In biology, as in life, mistakes happen — and most of them generally make things worse, not better. For example, “mistakes” can occur when cells are dividing and reproducing, as the DNA in one of your cells is copied and split between two cells. These mistakes accumulate as we age, making the cells of an old person more sluggish than the cells of a young person — or worse, cancerous. What’s striking is that such developmental mistakes nevertheless turn out to be essential to the process of evolution — in a good way.
To take a different example, consider the many opportunities for error in gene expression — the process by which a gene gives rise to a protein. Gene expression involves “transcribing” a particular sequence of DNA into a new RNA molecule, which is then “translated” to produce a protein. This is a very complex process. A typical protein is composed of about 500 amino acids, each of which is encoded through a triplet of nucleotides — meaning that 1,500 nucleotides need to be correctly transcribed from DNA to RNA, which must then be correctly translated into a protein. After that, there is even more room for error: this protein then has to be “folded” properly so that it can perform its particular biological functions.
It is remarkable that any proteins come out the right way at all. Errors during the transcription process or the translation process can result in a protein that has the wrong sequence of amino acids. And even if they have the correct sequence, proteins can easily misfold into shapes that not only fail to function, but are toxic to the cell. Indeed, a significant fraction of the activity of every cell is dedicated to what is, in effect, an elaborate garbage-disposal system that cleans up the mess left behind by the large number of flawed proteins that get produced.
These developmental errors during gene expression are distinct from the more familiar germline mutations, in which offspring wind up with a new version of a gene, one that is different from the versions of either parent. Germline mutations are particularly dangerous errors because they don’t just affect single proteins or cells that could be destroyed and recycled; instead, germline mutations permanently affect the whole organism as well as its future children and grandchildren.
Perhaps unsurprisingly, mutations — errors in copying germline DNA — occur much less often than do errors in processes such as transcription or translation. Preventing errors is costly, biologically speaking, and so our bodies invest only as much as they need to in the task. Because the damage from a mutation in the germline is greater than the damage from making a single faulty protein molecule, our costly safeguards to prevent mutations in eggs or sperm are more meticulous than those that ensure quality control during the production of a protein.
While the magnitude of the consequences of germline mutations is so much greater than that of gene-expression errors, their specific effects can be remarkably similar at the molecular level. For example, DNA (deoxyribonucleic acid) molecules, which use an “alphabet” of four nucleotides, abbreviated as G, A, T, and C, are transcribed into RNA (ribonucleic acid) molecules that use a similar alphabet of G, A, U, and C. The similarity between the chemistry of the DNA alphabet and the chemistry of the RNA alphabet means that the effects of a permanent mutation that changes a G to an A in the DNA are similar to the effects of a mistake in transcription that also changes G in the DNA to A in the RNA.
This similarity at the molecular level between germline mutation and errors in gene expression opens up a remarkable opportunity for mutations to get “prescreened” before they even occur —meaning that natural selection can occur prior to mutation. If you have, say, 1011 copies of a protein in your body, and a particular error happens at a rate of 10-5, then 1 million of your copies of the protein, on average, have that same error. So although your genotype encodes what a particular protein is “supposed” to be like, by making errors your body contains not only the “correct” version of the protein coded for by a given gene, but potentially a whole range of other similar — though “incorrect” — versions of that gene’s protein.
With the error rate in this example, that could be enough for natural selection to take notice. To see how, imagine you have a permanent, germline mutation that doesn’t affect how well your protein works in normal cases, when it’s transcribed and translated correctly. But the mutation does change the fraction of error-containing variants that work properly, say from 40 percent to 42 percent. That means slightly less work for your cells’ garbage-disposal system and more fitness for you — making you healthier and more likely to survive and reproduce. In other words, this mutation benefits you, evolutionarily speaking. Natural selection doesn’t just judge how well a gene works when its proteins are produced correctly, but also how well all the error-containing versions of the gene’s proteins work.
