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Efficient mathematical methodology to determine multistep mutant burden in spatially growing cell populations
Natalia L Komarova1, Justin R Pritchard2, Dominik Wodarz3
1Department of Mathematics, University of California San Diego, La Jolla, CA 92093, USA.
PNAS Nexus
|September 25, 2025
Summary
We developed universal scaling laws to predict mutant burden in growing cell populations. This computational method accurately forecasts the number of single-hit, double-hit, and multihit mutants without lengthy simulations.
Area of Science:
- Evolutionary biology
- Computational biology
- Cancer research
Background:
- Accurate prediction of mutant burden in growing cell populations is crucial for understanding evolution and predicting cancer relapse.
- Current computational methods are inefficient for biologically realistic parameters in large populations.
Purpose of the Study:
- To derive universal scaling laws for predicting mutant burden in spatially expanding cell populations.
- To enable straightforward prediction of mutant numbers without extensive computer simulations.
Main Methods:
- Derivation of universal scaling laws for mutant dynamics.
- Application to spatially expanding populations in various geometries.
- Validation using experimental evolution studies in bacteria.
Main Results:
- Established scaling laws for predicting single-hit, double-hit, and multihit mutants.
- Demonstrated applicability across different spatial geometries.
- Reconciled disparate results from bacterial evolution studies on gene amplification.
Conclusions:
- The derived scaling laws provide an efficient method for predicting mutant burden.
- This approach advances basic evolutionary science and clinical applications like cancer relapse prediction.
- The findings offer new insights into the role of gene amplification in bacterial evolution.
