John Higgins received an AB in Biology from Princeton University and then worked as a computer software engineer at MathWorks, Vermeer Technologies, and Microsoft. He received an MD from the Harvard-MIT Division of Health Sciences and Technology and an SM from MIT. He has done doctoral research with David Altshuler at the Whitehead/MIT Center for Genome Research and post-doctoral research with L. Mahadevan in Applied Mathematics at Harvard University. He completed a Residency in Clinical Pathology at Brigham and Women’s Hospital and is a board-certified clinical pathologist.

Research Summary

My research focuses on dynamics of human pathophysiologic processes. I am interested in developing mathematical descriptions of complex human disease phenotypes and how they change over time. Pathophysiology may be described at the molecular, cellular, tissue, and organismal levels and may show clinically significant variation over time scales ranging from seconds to years. My research combines clinical and pathophysiologic insight with dynamical systems theory to pursue two goals: (1) advance fundamental understanding of the dynamics of human pathophysiology, and (2) improve patient diagnosis, monitoring, and treatment.

Current work includes cellular-level fluid dynamic modeling of vaso-occlusion in sickle cell disease and patient-level stochastic modeling of immunologic response following blood transfusion. Experimental approaches utilize microfluidics, flow cytometry, computational video processing, simulation, and large-scale analysis of medical and clinical laboratory databases.

Selected References

Higgins JM, Eddington DT, Bhatia SN, Mahadevan L.  Sickle cell vaso-occlusion and rescue in a microfluidic device. Proceedings of the National Academy of Sciences of the United States of America 104, 20496-20500 (2007).  [See commentary in Science Editors’ Choice:  Science. 2007 Dec 14; 318(5857): 1699.]

Higgins JM and Sloan SR.  Stochastic Modeling of Human RBC Alloimmunization:  Evidence for a Distinct Population of Immunologic Responders.  Blood. 2008 Sep 15; 112( 6): 2546-53.  [See Blood editorial commentary: Blood 2008 Sep 15;112(2):2180-1.]

Higgins JM, Eddington DT, Bhatia S, Mahadevan L.  Statistical Dynamics of Flowing Red Blood Cells by Morphological Image Processing.  PLoS Computational Biology 2009 Feb;5(2):e1000288.