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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 676015, 9 pages
http://dx.doi.org/10.1155/2012/676015
Review Article

Understanding Immunology via Engineering Design: The Role of Mathematical Prototyping

1Department of Chemical Engineering and Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 25606, USA
2Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 25606, USA
3Department of Computer Sciences, Mathematics, and Engineering, Shepherd University, Shepherdstown, WV 25433, USA

Received 15 June 2012; Accepted 2 August 2012

Academic Editor: Francesco Pappalardo

Copyright © 2012 David J. Klinke and Qing Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A major challenge in immunology is how to translate data into knowledge given the inherent complexity and dynamics of human physiology. Both the physiology and engineering communities have rich histories in applying computational approaches to translate data obtained from complex systems into knowledge of system behavior. However, there are some differences in how disciplines approach problems. By referring to mathematical models as mathematical prototypes, we aim to highlight aspects related to the process (i.e., prototyping) rather than the product (i.e., the model). The objective of this paper is to review how two related engineering concepts, specifically prototyping and “fitness for use,” can be applied to overcome the pressing challenge in translating data into improved knowledge of basic immunology that can be used to improve therapies for disease. These concepts are illustrated using two immunology-related examples. The prototypes presented focus on the beta cell mass at the onset of type 1 diabetes and the dynamics of dendritic cells in the lung. This paper is intended to illustrate some of the nuances associated with applying mathematical modeling to improve understanding of the dynamics of disease progression in humans.