In our Study of the Day feature series, we highlight a research publication related to a John Templeton Foundation-supported project, connecting the fascinating and unique research we fund to important conversations happening around the world.
One way of thinking about aging and demise — whether in living organisms or in complex systems or machines — is that it’s simply a matter of accumulated wear and tear. Rust builds up, mutations accrue, the computer hard drive fills with junk files, and eventually things grind to a halt. Careful maintenance might be able to keep those forms of decay at bay for a while, but eventually the wear and tear wins out.
There’s a different, evolutionary way to think about aging, though: if a system (be it an organism or an organization) optimizes for one set of criteria, it necessarily will ignore or minimize other potential goals. Natural selection favors successful reproduction, so once an organism has finished reproducing and seeing to the success of those offspring, there’s no evolutionary pressure to avoid aging and death. In that model, aging isn’t a flaw in the successful functioning of an organism; it’s at best irrelevant to the function, and might even be thought of as part of the bigger picture.
In a new study (currently available in preprint), computational biologists Léo Pio-Lopez and Benedikt Hartl, both of the Allen Discovery Center at Tufts University, created computer simulations of simple lifeforms to see what qualities emerged as they developed, matured, and aged. The lifeforms were represented in a 16-by-16 pixel grids, with initial rules set up to create “the rough outlines of the amphibian face during early development” — within about 35 developmental steps a recognizable smiley-face would emerge from the starting random noise.
After “adulthood” was reached, though, the organisms were left to continue through hundreds of additional cycles, affected both by external noise — the equivalent of “wear and tear” — just by continuing to operate under the rules that had guided their development.
What the researchers found was that the smiley-faces would persist for a while, but eventually would lose an eye, a mouth, until there was nothing recognizable left. This decay and death was primarily due not to external noise but to the fact that the organisms had optimized for development rather than persistence.
“Aging emerges naturally after development due to the lack of an evolved regenerative goal,” Pio-Lopez and Hartl write, while “noise, reduced competency, communication failures, and genetic damage all accelerate aging but are not its primary cause.”
Pio-Lopez and Hartl did, however, find that the loss of function in aging isn’t always as full as it seems. When the simulated smiley-faces lost an eye, the automata maintained a sort of memory of what the organ was: “Despite organ loss, spatial information persists in the cybernetic tissue, indicating a memory of lost structures, which can be reactivated for organ restoration through targeted regenerative information.”
In the context of their simulation, the authors found considerably fewer interventions were required to cause one of their organisms to regrow a lost eye than to grow a new one from scratch.
This has implications for the study of interventions to slow or stop the aging of living organisms, since “reaching a goal by generative means is completely different from maintaining it.” We might, then, expect organisms to degrade over time after reaching the initial goal they were fine-tuned for, but also hope that there may be ways for those lost abilities to regenerate with minimal intervention.
Still Curious?
Read “Aging as a Loss of Goal-Directedness: An Evolutionary Simulation” (Preprint)
