The LTEE started on February 17, 1988. That was 11,517 days ago.
I was born on August 13, 1956. That was 23,034 days ago.
That means that the LTEE is now half as old as I am.
To put it another way, I’ve spent half a lifetime on the LTEE.
Well, that’s not quite the right way to put it, since I’ve done a few other things during that time. Like raising a family—with a lot of help. And a lot of other science, also with a lot of help, not to mention all the work of so many students and collaborators on the LTEE itself.
Here are a couple of photos from around the time the LTEE started. The first one shows Madeleine and me camping near Joshua Tree National Park in the summer of 1987, at the annual retreat of the UC-Irvine EEB department, and only a couple months before the birth of our youngest. The next one shows me snuggling with my three kids in early 1989.
How time flies. Luckily, though, I get to snuggle with my three grandkids now.
Bacterial generations. Human generations. Growing, evolving, and learning.
Michael Behe has a new book coming out this month called Darwin Devolves. Nathan Lents, Joshua Swamidass, and I wrote a review of that book for the journal Science. (You can also find an open-access copy of our review here.) It provides an overview of the problems we see with his thesis and interpretations. As our review states, Behe points to many examples of evolution in which genes and their functions have been degraded, but he largely ignores the ways that evolution generates new functions and thereby produces complexity. That’s a severe problem because Behe uses the evidence for the ease of gene degradation to support his overarching implication that the current scientific understanding of the mechanisms of evolution is inadequate and, consequently, the field of evolutionary biology has a “big problem.”
I won’t attempt to summarize Behe’s entire book nor our short review, as people can read those for themselves if they want. Instead, I hope to accomplish three things in this post and two more that will follow. In this first post, I explain why Behe’s so-called “first rule of adaptive evolution” does not imply what he says it does about evolution writ large. In the second post, I’ll discuss whether my long-term evolution experiment (the LTEE for short) does or doesn’t provide strong support for Behe’s position in that regard. In my third post, I’ll explain why I think that Behe’s positions, taken as a whole, are scientifically untenable.
I. Behe’s “First Rule of Adaptive Evolution” Confounds Frequency and Importance
Behe’s latest book is centered around what he calls “The First Rule of Adaptive Evolution: Break or blunt any gene whose loss would increase the number of offspring.” As he wrote in an immediate, dismissive response to our review: “The rule summarizes the fact that the overwhelming tendency of random mutation is to degrade genes, and that very often is helpful. Thus natural selection itself acts as a powerful de-volutionary force, increasing helpful broken and degraded genes in the population.”
Let’s work through these two sentences, because they concisely express the thrust of Behe’s book. The first sentence regarding “the tendency of random mutation” is not too bad, though it is overly strong. I would tone it down as follows: “The tendency of random mutation is to degrade genes, and that is sometimes helpful.” My reasons for these subtle changes are that: (i) many mutations are selectively neutral or so weakly deleterious as to be effectively invisible to natural selection; (ii) while loss-of-function mutations are sometimes helpful to the organism, I wouldn’t say that’s “very often” the case (though it may be in some systems, as I’ll discuss in part II); and (iii) even those degradative mutations that are not helpful on their own sometimes persist and occasionally serve as “stepping stones” on the path toward new functionality. This last scenario is unlikely in any particular instance, but given the prevalence of degrading mutations it may nonetheless be important in evolution. (This scenario does not fit neatly within the old-fashioned caricature of Darwinian evolution as only proceeding by strictly adaptive mutations, but it is certainly part of modern evolutionary theory.)
Behe’s next sentence then asserts the power of the “de-evolutionary” process of gene degradation. This is an unjustifiable extrapolation, yet it is central to Behe’s latest book. (It’s not the sort of error I would expect from anyone who is deeply engaged in an earnest effort to understand evolutionary science and present it to the public.) Yes, natural selection sometimes increases the frequency of broken and degraded genes in populations. But when it comes to the power of natural selection, what is most frequent versus most important can be very different things. What is most important in evolution, and in many other contexts, depends on timescales and the cumulative magnitude of effects. As a familiar example, some rhinoviruses are the most frequent source of viral infections in our lives (hence the expression “common cold”), but infections by HIV or Ebola, while less common, are far more consequential.
Or consider an investor who bought stocks in 100 different companies 25 years ago, of which 80 have been losers. Ouch? Maybe not! A stock can’t lose more than the price that was paid for it, and so 20 winners can overcome 80 losers. Imagine if that investor had picked Apple, for example. That single stock has increased in value by well over 100-fold in that time, more than offsetting even 80 total wipeouts all by itself. (In fact, research on the stock market has shown the vast majority of long-term gains result from a small minority of companies that, like Apple, eventually become big winners.)
