Molecular Network Analysis in Model and Non-Model Legumes: Challenges in Omics Data Interpretation Across Species, with a Focus on Glycine max, Lupinus albus and Medicago truncatula

Molecular network analysis offers powerful insights for plant improvement by capturing complex regulatory interactions. However, translating omics data across species presents significant challenges. Non-model crops such as soybean and lupin often lack comprehensive genomic resources, which complicates network analysis. Model species (e.g., Arabidopsis thaliana) provide rich data but may lack legume-specific pathways. This review synthesizes these challenges and examines legume networks in soybean, lupin, and the model legume, Medicago truncatula. Strategies such as multi-omics integration and Artificial Intelligence (AI)-driven tools, combined with wet lab validation studies such as clustered regularly interspaced short palindromic repeats (CRISPR), are discussed to bridge the gap between discovery and application. Ultimately, we conclude that cross-species multi-omics integration, empowered by AI and validated by gene editing, will be pivotal for translating network discoveries into resilient legume crops. Strategic investments in under-researched non-model legumes and advanced molecular tools are essential to ensure sustainable agriculture and future crop resilience.