DOI: 10.6084/m9.figshare.c.3712738_d5
title: Additional file 5: of Asymmetric cellular memory in bacteria exposed to antibiotics

description: To assess the sensitivity of the simulation outcomes to varying simulation parameters, we changed a single simulation parameter at a time and rerun the simulations shown in Fig. 7 and 9. Since we did not vary all of the parameters and did not change more than one parameter at a time, this is not exhaustive. The following table lists the parameter values used in the simulations in Fig. 5 and 7 in the column Default. For each parameter we chose a lower and a higher value to rerun the simulation (columns Lower and Higher). See description and usage of the parameters in supplementary material S7. The following figures show the results from simulations where single parameters were changed compared to the reference parameters used in Figs. 7 and 9. Figure S9.1: lambda = 0.1. Figure S9.2: lambda = 0.4. Figure S9.3: mutRate = 0.0001. Figure S9.4: mutRate = 0.01. Figure S9.5: rndKill = 0.01. Figure S9.6: rndKill = 0.1. Figure S9.7: daughtersAlwaysLeave, daughtersAlwaysStay and numEnv = 5. Lowering the rate to switch from recovery phase to stress phase to 0.1 led to the evolution of a genotype with a high basal protection as was observed with a lambda of 0.2 (compare Figure S9.1a to Fig. 7a). But we did no longer observe the evolution of a memory genotype (compare Figure S9.1b to Fig. 7b and Figure S9.1c to Fig. 9a). Interestingly a phenotype that segregated cellular protection only to one of the two cells emerging from division still did evolve in the case of two environments (Figure S9.1d). The simulation trajectories were comparable to the reference when lambda was increased from 0.2 of 0.4 (compare Figs. 7 and 9 to Figure S9.2). Qualitatively we observed the same simulation outcome when decreasing the mutation rate from 0.01 to 0.0001, although a slower convergence was observed (compare Figs. 7 and 9 to Figure S9.3). Simulation results that were run with an increased mutation rate (0.1) diverged from what we observed in the reference simulations (compare Figs. 7 and 9 to Figure S9.4). Note the bimodal distributions of both basal protection and the memory distribution factor in Figure S9.4c and S9.4d. Decreasing the fraction of individuals that are killed randomly in each simulation round from 0.05 to 0.01 led to the evolution of a basal protection genotype independent of information content and number of environments (Figure S9.5). In these simulations the carrying capacity of the population (10’000 individuals) was almost always exhausted, there was not enough ‘room’ for evolutionary mechanisms in the 100’000 time steps. When increasing the killing rate to 0.1 the mean basal protection that evolved in a random environment was significantly lower compared to the reference (compare Figure S9.6a to Fig. 7a). A high population turnover favors a genotype with an intermediate basal protection to increase probability of reproduction. The simulation results observed when increasing random killing of individuals from 0.05 to 0.1 were comparable to the reference (compare panel S9.6b, c and d to Figs. 7b, 9a and b). A set of simulations was run with the same parameters as shown in Fig. 9b, but daughter cells were not randomly moved to one of the two environments. Instead, the daughter cells were always moved to the environment where they were not ‘born’. This had an impact on the evolution of the memory distribution factor (mean 0.07 in Figure S9.7a versus 0.03 in the reference environment Fig. 9b). As expected we did not observe asymmetric memory when simulating two informative environments, where daughter cells were forced to stay in the environment they were born (Figure S9.7b). Increasing the number of informative environments from 2 to 5 had no noticeable impact on the evolution results (Figure S9.7c). (ZIP 5.56 mb)