A Physician's View of Complexity, the Origins of Order, Health and Disease
Meakins-Christie Laboratories, McGill University
(November 8, 2006 6:30 PM - 7:30 PM)
Two relatively unexplored features characterize physiologic systems: 1) They are complex, non-linear and dynamic which results in emergent phenomena that can neither be predicted nor explained by examining their component parts in isolation; 2) they become highly ordered during fetal development and throughout the course of Darwinian evolution in apparent violation of the second law of thermodynamics. It follows that interconnections among the parts must play a role in emergent phenomena and the origin of order. How this is accomplished through the nature and number of interconnections has been explored by Kauffman(1). Explanation of increasing order in spite of the second law was achieved by Prigogine(2) who showed that order can spontaneously appear in systems close to thermodynamic equilibrium if they are made to dissipate energy which increases order by displacing them far from equilibrium and decreasing entropy production rate. The approaches of Kauffman and Prigogine have not been combined or reconciled and this needs to be done in order to have a more complete understanding of health and how it breaks down in disease. If energy dissipation in a system is too little or too much and/or if the nature or number of a system's interconnections is altered malfunction results. Although how this occurs is rather obscure, fluctuations in time and space are a common feature of complex systems. Many ways have been used to characterize these fluctuations but few have yet proven beneficial to medicine. Another common feature of complex systems is that, unlike many physical systems, the future can only be assessed by statistical probability. Physicians deal inadequately with uncertainty. Prognosis is part of the art of medicine and is the least scientific part of our profession. Yet the development of statistical mechanics to quantify probabilities in quantum mechanics has the potential of making prognosis more precise. Of the many ways to characterize fluctuations in complex systems, power laws are ubiquitous(3). They have powerful predictive properties; e.g., the Gutenberg Richter Law can predict the probability of an earthquake of any magnitude occurring over any region of the earth's surface over any given time interval with a high degree of certainty. Can power laws make prognosis quantitative? Although the future of physiology is uncertain, I predict our understanding of health will depend on uncovering the secrets of energy-dissipating, interconnected complex biological systems. Precise knowledge of how abnormal interconnections and energy dissipation leads to dysfunction is essential in the understanding of disease and should lead to more precise prognostication.
- S. Kauffman. The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press, 1993.
- I. Prigogine and I Stengers. Order Out of Chaos: Man's New Dialogue with Nature. New York: Bantm Books, 1984
- P. Bak. How Nature Works: The Science of Self-Organized Criticality. New York: Springer-Verlag, 1996.