This innovative technology addresses previous computational challenges by enabling rapid, systematic searches for gene control targets even within large-scale distorted networks.

A KAIST research team led by Professor Kwang-Hyun Cho has developed a universal algebraic technology capable of identifying and restoring altered gene networks in cells, with promising applications in cancer therapy, drug development, and personalized medicine.

The approach allows scientists to predict stable cell states and determine how controlling specific genes can revert abnormal cellular responses to normal, facilitating targeted correction of gene networks.

By predicting how gene control interventions can shift cells toward healthy states, the method aids in identifying key gene targets for therapeutic purposes.

The team modeled complex gene interactions using Boolean network diagrams and visualized cellular responses with landscape maps, employing mathematical techniques such as the semi-tensor product and Taylor approximation to simplify calculations.

Using the semi-tensor product method, the researchers efficiently calculated how controlling particular genes influences overall cell responses, streamlining the analysis of gene network dynamics.

The technology has been successfully tested on various gene networks, including bladder cancer cells and immune cell differentiation pathways, demonstrating its ability to systematically identify core gene control targets.

This research was published in Science Advances on August 22, 2025, with contributions from KAIST students and support from the Korean government’s research programs.

Professor Cho emphasized that this innovation is fundamental to developing the Digital Cell Twin model, which aims to analyze and manipulate gene network phenotypes for broad applications in medicine and life sciences.