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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 970809, 8 pages
http://dx.doi.org/10.1155/2012/970809
Research Article

Mucus Distribution Model in a Lung with Cystic Fibrosis

1Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
2Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093, USA
3Department of Biology, San Diego State University, San Diego, CA 92182, USA
4School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
5Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA
6Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA

Received 22 June 2012; Revised 1 September 2012; Accepted 6 September 2012

Academic Editor: Reinoud Maex

Copyright © 2012 Sara Zarei et al. 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

Cystic fibrosis (CF) is the most common autosomal recessive disease in Caucasians with a reported incidence of 1 in every 3200 live births. Most strikingly, CF is associated with early mortality. Host in flammatory responses result in airway mucus plugging, airway wall edema, and eventual destruction of airway wall support structure. Despite aggressive treatment, the median age of survival is approximately 38 years. This work is the first attempt to parameterize the distributions of mucus in a CF lung as a function of time. By default, the model makes arbitrary choices at each stage of the construction process, whereby the simplest choice is made. The model is sophisticated enough to fit the average CF patients' spirometric data over time and to identify several interesting parameters: probability of colonization, mucus volume growth rate, and scarring rate. Extensions of the model appropriate for describing the dynamics of single patient MRI data are also discussed.