1. Results presented here hint that growing cultures of bacteria under highly stable conditions for several thousand generations may lead to a global decrease in error production whereas growing cells in highly unstable environments can select for replicative infidelity. This could have strong implications for biotechnology and cloning processes which rely on bacteria to produce molecules. These bacteria frequently evolve ways to circumvent the designers plans but perhaps evolving a strain with decreased capacity for generation of variation would alleviate these issues. Concurrently, for directed evolution experiments which try to evolve bacteria to perform a non-native function, harsh and unpredictable environments may produce quicker evolutionary change within the population. This model represents a potential way forward for many challenges of biological design that would benefit from the ability to tune evolution. A possible extension is to remove the 1-to-1 mapping between bacterial and environmental genomes and include an error-prone transcription/translation step. The dynamics of how this error-rate and the existing replicative error-rate co-evolve may produce interesting results with potential real world applications in fields attempting to use cellular transcriptional and translational machinery for the production of novel sequence defined drugs and materials. Selecting for variation: the benefits of infidelity Adam Hockenberry 1 1 Interdepartmental Biological Sciences Graduate Program, Northwestern University, Chicago, IL U.S.A ABSTRACT CONCLUSIONS AND FUTURE DIRECTIONS RESULTS METHODS AND MODEL DESIGN Evolutionary theory posits that over time both genetic drift and natural selection lead to a decrease in variation within a population. Variation is maintained by other processes such as gene flow, mutation, and sexual reproduction. However, mutation is not only a exogenous phenomenon. Rates of mutation vary across individuals and between species leading to speculation that the capacity to produce evolutionary change, heretofore referenced as evolvability, is selectable at the individual level. Whether the ability to evolve can itself be the subject of evolutionary pressures and whether it has been in the past is, however, still an open question. Through the use of agent based modeling, we show that the ability to maintain replicative variation can be selected for under certain conditions. These results add to the growing body of evidence which shows not only the possibility, but the likelihood of selection for evolvability in extant lineages. Environmental agents are stationary and simply grow food. They do, however, have maximum amounts of food that they contain as well as adjustable nutrient production rates which gives the system a defined carrying capacity. They also have an environmental `genome' which can be thought of as a binary representation of several nutrients or toxins that they contain. Bacterial agents have their own genomes which, unlike the binary environmental counter parts, are floating point numbers between 0 and 1. At each time step bacteria move pseudo-randomly, eat to gain energy, and lose a set amount of energy. If they have a threshold level of energy they reproduce and if they have zero energy, they die. Selection acts through their feeding as the individuals with genomes that more closely match the environment are able to eat more at a given time step which will allow them to reproduce faster and have a lower chance of dying. As an example: let the environmental genome = 0, 1, 0, 1, 0 for a given bacterial genome = 0.32, 0.75, 0.47, 0.98, 0.22 the amount of energy harvested from the environment = 5 - 0.32 - 0.25 - 0.47 - 0.02 - 0.22 = 3.72 Bacteria pass their genome to their offspring with variation according to the value of their error-rate. Each value is passed on according to a random-normal distribution centered around the current value with a standard deviation equal to the value of the error-rate and there is an absolute floor set on error-rates (0.1) to represent a maximum replicative fidelity that is achievable in a stochastic chemical system. The error-rate parameter is crucially also passed on to offspring based on a random normal distribution described above. In addition, the environmental genome can change over time. At each time step, there is a small chance that one (or more) of the environmental `genes' will flip from a 1 to 0 or vice versa. In addition, the sharply peaked fitness landscape can be varied by inserting various valleys of user defined depth and breadth. In effect, this penalizes bacteria which have a range of specific values for certain genes making it difficult to move between their current state and the optimum level. Histograms show the mean error-rates at each time step of 5 model simulations of 10000 time steps each with a starting error-rate of 0.1. (a) For a given magnitude of environmental change (40\%), increasing frequency of change occurring shows increased time spent at higher error-rates. (b) For a given frequency of environmental change (1:100) increasing magnitude of changes pushes the population to spend more time at higher-error rates. Our system produces population dynamics illustrative of a defined carrying capacity (a). (b) Mean (black), min (green), and max (red) generation present in the population during a representative run. (c) Separate runs showing the mean match of individuals in the population to the environment at each time step. (d) Separate runs showing the mean error- rate across the population over time. (a) Schematic showing the implementation of fitness valleys which lead to local fitness maxima. (b) Red traces show error-rates from separate runs under conditions of environmental stasis. Black traces show separate runs under conditions of environmental change. (c) For a set fitness valley depth, breadth of valley (0.1 red, 0.2 green)influences lag time for population to reach optimal match to the environment. (d) For a set fitness valley breadth, depth of fitness valleys (0.5 blue, 1 green) influences lag time for population to reach optimal match to the environment. (c) and (d) show 5 representative runs for each case. (e) For a stable environment, population levels and error- rate are relatively consistent throughout representative run. (f) During periods of environmental change, population levels fluctuate and correlate with increases in mean population error-rate. Replicating the basic features of evolution Error-rates and environmental change Features of a complex fitness landscape Evolution in an unstable environment (a) Population dynamics for a representative run with a 1:100 chance of a magnitude 40% environmental change occurring at each time step. (b) Trace of generation time during the same run shown in (a). (c) Separate traces for the same environmental change showing the oscillatory dynamics of match to the environment. (d) Separate traces showing the error-rate over time of the same 5 runs illustrated in (c). An illustration of the model’s graphical user interface and run-time output