Systems biology is a multidisciplinary field that incorporates a multitude of experimental and computational approaches to investigate complex biological systems using a holistic approach that underscores the integrated and dynamic networks of interacting components (genes, proteins, cells, organisms, and their interactions). Systems biology relies largely on multi-omic approaches to generate differential data, which has been facilitated by advancements in biological research techniques. Large biological datasets are generated from high-throughput experiments, such as microarrays, mass spectrometry, and high-throughput drug screening. Many comprehensive shared datasets are even available in numerous online portals and can provide unparalleled access to 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 more comprehensive understanding of how different dimensions of biology interact with and inform one another. Systems biology is instrumental in studying biological complexity and can pave the way for advances in addressing biomedical challenges.