Cardiovascular Disease: A Systems Biology Approach

In the post-genomic era, the main challenge is to extract meaningful and valuable information from a large pool of data generated by high throughput techniques like microarray and deep sequencing techniques. Systems biology is an emerging discipline that aids in interpreting a large amount of biological data in a meaningful way. It helps to draw significant inference from a large amount of data about the interactions of genes or proteins, by developing quantitative mathematical models. Due to its complex nature, cardiovascular diseases can be better understood using the systems biology concept. Different components of the disease like heart failure and coronary artery disease can be comprehended in a modular fashion, wherein each module consists of multiple genes and their nonlinear interactions. Another approach is population genetics or Genome-Wide Association Studies (GWAS), which has identified over two hundred chromosomal loci that modulate the risk of cardiovascular diseases. These GWAS variation data can be integrated with multi-omics data and gene network data to identify the susceptible pathways, modules and genotyping cause behind it. Identification of a hub gene in a network is one of the main approaches of research in systems biology of cardiovascular diseases. This hub gene can serve as a biomarker for early detection or therapeutic targets. Comorbidities are another cause of increased risk leading to further complications in patients with cardiovascular diseases. Analysis of association of the comorbidities, using a system biology approach, focuses on the prevention of severe vascular events. The most common comorbidities include diabetes, kidney disease, peripheral arterial disease, etc. Systems biology can aid in identifying special biomarkers for early diagnosis of cardiovascular comorbidities and the following careful management might lead to prolonged survival of the patient.

Keywords: Bioinformatics, Cardiovascular Diseases, Data analysis, Disease comorbidities, Genome-wide association studies, Integrated omics, Network Biology, Network Medicine, Systems Biology.