 To work out how this had occurred, the researchers reconstructed the particular evolutionary trajectory that led to this multi-step innovation (Blount et al. 2012). They were able to do this step-by-step reconstruction because of the tractability of the model system, and in particular because they had frozen regular time points in the evolutionary history of each bacterial lineage. Large bacterial populations can be frozen and later revived; researchers can thus maintain a “frozen fossil record” (Lenski 2011) and compare a current population’s features (such as fitness or morphology) to those of its ancestral population, or reconstruct steps in its evolutionary trajectory. In this case, frozen population samples could be sequentially analyzed to infer the order of mutational steps. A very similar sort of reconstruction could be carried out on the complex mutations occurring in the Avida simulation (Lenski et al. 2003). The researchers were able to rerun the computer model many times to understand every step along the way towards the accumulation of a complex mutation, in this case, a logic function more complicated than any of the basic instructions in the digital organisms computer-code genomes. This allowed researchers to study the accumulation of mutations leading to complex novel traits in two complementary and comparable sorts of model systems.