  Cell competition is a quality control mechanism in tissues that results in
the elimination of less fit cells. Over the past decade, the phenomenon of cell
competition has been identified in many physiological and pathological
contexts, driven either by biochemical signaling or by mechanical forces within
the tissue. In both cases, competition has generally been characterized based
on the elimination of loser cells at the population level, but significantly
less attention has been focused on determining how single-cell dynamics and
interactions regulate population-wide changes. In this review, we describe
quantitative strategies and outline the outstanding challenges in understanding
the single cell rules governing tissue-scale competition dynamics. We propose
quantitative metrics to characterize single cell behaviors in competition and
use them to distinguish the types and outcomes of competition. We describe how
such metrics can be measured experimentally using a novel combination of
high-throughput imaging and machine learning algorithms. We outline the
experimental challenges to quantify cell fate dynamics with high-statistical
precision, and describe the utility of computational modeling in testing
hypotheses not easily accessible in experiments. In particular, cell-based
modeling approaches that combine mechanical interaction of cells with
decision-making rules for cell fate choices provide a powerful framework to
understand and reverse-engineer the diverse rules of cell competition.
