Acquired mtDna Mutations

We have used two slightly different computational models to represent the somatic mutations that occur in mtDNA as part of the aging process. One method is to tie mutation to the replication process, to represent mutations that arise from replication errors. In this case, at every mtDNA replication event there is some probability Pmut that a new mtDNA mutation will be created. An alternative method is to model mutation formation independent of the mtDNA replication process. Then, over every time interval At there is a probability Pmut that a mtDNA molecule may be converted to a new mutation. Unless the replication rate is varying with time in the model, there is little significant difference between these two models for de novo mtDNA mutation formation. The parameter Pmut can also be made a function of time to represent changing mutation conditions, such as periods of increased radiation exposure, for example.

Models of acquired mtDNA mutations based on the mechanisms of mtDNA replication and degradation have a particular behavior that must be considered in the design of the simulation. Just after a new mutation forms, the population of mtDNA within the simulated cell will have only one copy of that mutation and about 1,000 other mtDNA molecules that are wild-type or different mutations. In simulated postmitotic cells n

(where the cellular mtDNA population is not growing with time) there is a 50% chance that this particular mutant mtDNA molecule will be degraded before it is copied, thus removing the new mutation relatively quickly from the cell. Even if the mutated mtDNA does manage to be copied, then you have only two copies of the new mutant and it is still very likely that you will lose both of those copies to degradation. So you should expect that the majority of de novo mutations will survive in the simulated cell for only a short time (Elson et al., 2001), and the data structures and memory management of your simulation design should be made with this point in mind to avoid wasting memory on the many mutations that transiently appear in the simulated cell.

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