Optimal checkpoint strategies

Surveillance systems are critical for biological systems. Cellular self-replication is monitored by checkpoints, which arrest cells when errors are detected. However, checkpoints are hardly explored quantitatively. We were interested in checkpoint override, the phenomenon that checkpoints 'give up' or 'override' when cells are faced with persistent errors. We sought to find patterns to checkpoints override, that is, whether there are quantitative laws that describe how long checkpoints arrest before override. We investigated a theory that balances risk and speed for cells, which required two parameters to be measured, how fast errors are repaired and what the survival probability is if checkpoints override. We measured these parameters for the first time precisely. Based on these data, the theory predicted the optimal checkpoint override times. Measuring these times experimentally in cells with multiple DNA breaks, we found a remarkably close match between theory and experiments. The universal nature of the balance between risk and self-replication opportunity is in principle relevant to many other systems, suggesting potential further applications of the theory.

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