One of the single greatest questions in biology is how the message encoded in the genome plays itself out to create the phenotypes we observe and how this process goes wrong in disease. In the simplest case, a mutation in a single gene leads in a linear fashion to a disease phenotype. In sickle-cell anemia, for example, the substitution of glutamate with valine in the hemoglobin protein allows it to polymerize and distort the shape of the red blood cells, blocking normal blood flow in capillaries. However, as more data has become available, we have come to understand that in most cases, it is not single genes but complex cellular networks involving multiple genes, epigenetic modification, and gene expression changes, and well as metabolic and protein-protein interaction shifts, that mediate cellular processes and, when altered, lead to disease. Systems biology attempts to address this question by looking beyond single genes to build models of these underlying networks and pathways. Combining laboratory and modeling approaches, systems biology has the goal of building predictive mathematical models that can be further tested and refined.