Resilience in biomolecular networks

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 review 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 biomarkers 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 review underscores the importance of resilience research in addressing biological and medical challenges.