Resiliomics: A Systems-Level and Genetic Framework for Quantifying Biological Resilience

Biological systems are continuously exposed to internal and external perturbations, yet they maintain functionality through complex adaptive and regulatory mechanisms. While conventional omics approaches have substantially advanced our understanding of molecular responses to stress, they predominantly capture static states or peak responses, overlooking the dynamic processes governing recovery and long-term system stability. Here, we introduce resiliomics, a conceptual omics framework dedicated to the quantitative characterization of biological resilience. Resiliomics integrates time-resolved multi-omics data with systems biology and network-based approaches to quantify key parameters, including recovery kinetics, perturbation amplitude, and system robustness. Importantly, resiliomics incorporates genetic determinants of resilience, including gene regulatory mechanisms, genetic variation, and genotype–phenotype relationships, which shape system responses and recovery trajectories across biological contexts. By shifting the focus from response-centric to recovery-centric analyses, this framework enables the investigation of dynamic trajectories underlying adaptation and stability. Resiliomics provides a unifying perspective that bridges molecular biology, ecology, and engineering principles of resilience, offering new opportunities for predictive modeling and integrative analyses across biological scales. This approach has broad implications for understanding stress tolerance, disease dynamics, and ecosystem stability in a rapidly changing environment.