Genomic, pangenomic, metagenomic and trancriptomics perspectives to enhance microbial modeling and quantitative risk assessment in food environments

Recent advances in genomics, pangenomics, transcriptomics, and metatranscriptomics have expanded the resolution with which microbial traits relevant to food safety can be described. These approaches complement classical predictive models, which traditionally rely on population-averaged parameters and may overlook the heterogeneity that exists among strains and microbial communities. Omics data help identify genetic, functional, and regulatory features that underpin differences in stress tolerance, growth potential, and virulence, offering a more precise basis for hazard identification and exposure assessment. Genomic and pangenomic analyses clarify how core and accessory gene pools shape strain-level behavior, while transcriptomic studies reveal active pathways during acid, cold, or osmotic challenges. Metatranscriptomics extends this insight to complex communities, capturing how dominant and satellite members contribute to ecosystem function under food-relevant conditions. Incorporating these datasets into predictive microbiology and quantitative microbial risk assessment (QMRA) supports more realistic estimates of growth, survival, and persistence, reducing uncertainty in hazard characterization. Evidence shows that many food-associated strains are hypovirulent or slow-growing, indicating that risk may be overestimated when genetic heterogeneity is not considered. Although molecular data do not directly prescribe mitigation strategies, they support risk management by identifying which subpopulations merit targeted interventions, clarifying which process parameters influence persistence, and refining prioritization decisions. Our work discusses how omics tools align with primary, secondary, and tertiary predictive models and examines the complementarity between traditional decision-making frameworks and AI-based methods. Emphasis is also placed on sustainability, as omics-informed modeling enables more efficient in silico assessments and reduces dependence on resource-intensive challenge testing. Together, these developments strengthen the connection between risk assessment and risk management, supporting more proportionate and informed food safety decisions.