Integrative Omics and AI-Driven Systems Biology: Multilayer Networks Decoding Apis mellifera Health and Resilience.

Abstract

Honey bees (Apis mellifera) are vital pollinators essential for maintaining ecosystem stability and global food production, but they face escalating threats from pathogens, agrochemicals, and climate change. Although proteomics has advanced our understanding of bee physiology, single-omics approaches are insufficient to capture the complexity of colony health. This review highlights the rise of integrative multiomics frameworks─combining proteomics, metabolomics, and lipidomics─with artificial intelligence (AI)-based strategies to decode molecular resilience in bees. We summarize recent advances in omics technologies, including spatial and single-cell platforms, mass spectrometry innovations, and customized computational pipelines. Furthermore, we highlight how AI-enhanced multiomics integration facilitates biomarker discovery, elucidates regulatory networks, especially in nonmodel organisms like honey bees. Emerging computational methods such as deep learning, graph neural networks, and multilayer network models offer predictive, scalable, and interpretable insights. Despite challenges like limited sample input and cross-omics heterogeneity, the convergence of omics and machine learning represents a transformative paradigm for decoding complex biological systems. These integrative approaches offer not only a deeper molecular understanding of bee biology but also generalizable frameworks for systems biology in other ecologically relevant species.