Systems Biology: Generating and Understanding Big Data

Systems biology is the study of complex biological systems from a holistic, big-picture view. Advancement in biological research techniques to generate more data efficiently has facilitated a surge in systems biology, which relies on analysis of large datasets to elucidate a cell’s genome, transcriptome, proteome, and metabolome. Large biological datasets are generated from high-throughput experiments, such as microarrays, mass spectrometry, and high-throughput drug screening. Many datasets from previous experiments done by various laboratories and organizations are available in numerous online portals and can provide valuable information. Analysis of data from genomic, transcriptomic, proteomic, and metabolomic experiments can elucidate changes caused by perturbations like disease process and therapeutic interventions. Although each type of “omics” dataset on its own can provide important insights, integrating data from multiple omics experiments and dimensions (e.g., genome and proteome) can provide a better understanding of how different dimensions of biology are coordinated with each other. This can lead to comprehensive information on causes and effects of a disease process and effectiveness and resistance to therapies.