Biological Systems to Computational Systems Biology

Abstract

Systems biology has emerged during the past 20 years with the goal of studying organisms holistically and comprehensively. It is characterized by modeling and large datasets. The introduction of high-throughput technology in the 1990s led to a wealth of biology knowledge. On the other hand, the data at the time required computational simulations and mathematical models in order to be understood. In contrast to more conventional branches of biology such as evolutionary, molecular, and developmental biology, systems biology has had a long history of computer and mathematical research since the early 1990s. Initial systems biologists devised various methods for handling large datasets and formalizations that simulate certain channels, such as signal transduction systems, gene monitoring, and metabolic systems, to improve the technique. These developments led to the emergence of other systems biology sub-disciplines, including systems pharmacology, which also uses systems biology techniques to study the mechanisms underlying medications, and cancer systems biology, which employs computational modeling to identify cancer-causing pathways. Here, the approaches based on systems biology have enormous advantages for biologists, especially for those in life science research. First, complex biological networks, rather than just one or a few genes, play a role in many complex diseases such as diabetes, lung disease, and cardiovascular disease. Furthermore, systems biology methods permit the modeling, manipulation, and predictions of multifaceted systems, which are essential for the diagnosis and treatment of complex disorders. The systems biology concept is proactive instead of reactive for the reasons mentioned above.