Directed evolution simulator
Directed evolution is natural selection sped up in the lab: create variation, keep the best and repeat. Set the mutation rate and the selection pressure and see how many generations it takes to adapt a population to a hostile environment.
Key formula
p′ = p · (1 + s) / (1 + p · s)
Try it
How to use the simulator
Directed evolution is a real lab technique: instead of designing an organism, you let it mutate and, generation after generation, keep the individuals that best tolerate the condition you care about. The simulator follows a Taumoeba population that has to adapt to a new environment, with an adaptation score from 0 to 100%.
Controls
- Mutation rate: how many changes appear each generation. Only a small share are beneficial.
- Selection pressure: how harsh the environment is. More pressure removes more individuals but speeds up the filtering.
- Buttons: simulate one generation, evolve automatically to the target, or reset.
Things to try
- Keep the defaults and click "Evolve to target". Note how many generations it takes to reach 95%.
- Reset with the mutation rate at its minimum and try again. Without variation there's nothing to select, so it takes much longer.
- Push selection pressure to the maximum and check survival in the first generations: in a real lab, the population might die out before it adapts.
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Variation and selection
Every time an organism reproduces, its DNA is copied with a few errors. Most of those mutations are neutral or harmful, but now and then one improves something. If the environment favors carriers of that mutation, they leave more offspring and the variant spreads.
Strong selection pressure speeds things up by weeding out the less fit sooner, but it also shrinks the population and the variation available. Too much pressure can wipe the population out before the useful mutation shows up.
In the lab
Frances Arnold won the 2018 Nobel Prize in Chemistry for using directed evolution to create new enzymes: she introduced random mutations, measured which variants worked best and used them as the starting point for the next round. Today it is used to make detergents, drugs and biofuels.
Richard Lenski's E. coli experiment, started in 1988 and now past 75,000 generations, showed a population developing new abilities in real time, such as feeding on citrate, something the original species couldn't do in the presence of oxygen.
Worked example
Example: how long a mutation takes to spread
- Suppose 1% of the population carries a mutation with an advantage s = 0.5 (it leaves 50% more offspring).
- With the formula above, the ratio of carriers to non-carriers multiplies by 1.5 every generation.
- Going from 1:99 to 95:5 means multiplying that ratio by about 1,900: ln(1,900) / ln(1.5) ≈ 19 generations.
- For bacteria that divide every 20 minutes, that is under 7 hours. Try the simulator to see how the number of generations changes with different selection pressure.
In Project Hail Mary
In the novel, scientists need a microorganism to survive in conditions different from its own, and turn to directed evolution to get there generation after generation.
Frequently asked questions
Is it the same as genetic engineering?
No. Genetic engineering changes DNA in a planned way. Directed evolution doesn't need to know which change is required: it generates many variants and lets selection find the right one.
Why do antibiotic-resistant bacteria appear?
Through the same mechanism. An antibiotic is a huge selection pressure: it kills the susceptible bacteria and clears the way for any resistant mutant. That is why doctors stress not using them unnecessarily.
Is a higher mutation rate always better?
No. Beyond a certain threshold, errors destroy genetic information faster than selection can preserve it. Many RNA viruses live close to that limit.