Adapting systems biology to address the complexity of human disease in the single-cell era

Abstract

Systems biology aims to achieve holistic insights into the molecular workings of cellular systems through iterative loops of measurement, analysis and perturbation. This framework has had remarkable success in unicellular model organisms, and recent experimental and computational advances — from single-cell and spatial profiling to CRISPR genome editing and machine learning — have raised the exciting possibility of leveraging such strategies to prevent, diagnose and treat human diseases. However, adapting systems-inspired approaches to dissect human disease complexity is challenging, given that discrepancies between the biological features of human tissues and the experimental models typically used to probe function (which we term ‘translational distance’) can confound insight. Here we review how samples, measurements and analyses can be contextualized within overall multiscale human disease processes to mitigate data and representation gaps. We then examine ways to bridge the translational distance between systems-inspired human discovery loops and model system validation loops to empower precision interventions in the era of single-cell genomics. Differences between humans and experimental models create a translational gap that makes it difficult to extrapolate research findings. The authors review systems-focused approaches to identify and control the translational distance between a complex disease process being studied and the experimental model used for testing.