Key result
Systems biology approaches can be used to computationally simulate post-myocardial infarction remodeling by integrating large-scale genomic and proteomic data into dynamic models.
This review summarizes systems biology approaches for computationally simulating post-myocardial infarction remodeling, including data acquisition, integration, and interpretation.
May inform post-MI remodeling research; leaves open clinical utility pending prospective validation.
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
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Ghasemi et al. (2013) conducted a review in Myocardial infarction. Systems biology approaches was evaluated. Systems biology approaches can be used to computationally simulate post-myocardial infarction remodeling by integrating large-scale genomic and proteomic data into dynamic models.
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