The optimal strategy balancing risk and speed predicts DNA damage checkpoint override times

Checkpoints arrest biological processes, allowing time for error correction. The phenomenon of checkpoint override during cellular self-replication is biologically critical, but it currently lacks a quantitative, functional or system-level understanding. To uncover fundamental laws governing error correction systems, we derived a general theory of optimal checkpoint strategies, balancing the trade-off between risk and self-replication speed. Mathematically, the problem maps onto the optimization of an absorbing boundary for a random walk. We applied the theory to the DNA damage checkpoint in budding yeast, an intensively researched model checkpoint. Using novel reporters for double-strand DNA breaks (DSBs), we first quantified the probability distribution of DSB repair in time including rare events; second, we determined the survival probability after override. With these inputs, the optimal theory remarkably accurately predicted override times as a function of DSB numbers, which we precisely measured for the first time. Thus, a first-principles calculation revealed undiscovered patterns underlying highly noisy override processes. Our multi-DSB measurements revise well-known past results and show that override is more general than previously thought.