In order to address the impact of noise and error amplification on the results of the 2DSA algorithm, it was combined by Brookes et al. (2009) with a Monte Carlo analysis. Fifty iterations were performed by the Demeler laboratory in order to determine 95% confidence intervals. This seems to be an unusually low number of iterations, in particular since the high confidence limits require estimating the quantiles of rare events, in this case the 2.5 and 97.5 percentiles. With 50 iterations, they are determined by the extreme 1.25 occurrences of parameter values, which makes these estimates of the confidence intervals quite variable statistical quantities themselves. As is well known, usually the number of Monte Carlo iterations required to produce meaningful results is typically on the order of 1,000-10,000. However, it seems this would lead to excessive computational effort, several orders of magnitude more costly than the Tikhonov regularization in the standard approach with the full high-resolution grid, which requires for our standard example only a few seconds on our PC.