immunotherapy response for Acute Myeloid Leukemia (AML). He would publish his main project (PMID: 27544285) which involved residual disease detection in the setting of autologous bone marrow transplantation. Matt went on to translate this research into the clinic by developing a PCR-based diagnostic test which served as the endpoint of a clinical trial at NIH.

Matt graduated cum laude from Tufts University in 2014, earning Highest Thesis Honors for two years of work in Dr. Andrew Camilli’s lab. He traces his interest in immunology to time spent in the Camilli lab working with a postdoctoral fellow, Dr. Mara Shainheit. Together they would reveal mechanisms by which pneumococcus evades host immunity by regulating its anti-phagocytic capsule (PMID: 26111465). He also researched Actinomyces with Dr. Lori Bergeron at New England College (PMID: 26685151). He was awarded the Nathan T. Gantcher Scholarship and an INBRE Fellowship as an undergraduate and was awarded the University Cancer Research Fund Scholarship from UNC in 2017. Matt is focused on understanding the dynamics of human immunopathology by integrating time series data across different omic platforms to build diagnostic tools and understand the biology behind clinical outcomes in autoimmune disease, infection and cancer. 

Institute Center Mentor
National Institute of Allergy and Infectious Diseases (NIAID)
Mentors

Dr. John Tsang (NIAID) and 
Prof. Ken Smith (Cambridge)

Homepage Description
Matt is focused on understanding the dynamics of human immunopathology by integrating time series data across different omic platforms to build diagnostic tools and understand the biology behind clinical outcomes in autoimmune disease, infection and cancer. 
Entry Year
2018
Thesis Pending
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