Cardiovascular Disease: A Systems Biology Approach
Pp. 234-250 (17)
Sujata Roy and Ashasmita S Mishra
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.
Bioinformatics, Cardiovascular Diseases, Data analysis, Disease
comorbidities, Genome-wide association studies, Integrated omics, Network
Biology, Network Medicine, Systems Biology.
Department of Biotechnology, Rajalakshmi Engineering College, Rajalakshmi Nagar, Thandalam, Chennai-602105, Tamil Nadu, India.