Because the same protein variants can be produced either by mutation or by gene expression error, evolution under constant pressure from developmental errors leads to improvements not only in developmental robustness, but also in what biologists call “evolvability” — the capacity to produce genetic variants that help adaptation. For evolution by natural selection to work, a reasonable fraction of genetic mutations has to be not just harmless but also helpful. This seems like a tall order. Aren’t random changes to any highly complex thing likely to break it, rather than improve it? Yes, but they are much less likely to break it, and hence more likely to improve it, if those mutations have already been prescreened.
One of the “incorrect” protein variants your body produces in error today could be the new “normal” tomorrow, if the right permanent germline mutation comes along. So the more resilient your proteins are to the presence of these “mistakes,” the higher the number of viable options that evolution has to work with, and the easier it is to pick out the good ones. Developmental errors provide a kind of playground where future genetic options can be tried out on a small scale, before they are risked on the larger, evolutionary stage.
The evolution of evolvability may seem like a problematic idea at first, because evolution has no foresight. How can evolution know what will be adaptive? On one level, this is true, of course: evolution does not know what will turn out to be useful. But evolution does know what won’t be useful. Genetic variants that increase the likelihood of death or sterility generally fit the bill of uselessness. Breaking essential housekeeping genes that deal with basic cellular functions is also almost always a bad idea, evolutionarily speaking. But the effects of other, less drastic variants are harder to predict, and may depend on the circumstances. Mutations that fit into this latter category tend to be ones that are mostly harmless, mimics of the kind of developmental errors that evolution tolerates. Problematic mutations, by contrast, especially catastrophic ones, resemble at the molecular level the kind of developmental errors that evolution moves far away from.
Developmental errors give evolution a glimpse of the future, so to speak — not enough to tell it where to go, but enough to help it avoid some of the more obviously bad paths. By a process of elimination, whatever is left over after this “prescreening” process can only be better than the original set of choices.
By contrast, if there were no developmental errors, there would be no effective prescreening for robustness and the evolvability that comes with it. Evolution — if it worked at all — might end up taking other, less productive paths, producing proteins so fragile that almost any mutation would destroy them.
Without errors, in other words, evolution’s creativity would be stifled. If we were already perfect biological organisms, evolution would have nowhere to go — no diversity to explore, no fountain of creativity. The takeaway? Embrace the waste, the mess, and the errors — embrace the imperfection. Without it, the diverse wonders of the natural world could not have come to be.
- Could some species be less error-prone than others, and hence less evolvable?
- Have more evolvable genotypes with higher error rates been favored by natural selection, leading to the evolution of evolvability?
- If we allowed computer hardware to make more errors, could software become as evolvable as biological systems?
- How often do small mistakes in our personal and business lives inadvertently give us a preview of some better option — leading to a better outcome than if everything were always done “correctly”? Are these mistakes more like permanent “mutations”, or like “gene expression errors” that provide a miniature preview of what a future mutation might be like?
Two themes that came up in the discussion about developmental mistakes and evolution were: (1) agency/intent, and (2) the challenge of finding the right language to express scientific ideas.
The two are related. A major challenge for the language in which we give popular accounts of evolutionary biology is to avoid making claims about agency that are not warranted by the science. This is partly an issue of the popularization of science: In the original scientific research, many of the ideas are expressed in the language of mathematics, which is naturally far more precise and has a smaller range of connotations.
But this is not exclusively an issue of popularization. Professional scientists don’t think exclusively in mathematical terms; we all are guided by metaphors and narratives to some degree. Moreover, some technical scientific jargon is also drawn from ordinary English words, which can no doubt be confusing to a popular audience. But although their ordinary meaning may be supplanted during the long training scientists go through, this process is bound to be incomplete, with the broader range of connotations retaining some salience for scientists too, not always at a conscious level.
So the discussion points to some new big questions not only about how we express scientific ideas in the public domain, but also about how language and metaphor might influence scientific inquiry itself.
New Big Questions:
- How do we discuss scientific topics in the public domain for which full definitions require mathematics and/or highly technical jargon and when partial definitions can seem to imply things that are not meant?
- If scientists’ insider language is difficult for others to follow, who can best act as a useful outsider, scrutinizing the choice of metaphors and narratives that might subconsciously influence their work?