In the same vein, even if many more mutations destroy functions than produce new functions, the latter category has been far more consequential in the history of life. That is because a new function may enable a lineage to colonize a new habitat or realm, setting off what evolutionary biologists call an “adaptive radiation” that massively increases not only the numbers of organisms but, over time, the diversity of species and even higher taxa. As one example, consider Tiktaalik or some relative thereof, in any case a transitional kind of fish whose descendants colonized land and eventually gave rise to all of the terrestrial vertebrates—amphibian, reptiles, birds, and mammals. That lineage left far more eventual descendants (including ourselves), and was far more consequential for the history of life on Earth, than 100 other lineages that might have gained a transient advantage by degrading some gene and its function before eventually petering out.
Asteroid impacts aren’t common either, but the dinosaurs (among other groups) sure felt the impact of one at the end of the Cretaceous. (There remains some debate about the cause of that mass extinction event, but whatever the cause its consequences were huge.) Luckily for us, though, some early mammals survived. Evolution often leads to dead ends, sometimes as a consequence of exogenous events like asteroids, and other times because adaptations that are useful under a narrow set of conditions (such as those caused by mutations that break or degrade genes) prove vulnerable over time to even subtle changes in the environment. It has been estimated that more than 99% of all species that have ever existed are now extinct. Yet here we are, on a planet that is home to millions of diverse species whose genomes record the history of life.
Summing up, Behe is right that mutations that break or blunt a gene can be adaptive. And he’s right that, when such mutations are adaptive, they are easy to come by. But Behe is wrong when he implies these facts present a problem for evolutionary biology, because his thesis confuses frequencies over the short run with lasting impacts over the long haul of evolution.
[The picture below shows the Tiktaalik fossil discovered by Neil Shubin and colleagues. It was posted on Wikipedia by Eduard Solà, and it is shown here under the indicated Creative Commons license.]
This post follows up on my post from yesterday, which was about choosing a dilution factor in a microbial evolution experiment that avoids the loss of too many beneficial mutations during the transfer bottleneck.
If we only want to maximize the cumulative supply of beneficial mutations that survive dilution, then following the reasoning in yesterday’s post, we would choose the dilution factor (D) to maximize g Ne = (g2) Nmin = (g2) Nmax / (2g), where Nmax is a constant (the final population size) and D = 1 / (2g). Thus, we want to maximize (g2) / (2g) for g > 0, which gives g = ~2.885 and D = ~0.1354, which is in agreement with the result of Wahl et al. (2002, Genetics), as noted in a tweet by Danna Gifford.
The populations would therefore be diluted and regrow by ~7.4-fold each transfer cycle. But as discussed in my previous post, this approach does not account for the effects of clonal interference, diminishing-returns epistasis, and perhaps other important factors. And if I had maximized this quantity, the LTEE would only now be approaching a measly 29,000 generations!
So let’s not be purists about maximizing the supply of beneficial mutations that survive bottlenecks. There’s clearly also a “wow” factor associated with having lots and lots of generations. This wow factor should naturally and powerfully reflect the increasing pleasure associated with more and more generations. So let’s define wow = ge, which is both natural and powerful. Therefore, we should maximize wow (g2) / (2g), which provides the perfect balance between the pleasure of having lots of generations and the pain of losing beneficial mutations during the transfer bottlenecks.
It turns out that the 100-fold dilution regime for the LTEE is almost perfect! It gives a value for wow (g2) / (2g) of 75.93. You can do a tiny bit better, though, with the optimal ~112-fold dilution regime, which gives a value of 76.03.
Every day, we propagate the E. coli populations in the long-term evolution experiment (LTEE) by transferring 0.1 ml of the previous day’s culture into 9.9 ml of fresh medium. This 100-fold dilution and regrowth back to stationary phase—when the bacteria have exhausted the resources—allow log2 100 = 6.64 generations (doublings) per day. We round that to six and two-thirds generations, so every 15 days equals 100 generations and every 75 days is 500 generations.
A few weeks ago, I did the 10,000th daily transfer, which corresponds to 66,667 generations. Not bad! But as I was walking home today, I thought about one of the decisions I had to make when I was designing the LTEE. What dilution factor should I use?
If … if I had chosen to use a 1,000-fold dilution instead of a 100-fold dilution, the LTEE would be past 100,000 generations. That’s because log2 1,000 = ~10 generations per day. In that case, we’d have reached a new power of 10, which would be pretty neat. As it is, it will take us (or rather the next team to take over the LTEE) another 14 years or so to get there.
I’ll discuss my thinking as to why I chose a 100-fold dilution factor in a bit. But first, here’s a question for you, which you can vote on in the poll below.
Let’s say that we had done a 1,000-fold daily dilution all along. And let’s say we measured fitness (relative to the ancestral strain, as we usually do) after 10,000 days. Do you think that the mean fitness of the evolved populations subjected to 1,000-fold dilutions after 100,000 generations (on day 10,000) would be higher or lower than that of the evolved populations subjected to 100-fold dilutions after 66,667 generations (also day 10,000)?
