Scaling Laws Predict Mutant Burden in Expanding Cell Populations

Researchers from the University of California San Diego have derived universal scaling laws to predict the rise of mutants in spatially growing cell populations, a development that addresses a long-standing challenge in both evolutionary biology and biomedical research. These laws link the expected number of mutants to the size of the overall population and provide insights into when mutations may emerge under selective pressure.

In populations exposed to strong fitness pressures, such as drug treatment designed to eliminate cells, mutations inevitably arise. Yet predicting when they appear and spread has been limited by the immense computational cost of simulating all possible trajectories of mutant development in growing populations. By focusing on broad, universal rules rather than detailed simulations, Dominik Wodarz, senior author of the study published in PNAS Nexus, and colleagues describe how mutant numbers scale in relation to population size and the dimensionality of the system—whether as a two-dimensional layer of cells like a biofilm or a three-dimensional tumor mass.

The framework accounts for several types of intermediate mutants. For example, cells with gene amplifications—duplications of genes within the genome—are represented in the model, as are mismatch repair deficient cells that cannot accurately fix their own mutations due to errors in DNA repair mechanisms. In this way, the scaling laws provide a unifying approach for different biological contexts.

Central to the laws are variables such as the total colony size or time elapsed during growth. These factors are linked to a power that changes depending on elements including the system’s geometry, the number of mutational “hits” required, and whether mutations confer an advantage to cells. The laws thus generalize across situations that might otherwise require prohibitively complex computational evaluation.

For evolutionary biology, the scaling laws help explain experimental bacterial evolution studies, including the observed role of gene amplification in shaping evolutionary rates. From a biomedical perspective, the ability to predict mutant burdens without exhaustive simulations may aid in anticipating when bacterial populations or tumors could evolve resistance to therapy.