Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition – Eui Min Jeong

In this talk, we discuss the paper “Quantifying the energy landscape of high-dimensional oscillatory systems by diffusion decomposition” by S. Bian et.al., Cell Reports Physical Science, 2025.

Abstract

High-dimensional networks producing oscillatory dynamics are ubiquitous in biological systems. Unraveling the mechanism of oscillatory dynamics in biological networks with stochastic perturbations becomes of paramount significance. Although the classical energy landscape theory provides a tool to study this problem in multistable systems and explain cellular functions, it remains challenging to accurately quantify the landscape for high-dimensional oscillatory systems. Here, we propose an approach called the diffusion decomposition of Gaussian approximation (DDGA). We demonstrate the efficacy of the DDGA in quantifying the energy landscape of oscillatory systems and corresponding stochastic dynamics in comparison with existing approaches. By further applying the DDGA to high-dimensional biological networks, we are able to uncover more intricate biological mechanisms efficiently, which deepens our understanding of cellular functions.