Many cellular populations are tightly packed, such as microbial colonies<br> and biofilms, or tissues and tumours in multicellular organisms. The<br> movement of one cell in these crowded assemblages requires motion of<br> others, so that cell displacements are correlated over many cell<br> diameters. Whenever movement is important for survival or growth, these<br> correlated rearrangements could couple the evolutionary fate of<br> different lineages. However, little is known about the interplay between<br> mechanical forces and evolution in dense cellular populations. Here, by<br> tracking slower-growing clones at the expanding edge of yeast colonies,<br> we show that the collective motion of cells prevents costly mutations<br> from being weeded out rapidly. Joint pushing by neighbouring cells<br> generates correlated movements that suppress the differential<br> displacements required for selection to act. This mechanical screening<br> of fitness differences allows slower-growing mutants to leave more<br> descendants than expected under non-mechanical models, thereby<br> increasing their chance for evolutionary rescue. Our work suggests that,<br> in crowded populations, cells cooperate with surrounding neighbours<br> through inevitable mechanical interactions. This effect has to be<br> considered when predicting evolutionary outcomes, such as the emergence<br> of drug resistance or cancer evolution.

Evolutionary dynamics are controlled by a number of driving forces, such<br> as natural selection, random genetic drift and dispersal. In this<br> perspective article, we aim to emphasize that these forces act at the<br> population level, and that it is a challenge to understand how they<br> emerge from the stochastic and deterministic behaviour of individual<br> cells. Even the most basic steric interactions between neighbouring<br> cells can couple evolutionary outcomes of otherwise unrelated<br> individuals, thereby weakening natural selection and enhancing random<br> genetic drift. Using microbial examples of varying degrees of<br> complexity, we demonstrate how strongly cell-cell interactions influence<br> evolutionary dynamics, especially in pattern-forming systems. As pattern<br> formation itself is subject to evolution, we propose to study the<br> feedback between pattern formation and evolutionary dynamics, which<br> could be key to predicting and potentially steering evolutionary<br> processes. Such an effort requires extending the systems biology<br> approach from the cellular to the population scale.