Document Detail


Mesoscopic modeling as a starting point for computational analyses of cystic fibrosis as a systemic disease.
MedLine Citation:
PMID:  23570976     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Probably the most prominent expectation associated with systems biology is the computational support of personalized medicine and predictive health. At least some of this anticipated support is envisioned in the form of disease simulators that will take hundreds of personalized biomarker data as input and allow the physician to explore and optimize possible treatment regimens on a computer before the best treatment is applied to the actual patient in a custom-tailored manner. The key prerequisites for such simulators are mathematical and computational models that not only manage the input data and implement the general physiological and pathological principles of organ systems but also integrate the myriads of details that affect their functionality to a significant degree. Obviously, the construction of such models is an overwhelming task that suggests the long-term development of hierarchical or telescopic approaches representing the physiology of organs and their diseases, first coarsely and over time with increased granularity. This article illustrates the rudiments of such a strategy in the context of cystic fibrosis (CF) of the lung. The starting point is a very simplistic, generic model of inflammation, which has been shown to capture the principles of infection, trauma, and sepsis surprisingly well. The adaptation of this model to CF contains as variables healthy and damaged cells, as well as different classes of interacting cytokines and infectious microbes that are affected by mucus formation, which is the hallmark symptom of the disease (Perez-Vilar and Boucher, 2004) [1]. The simple model represents the overall dynamics of the disease progression, including so-called acute pulmonary exacerbations, quite well, but of course does not provide much detail regarding the specific processes underlying the disease. In order to launch the next level of modeling with finer granularity, it is desirable to determine which components of the coarse model contribute most to the disease dynamics. The article introduces for this purpose the concept of module gains or ModGains, which quantify the sensitivity of key disease variables in the higher-level system. In reality, these variables represent complex modules at the next level of granularity, and the computation of ModGains therefore allows an importance ranking of variables that should be replaced with more detailed models. The "hot-swapping" of such detailed modules for former variables is greatly facilitated by the architecture and implementation of the overarching, coarse model structure, which is here formulated with methods of biochemical systems theory (BST). This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
Authors:
Eberhard O Voit
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2013-04-06
Journal Detail:
Title:  Biochimica et biophysica acta     Volume:  1844     ISSN:  0006-3002     ISO Abbreviation:  Biochim. Biophys. Acta     Publication Date:  2014 Jan 
Date Detail:
Created Date:  2013-12-09     Completed Date:  2014-03-18     Revised Date:  2014-03-19    
Medline Journal Info:
Nlm Unique ID:  0217513     Medline TA:  Biochim Biophys Acta     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  258-70     Citation Subset:  IM    
Copyright Information:
Copyright © 2013 Elsevier B.V. All rights reserved.
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MeSH Terms
Descriptor/Qualifier:
Computational Biology / methods*
Cystic Fibrosis / genetics*,  pathology
Humans
Models, Theoretical*
Systems Biology*
Grant Support
ID/Acronym/Agency:
7R01HL109362-01/HL/NHLBI NIH HHS; R01 HL109362/HL/NHLBI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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