1. ¹Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
2. ²Metrum Institute, Tariffville, Connecticut, USA
3. ³Department of Pharmacology, Georgetown University Medical Center, Washington, District of Columbia, USA

Correspondence: RR Bies, (rrb47@pitt.edu)

Received 9 January 2008; Accepted 15 February 2008; Published online 26 March 2008.

Abstract

"Being able to predict things or to describe them, however accurately, is not at all the same thing as understanding them."

David Deutsch¹

The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.