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.