Gordon Broderick – Rochester General HospitalDirector, Center for Clinical Systems Biology Group
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 received post-doctoral training in cancer genomics at McGill’s School of Computer Science, and computational biochemistry at the University of Alberta, where he led a high-performance computing effort in modeling the molecular dynamics of intracellular life.
Building on this study of complex emergent behavior in biology, Dr. Broderick’s current research is focused on understanding immune dysfunction and autoimmunity from an integrated systems perspective. Members of his group are applying information and dynamic systems theory to tap into the neuro-endocrine immune communication highway in order to decipher and redirect pathogenic immune conversations with well-chosen and well-timed pharmaceutical messages. This work is funded under awards from the U.S. Department of Defense, the Department of Veterans Affairs and the National Institutes of Health (NIH).
Dr. Broderick also serves as an associate editor to the journal BMC Systems Biology, and editorial board member for the new journal Systems Biomedicine (Taylor & Francis).
Sequential Literature-informed Refinement of Mechanistic Hypotheses in Biology and Medicine
Natural Language Processing now make it possible to extract statements about mechanistic relationships directly from the research community’s understanding of biological processes. By integrating experimental and clinical data with these statements, we can model these processes as logical decision-making circuits. Analysis of the differences in competing candidate models helps identify maximally informative experiments. Recently we developed a model of immune response to upper respiratory infection based on regulatory mechanisms documented in over 20,000 journal publications. We identified 35 candidate models consistent with available data. However, different behaviors were predicted from one model to the next. Consensus among the family of competing models identified the immune mediators IL-4 and CXCL8 as maximally informative measurements. By performing these measurements, the set of feasible models was reduced from 35 to 11. Casting existing data in the context of documented regulatory mechanisms efficiently focuses experimentation in an advance over conventional design of experiments.