In the late 20th century, life science was advanced by the paradigm of knowledge by stating that “It is only possible to understand the functions of organisms when they are analyzed all the way to the molecular level”.

While the dramatic improvement of measurement technologies such as DNA sequencers and mass analyzers, and the ability to measure single molecules has led to a rapid expansion of our understanding in this direction, the fact remains that organisms have hierarchies from the molecular to the individual level, and that each hierarchy has both hetero and dynamic characteristics. For this reason, a new paradigm is required to understand such complex and dynamic characteristics.

Computational life science makes proactive use of supercomputers to efficiently analyze hetero big data generated from innovative measurement technologies, consistently integrate dynamic multilayer systems, and understand life systems. Only then can life science achieve predictability and controllability.