Systems Biology in Personalized Healthcare

Abstract

Modern medicine increasingly recognises that one-size-fits-all treatment protocols leave too many patients under-served. Systems biology, with its focus on mapping the tangled web of molecular interactions that drive disease, offers a natural foundation for personalised healthcare because it treats the patient as an integrated system rather than a collection of isolated symptoms. Yet translating systems-level models into bedside decisions has proven stubbornly difficult. Data integration across omics layers is messy, computational models are often too abstract for clinicians to act on, and validation cohorts large enough to be convincing are expensive to assemble. We examined 240 systems biology programmes aimed at personalised care, drawn from partner institutions in Switzerland, Spain, and Sweden over the period 2019 to 2023. Five programme categories were represented: genomic network modelling, transcriptomic pathway analysis, proteomic interaction mapping, metabolomic profiling, and multi-omics integration platforms. A Systems Biology Personalisation Quality Index (SBPQI) was built from five sub-scores -- data integration depth, model interpretability, clinical actionability, validation rigour, and scalability potential -- with weights derived from regression against translational outcomes. SBPQI correlated with clinical adoption at r = +0.84 (AUC = 0.884). Multi-omics integration platforms scored highest (mean SBPQI 0.827), while standalone metabolomic profiling programmes sat lowest at 0.612. Only 37.1 percent of programmes exceeded SBPQI of 0.75, underscoring how much work remains before systems biology routinely shapes clinical decision-making.