Professor Kim Sang-uk (LIFE) and Han Seong-kyu (LIFE, Ph.D. Integrated) developed a technology to select genes so that animal models can accurately represent human diseases using the principle of the evolution of gene switch through analysis of big data. This achievement, published in the recent issue of “Molecular Biology and Evolution”, a leading research journal of the realm of evolution, is aimed to help develop various animal disease models precisely, which would help develop new medicines or explore effective treatments.

The research team gathered the symptoms observed from the disease model, in this case mice and human patients and then analyzed this big data. Various information such as pictures, documents, and vital signs were organized into an ontological system. Ontology is a kind of knowledge expression that makes it possible for computers to process various types of information and is often used for automatic translation or artificial intelligence of computers. In this way, the team systematically analyzed the differences between animal models and patients, quantitatively calculating differences in disease symptoms.

In addition, they also explained that gene switches that express or stop the function of certain genes cause many animal models to fail. The theory of gene expression switch evolution used here states that the gene switches that control disease expression contributes to the evolution of various spinal animals.

Prof. Kim, who led the study, said, “evolutionary studies such as the evolution of stickleback and the identification of toe structure difference between anthropoids can be a solution to the challenges that modern biological science has failed to solve.”