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

Systems biology aims to achieve holistic insights into the molecularworkings of cellular systems through iterative loops of measurement,analysis and perturbation. This framework has had remarkablesuccess in unicellular model organisms, and recent experimental andcomputational advances — from single-cell and spatial profiling toCRISPR genome editing and machine learning — have raised the excitingpossibility of leveraging such strategies to prevent, diagnose and treathuman diseases. However, adapting systems-inspired approachesto dissect human disease complexity is challenging, given thatdiscrepancies between the biological features of human tissues andthe experimental models typically used to probe function (which weterm ‘translational distance’) can confound insight.

Here we reviewhow samples, measurements and analyses can be contextualizedwithin overall multiscale human disease processes to mitigate data andrepresentation gaps. We then examine ways to bridge the translationaldistance between systems-inspired human discovery loops and modelsystem validation loops to empower precision interventions in the eraof single-cell genomics.