Systems Analysis and Translational Research in Metabolism

Friday, May 31, 2013 - 2:00pm
Fung Auditorium | Powell-Focht Bioengineering Hall
Richard N. Bergman, Ph.D.

Alfred Jay Firestein Chair in Diabetes Research
Cedars-Sinai Medical Center

Systems Analysis and Translational Research in Metabolism


The term “systems analysis” in biology has had multiple definitions.  Recently it has referred to the use of great computing power to ascertain and analyze immense data sets, generally related to the genome, the proteome and/or the metabolome.  By this approach it is generally assumed that the availability of massive data and computing power will lead to greater understanding of metabolic regulation; the approach is sometime referred to as “hypothesis-generating.”  But, the term systems analysis had an earlier definition – the expression of physiological knowledge in mathematical models which in themselves represent specific hypotheses which can be carefully tested and revised to yield a comprehensive representation of physiological understanding.   This earlier approach has much in common with the traditional scientific method in which hypotheses and experiments are synergized to yield important and usable physiological understanding.   We used careful measurements made in animals to examine the relationship among plasma measurements during physiological perturbations.  We imposed the concept of optimal simplicity – a mathematical representation which could account for the data with the simplest physiological representation.  The model, of course, explained the data used for its construction; more important it made specific predictions which were amenable for experimental test.   From the model were able to define a series of important clinical variables: insulin resistance and functionality of the beta-cells of the endocrine pancreas.  A single parameter emanating from the model – the “disposition index” became very useful because it was shown to be the strongest predictor of conversion from pre-diabetes to full blown Type 2 diabetes mellitus.  It was also apparently coded by several genetic variants.  The model was recently modified to include the role of the liver to phosphorylate plasma glucose to lactate – this modification allows us to examine the role of insulin-independent glucose utilization in the pathogenesis of Type 2 diabetes.  Other outcomes of the model include understanding of insulin transport across capillary endothelium which can be altered in the obese, insulin resistant state.  These studies of carbohydrate metabolism are a case study of hypothesis-based systems analysis, and demonstrate that the more traditional definition of “systems analysis,” – hypothesis based, rather than hypothesis-generating remains a powerful approach to understanding physiological regulation, and how it can be used to advance clinical medicine.

Dr. Richard Nathan Bergman holds an undergraduate degree in Engineering from Case-Western Reserve University (Case Alumni Scholar) and the Ph.D. in Physiology from the University of Pittsburgh. Bergman was the Keck Foundation Chair at the USC Keck School of Medicine, and was Professor of Medicine and Biomedical Engineering and Chairman of Physiology/Biophysics for 17 years. He is now Director of the new Cedars-Sinai Research Institute of Diabetes and Obesity. Dr. Bergman is a pioneer in systems biology of metabolism. Richard Bergman has won several honors for his work, including the "Outstanding Diabetes Investigator (Lilly Award)" from the American Diabetes Association, the "Lifetime Achievement Award" from the Los Angeles ADA, the "TOPS" award from the Obesity Society (NAASO), a MERIT award from the NIH.  He has delivered numerous prestigious lectureships, including the Mosenthal Lectureship, the Schwartz Lectureship, and the Berson Lectureship of the Experimental Biology Society, the Taft Lecture.  Dr. Bergman was Editor-in-Chief of the Journal Obesity from 2007-2012.  Dr. Bergman was awarded the Banting Medal from the American Diabetes Association and delivered the Banting Lecture.