System Dynamics Applications to Physiology

Three articles are recommended to introduce you to the potential usefulness of system dynamics for physiological systems. The first article is a good opening example since the authors limit their model to two stock variables. The second article describes a more complex model with eleven stock variables. It is based on currently accepted physiological concepts and was designed so that each parameter has a direct physiological counterpart, thus making experimental evaluation possible. The third article provides an overview of the potential for system dynamics.

Bush (1985) used system dynamics to study plasma water loss from patients burned over a large portion of their body. The model used one stock to simulate plasma water in the body and a second stock to keep track of the reservoir of prescribed water used for intravenous treatment. The model was published as a first step toward a detailed study of burn patients which might lead to better guidelines for fluid therapy in general and possibly to special rules to be followed in well defined circumstances.

Hansen and Bie (1987) describe a whole-body model of the microvascular and lymphatic transfer of fluid and plasma protein in dogs. The model used four stocks to keep track of plasma water, interstitial water, lymphatic water and cellular water. Three stocks were used to keep track of plasma protein, interstitial protein and lymphatic protein. The final three stocks simulated the plasma sodium, interstitial sodium and lymphatic sodium. The model simulations showed good agreement with experimental data from conscious dogs in steady state during two protocols of acute hypotonic overhydration.

Gallaher (1996) explains that many biomedical problems (such as diabetes, hypertension and drug tolerance) are fundamentally problems of biological control systems. He argues that system dynamics is ideally suited for the analysis and interpretation of these systems, and he sets forth guidelines to promote a field of "Biological System Dynamics."

Additional examples include the work by Sturis (1991), Smith (1996) and Hargrove (1998). Sturis explains a computer simulation model to illuminate the possible causes of ultradian ocsillations in human insulin secretion. These are long cycles (around 120 minutes) that may appear in humans during continuous enteral nutrition, after meal ingestion and during constant glucose infusion. The model includes state variables for plasma insulin, for insulin in the interstitial fluid and for the amount of glucose in the glucose space. Sturis found that the sustained oscillations could be attributed to the feedback between glucose and insulin. The dynamics were found to be heavily influenced by a 30-45 minute time delay for the effect of insulin on glucose production and a sluggish effect of insulin on glucose utilization.

Smith (1996) uses Stella to simulate the process of bone remodeling in which the surface of adult bone is constantly renewed. The model simulates the "journey" of bone remodeling with separate stocks assigned to resorption, reversal, and formation. The model may be used to shed light on the imbalance of bone resorption and formation that is often manifested by a pathological state such as osteoporosis. The model is documented in a unique manner in Smith's (1996) MS thesis to serve as a tool for medical education.

James Hargrove, Smith's professor at the University of Georgia, includes a "Stochastic Model of Bone Remodeling and Osteoporosis" in his book on Dynamic Modeling in the Health Sciences (Hargrove 1998). Other chapters are devoted to a variety of topics including human cholesterol, circadian rhythms and tumor progression. (Also, his chapter on genotypes and phenotypes is useful reading if you plan to work on the industrial melanism exercises.)