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

Abstract

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.

Keywords: Glycine max; Lupinus albus; Medicago truncatula; co-expression networks; cross-species gene analysis; legumes; molecular network analysis; non-model plants; omics data integration; orphan crops.

Figure 1

Conceptual overview showing how model and non-model legumes (soybean, lupin, Medicago truncatula, and Arabidopsis) inform integration strategies like multi-omics, AI, and CRISPR to connect omics layers with environment and support crop improvement. The schematic highlights cross-layer integration of data: genomic, transcriptomic, proteomic, metabolomic, and phenomic information (depicted as interconnected layers and arrows) are combined across these species. Computational tools such as AI facilitate the fusion of these omics layers, and validation methods like CRISPR link molecular findings to phenotypic outcomes. This cross-omics integration provides a holistic framework that connects gene networks to traits under real-world conditions, ultimately guiding breeding and biotechnology strategies for crop improvement. Created with BioRender.com.