Discrete Time Evolution of Proteomic Biomarkers

Author

Gnabasik, David ; Alaghband, Gita

Author_Institution

Coll. of Eng. & Appl. Sci., Univ. of Colorado Denver, Denver, CO, USA

Volume

2

fYear

2014

fDate

10-13 March 2014

Firstpage

11

Lastpage

16

Abstract

We measured a panel of 12 cytokines in seven different populations: i.e., healthy non-smokers, healthy smokers, COPD, Aden carcinoma and Squamous cell carcinoma of the lung. From these 12 biomarkers of host response to lung disease we have developed a computational and visual model that reliably distinguishes these clinical types. Protein biomarker behavior models are developed as the topological evolution of linear discrete systems from changes in patient protein sample concentrations.

Keywords

cellular biophysics; diseases; lung; proteins; proteomics; topology; COPD; adenocarcinoma cell carcinoma; computational model; cytokines; discrete time evolution; healthy nonsmokers; healthy smokers; host response; linear discrete systems; lung disease; patient protein sample concentrations; protein biomarker behavior models; squamous cell carcinoma; topological evolution; visual model; Biological system modeling; Computational modeling; Equations; Mathematical model; Proteins; Proteomics; cytokine biomarker; discrete time evolution; proteomics; topological analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Science and Computational Intelligence (CSCI), 2014 International Conference on

Conference_Location

Las Vegas, NV

Type

conf

DOI

10.1109/CSCI.2014.87

Filename

6822296