Open Access
Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction
Author(s) -
Ghasemi Omid,
Ma Yonggang,
Lindsey Merry L.,
Jin YuFang
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: systems biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.087
H-Index - 51
eISSN - 1939-005X
pISSN - 1939-5094
DOI - 10.1002/wsbm.1248
Subject(s) - systems biology , proteomics , computational biology , myocardial infarction , extracellular matrix , ventricular remodeling , computer science , translational research , bioinformatics , biology , medicine , pathology , microbiology and biotechnology , gene , genetics
Inflammation and extracellular matrix ( ECM ) remodeling are important components regulating the response of the left ventricle to myocardial infarction ( MI ). Significant cellular‐ and molecular‐level contributors can be identified by analyzing data acquired through high‐throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large‐scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post‐ MI . In this review, we summarize the systems biology approaches needed to computationally simulate post‐ MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration. WIREs Syst Biol Med 2014, 6:77–91. doi: 10.1002/wsbm.1248 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Proteomics Methods Translational, Genomic, and Systems Medicine > Translational Medicine