Gordon Broderick

Vaccine Formulation & Delivery Group

Dr. Broderick is Principal Investigator and Lead of the newly formed Mathematical Immunology and Immunodynamics Lab at VIDO as well as being Adjunct Professor in the Department of Mathematics and Statistics and faculty investigator with the Centre for Quantum Topology and Its Applications (quanTA) at the University of Saskatchewan.

An engineer by training, Dr. Broderick, holds a doctorate in chemical engineering from the University of Montreal as well as a master's in chemical engineering and an undergraduate in mechanical engineering, both from McGill University. He completed post-doctoral training at McGill’s School of Computer Science in cancer genomics and a research fellowship in computational biochemistry at the University of Alberta, where he led a high-performance computing effort in modeling the molecular dynamics of intracellular life.  In 2013, a tenured associate professor of medicine at the University of Alberta, he moved his program to the US to drive the development of large computing approaches directed at understanding the immune system’s design principles and algorithmic programming. Building on a background in team-based science, Dr. Broderick’s current research efforts focus primarily on the emerging field of computational immunology and on how an integrated systems perspective might improve our understanding of endocrine-immune dysfunction, autoimmunity, vaccine responsiveness and complex persistent post-infectious illness (e.g. Long-COVID). His work has been funded under a number of grants from the U.S. Department of Defense (CDMRP), the National Institutes of Health (NIH) and the U.S. Department of Veterans Affairs. In addition to serving as an associate editor and guest editor on a number of scientific journals, he sits on Advisory Board for the new Cell Press journal Patterns.

After over a decade in the US, Dr. Broderick recently moved his research back to Canada to join VIDO in developing new initiatives in translational and computational vaccine medicine. He expects this research will bring together a truly cross-disciplinary mix of investigators from the computational, clinical and basic life sciences with the goal of leveraging deep computing in developing novel vaccine strategies and immune therapies for complex illnesses that are both safe and effective.

Research Interests:

  • Endocrine-immune regulatory network structure and complex dynamics
  • Assembly of biological networks from prior knowledge extracted from text and schema, as well as by leveraging large Language Models
  • Mathematical models of biological networks, large-scale optimization for parameter identification and intervention design
  • Large-scale computer simulation of endocrine-immune responses to infectious challenge
  • Migration of optimization algorithms to quantum computing platforms 

Current Projects:

  • Mechanisms of poor vaccine response and increased susceptibility to infection in early childhood and neonates (University of Rochester, Rochester Regional Health)
  • Mechanisms of frequent infectious exacerbation in Chronic Obstructive Pulmonary Disease (COPD) (University at Buffalo, US Department of Defense)

Webcasts:

Identifying New Knowledge by Applying State-of-the-art Methods in AI and Machine Learning ConTech 2018, London UK

Iterative Translation of Big Knowledge into Improved Care (see time 1:29:39)

CFS/ME and Gulf War Illness Patient Conference 2013 (see time 19:45)

Publications