Resilience in biomolecular networks

The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology - Wuhan 430074, China

Received: 17 March 2025
Accepted: 23 May 2025

Abstract

Resilience, a core feature of biological networks, enables systems to endure disturbances while maintaining functionality. We review resilience across scales, from small molecular systems to complex multicellular organisms. We highlight bistability, noise buffering, and the limitations of traditional methods in small-scale networks. Expanding to large-scale networks, this Perspective explores advances in modeling techniques, such as dynamic equations and stochastic approaches, which capture high-dimensional and nonlinear dynamics. The potential landscape framework is presented as a tool for visualizing stability and transitions in processes like cell differentiation and disease progression. In addition, early-warning signals (EWS), including dynamic network biomarker and machine learning, are discussed as predictive tools for critical transitions. Despite challenges in data complexity and integration, emerging computational tools provide transformative insights. By bridging theoretical models and empirical applications, this Perspective underscores the importance of resilience research in addressing biological and medical challenges.