The study of evolution delves into how living organisms adjust to their surroundings over generations, yet what about the evolution of the evolutionary process itself?

Scientists have long pondered why biological populations excel at utilizing their surroundings—a characteristic termed “evolvability.” Consider, for instance, antimicrobial resistance and the rapid mutation of new viral pathogens that can successfully evade vaccines.

Recently, a study conducted by the University of Michigan suggests that one reason evolution is so powerful is that it is capable of evolving itself. The findings are documented in the Proceedings of the National Academy of Sciences.

“Life demonstrates remarkable capabilities in addressing challenges. Observing the immense diversity in life, the notion that all of these forms originated from a single ancestor is astonishing to me,” stated Luis Zaman, an evolutionary biologist at U-M and principal author of the research. “What makes evolution appear so inventive? Perhaps this trait itself is something that has undergone evolution.”

The question of whether evolvability can evolve is a complex one, according to Zaman, as mutations—which elevate an organism’s fitness and enhance their survival likelihood in the current environment—are a major driving force of evolution. However, evolvability does not focus on immediate fitness augmentation. Instead, it enhances the potential for future fitness of an organism.

“This prospective trait of evolvability makes it controversial,” Zaman explained. “We believe it to be significant. Its occurrence is evident. However, the reasons and timing of its emergence remain less clear. We aimed to explore: Can we perceive the evolution of evolvability within a more realistic computational framework?”

Transforming a specialist into a generalist

To investigate these concepts, Zaman and his colleagues developed a computational model featuring a series of three advantageous logic functions and three detrimental logic functions. The logic functions can be visualized as red and blue berries, varying in their benefits or toxicity across different environments, the researchers indicated. In one scenario within the model, red berries are advantageous for a population, while blue berries are harmful. Conversely, in the second scenario, blue berries benefit the population, and red berries are toxic. This implies that a population cannot excel in both environments simultaneously—it can thrive in either one or the other, according to Zaman.

The researchers then executed a range of scenarios and monitored how evolvability might shift throughout each one. In one scenario, the environments remained stable: the population consistently consumed either red berries or blue berries. In another scenario, the population alternated between consuming red and blue berries.

They discovered that when they made the populations “cycle” between these two environments, the populations were able to pivot back and forth between these contrasting environments and thrive in both.

Specifically, this cycling resulted in a thousandfold increase in mutations that enabled the populations to effectively alternate between consuming red and blue berries in each environment.

Adjusting to a mutational terrain

The computational framework utilized by the researchers to assess evolvability is known as Avida. When scenarios were established using Avida that cycled between logic functions (symbolized by red and blue berries), the programs facilitated their move into new mutational terrains.

Envision the evolved computer programs as conduits comprised of numerous genes represented by computer codes, Zaman suggested. Each time the environment shifts, this conduit must be rearranged to accommodate the new berry type.

“The mutational areas that populations explore—through evolutionary processes—represent spaces where minor mutations can recalibrate this conduit,” he stated.

Mutations materialize when an individual computer instruction (gene) within the program (genetic conduit) is altered. Over time, this adjustment reconfigures the pathway, subsequently enabling the population of computer programs to thrive in an area where specialists for both red and blue berries coexist.

The team also modified how frequently they cycled between environments: They evaluated the results of when a population remained in one environment for a single generation prior to transitioning, contrasted with staying for 10 generations and then 100 generations. They discovered that if fluctuations occurred too rapidly, an increase in evolvability was not observed. However, intriguingly, even relatively extended cycling periods—spanning hundreds of generations—could foster the evolution and maintenance of evolvability.

“Once a population attains this evolvability, it appears to be preserved despite subsequent evolutionary changes,” Zaman concluded.

This suggests that once evolution enhances its capability for further evolution, such evolvability is likely to be a lasting trait.

Authored by Morgan Sherburne, Michigan News