I’ll begin by mentioning a couple of practical issues, but then set them aside, as they aren’t so interesting. First, a 100-fold dilution is extremely simple to perform given the volumes involved (i.e., 0.1 and 9.9 ml). And the LTEE was designed to be simple, in order to increase its reliability. A 1,000-fold dilution isn’t quite as easy, as it involves either an intermediate dilution or the transfer of a smaller volume (0.01 ml), which in my experience tends to be a bit less accurate. Second, the relative importance of the various phases of growth—lag, exponential, transition, and stationary—for fitness would change a bit (Vasi et al., 1994).
Setting those issues aside, here was my thinking about the dilution factor when I planned the LTEE. In asexual populations that start without any standing genetic variation, the extent of adaptive evolution depends on both the number of generations and the supply rate of beneficial mutations. The supply rate of beneficial mutations, in turn, depends on the mutation rate (m) times the fraction of mutations that are beneficial (f) times the effective population size (Ne).
There are many different uses and meanings of effective population size in population genetics, depending on the problem at hand: the question is “effective” with respect to what process? Without going into the details, we would like to express Ne such that it takes into account the expected loss of beneficial mutations during the daily dilutions. To a first approximation, theory shows that the relevant Ne is equal to the product of the “bottleneck” population size right after the dilution (Nmin) and the number of generations (g) between Nmin and the final population size during each transfer cycle (Lenski et al., 1991).
The final population size in the LTEE is ~5 x 108 cells (10 ml x 5 x 107 cells per ml), and it is the same regardless of the dilution factor, provided that the bacteria have enough time to reach that density between transfers. The 1,000-fold dilution regime would reduce Nmin by 10-fold relative to the 100-fold regime, although the 50% increase in the number of generations per cycle would offset that reduction with respect to the effective population size. Nonetheless, Ne would be ~6.7-fold higher in the 100-fold regime than in the 1,000-fold regime.
The greater number of generations in 10,000 days under the 1,000-fold regime would also increase the cumulative supply of beneficial mutations by 50%. Nonetheless, the extent of adaptive evolution, which is (under this simple model) proportional to the product of the elapsed generations and Ne, would be ~44% greater under the 100-fold dilution regime than the 1,000-fold dilution regime. So that’s why I chose the 100-fold dilution regime … I was more interested in making sure we would see substantial adaptation than in getting to a large number of generations.
Now you know why the LTEE has only reached 67,000 or so generations.
Of course, I could also have chosen a 10-fold regime, and by this logic the populations might have achieved even higher fitness levels. I could also have chosen a much higher dilution factor; even with a 1,000,000-fold dilution the ancestral strain could double 20 times in 24 h, allowing them to persist. Or at least they could persist for a while. With severe bottlenecks, natural selection becomes unable to prevent the accumulation of deleterious mutations by random drift, so that fitness declines. And if fitness declines to the degree that the populations can no longer double 20 times in 24 h, then the bacteria would go extinct as the result of a mutational meltdown.
Returning to the cases where the bottlenecks are not so severe, the theory that led me to choose the 100-fold dilution regime ignores a number of complicating factors, such as clonal interference (Gerrish and Lenski, 1998; Lang et al., 2013; Maddamsetti et al., 2015) and diminishing-returns epistasis (Khan et al., 2011; Wiser et al., 2013; Kryazhimskiy et al., 2014). It’s predicated, I think, on the assumption that the supply rate of beneficial mutations limits the speed of adaptation.
When the LTEE started, I had no idea what fraction of mutations would be beneficial. I think it was generally understood that beneficial mutations were very rare. But the LTEE and other microbial evolution experiments have shown that beneficial mutations, while rare, are not so rare as we once thought, especially once an experiment has run long enough (Wiser et al., 2013) or otherwise been designed (Perfeito et al., 2007; Levy et al., 2015) to allow beneficial mutations with small effects to be observed and counted.
So I think it remains an open question whether my choice of the 100-fold dilution regime was the right one, in terms of maximizing fitness gains.
And that makes me think about redoing the LTEE. OK, maybe not starting all over, as we do have a fair bit invested in the last 29 years of work. But maybe expanding the LTEE on the fly, as it were. We could, for example, expand from 12 populations to 24 populations without too much trouble. We’d keep the 12 original populations going, of course, but we’d spin off 12 new ones in a paired design (i.e., one from each of the 12 originals) where we changed the dilution regime. What do you think? Is this a good idea for a grant proposal? And if so, what dilution factor would you suggest we add?
Feel free to expand on your thoughts in the comments section below!
Note: See my next post for a bit more of the mathematics, along with a tongue-in-cheek suggestion for combining the effects of the beneficial mutation supply rate and a “wow” factor associated with having lots of generations.