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The LTEE as meta-experiment: Questions from Jeremy Fox about the LTEE, part 3

EDIT (23 June 2015): PLOS Biology has published a condensed version of this blog-conversation.

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This is the 3rd installment in my responses to Jeremy Fox’s questions about the LTEE (my lab’s long-term evolution experiment with E. coli), which he asked at the Dynamic Ecology blog. This response addresses his 2nd and 7th questions, which I’ve copied below. I like all of Jeremy’s questions, but I especially like his 2nd one because it forced me—and many readers, I hope—to think carefully about what experiments are and why we do them.

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  • Is the LTEE actually an experiment, and wouldn’t it have been even better if it was? It’s just one “treatment”–12 replicates of a single set of conditions. Wouldn’t it have been even more interesting to have, say, two treatments? Two different culture conditions, two different founding genotypes, two different founding species…?
  • Is the LTEE itself now a “model system”? Model systems in biology–systems in which it’s tractable to ask a given question–often are systems that we know a lot about. We can leverage that background knowledge to ask questions that otherwise wouldn’t be tractable. coli of course is a model organism for many purposes, because we know so much about it. But is the LTEE itself now a model system?

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You’re certainly right, Jeremy, that experiments in the fields of ecology and evolutionary biology typically have two or more treatments. But it’s not an essential part of the definition of an experiment that it has that sort of structure. It would have been nice, perhaps, if the LTEE did have two or more environments and/or two or more ancestors, as you suggest—in fact, we’ve run several of those types of experiments over the years in my lab, and I’ll mention a few of them below.

The reason I didn’t do that with the LTEE, though, was because one of my core motivating questions (see part 2 of my response) concerned the repeatability of evolutionary dynamics across replicate populations. That’s a question about the trajectory of variances over time, which is challenging statistically because estimates of variances have large uncertainties. So if the LTEE had two treatments, I might have been able to say something meaningful about the differences between them, but I would have had less power to say anything about the among-replicate variances for either treatment. In other words, with respect to that motivating question, going from 12 replicate populations down to 6 replicates would have been risky.

It certainly would be nice to have more total populations, say, 24 or even more; and nowadays many labs use 96-well plates for evolution experiments, with each well a replicate population and liquid-handling robots to automate the transfers. When I started the LTEE, though, we worked with flasks (albeit small ones); 12 may not seem like too many, but when we run the competition assays to measure fitness, we then have replicate assays for each population and we analyze multiple generations simultaneously, so the students and postdocs running these assays are handling many dozens or even hundreds of flasks.

The LTEE as a meta-experiment

Stepping back a bit, I’d like to suggest that the LTEE is a sort of meta-experiment, to coin a term. (This idea echoes the question where you suggested that the LTEE has itself become a model system.) By “meta” I mean the LTEE transcends—goes above and beyond—what one usually considers an experiment because the LTEE enables experimentation at several levels.

Level 1: The LTEE as an experiment

First, it is an experiment in the sense that it set out to measure, under defined conditions and with replication, certain specific quantities, such as fitness trajectories. It may not be typical in having a single treatment, but the temporal dimension coupled with being able to analyze multiple time points simultaneously—that is, the “time travel” enabled by the frozen samples across the generations, including the use of the ancestral strain as an internal control in fitness assays—functions in much the same way from an analysis standpoint.

Level 2: The LTEE as a generator of new questions and experiments to answer them

Second, the LTEE has generated a number of new questions and hypotheses that are themselves amenable to structurally independent follow-on experiments. Let me give two examples. We observed fairly early on that several populations had evolved changes in their DNA metabolism and repair that caused their mutation rates to increase by roughly 100-fold (Sniegowski et al. 1997). Such “mutator” mutations can arise by hitchhiking, albeit only occasionally and stochastically, with beneficial mutations that they cause (Lenski 2004, see pp. 246-251). It wasn’t clear, though, whether they would necessarily increase the rate of fitness improvement, given the large populations and correspondingly large potential supply of beneficial mutations in the LTEE. So we designed a separate, shorter-duration experiment with some 48 populations where we varied the mutation rate, population size, and initial fitness of the founding ancestor, and assessed the resulting fitness gains over 1,000 generations (de Visser et al. 1999).

Another case is the “replay” experiments that Zachary Blount ran after one lineage evolved the ability to grow on citrate in the presence of oxygen, which E. coli generally cannot do (Blount et al. 2008). Zack ran thousands of populations that started from genotypes isolated at different times from the population that eventually evolved this new function, in order to test whether it could have arisen at any time by an appropriate mutation or, alternatively, whether it required first evolving a “potentiated” genetic background, or context, in which the “actualizing” mutation would then confer the citrate-using phenotype.

In both of these examples, the subsequent experiments, though separate and distinct from the LTEE, nonetheless emerged from the LTEE. That is, the questions and hypotheses tested in these later experiments were motivated by observations we had made in the LTEE itself.

Level 3: The LTEE-derived strains as useful ancestors for a variety of experiments meant to address existing questions

The third level of the meta-experiment involves questions that arise outside of the LTEE, but for which the LTEE generates a set of materials—specifically, strains—that are especially useful for experiments to address those questions. Again, I’ll give a couple of examples.

Many ecologists, physiologists, and others are interested in studying adaptation to specific environmental factors—such as resource availability, temperature, etc.—as well as examining possible tradeoffs associated with adaptation to those factors. One difficulty, though, is that by moving organisms from nature into the lab and allowing them to evolve under, say, different temperature regimes, adaptation to the shared features of the lab environments may well outweigh adaptation to the specific variable of interest. If so, that would interfere with one’s ability to identify the mutations and adaptations most relevant to the factor of interest, and it could also obscure tradeoffs that might be important if populations were already well adapted to the other aspects of the environment. With these considerations in mind, Albert Bennett and I took a strain from the LTEE that had evolved in and adapted to those conditions—the resources, pH, absence of predators, etc.—and we used it as the ancestor for a new evolution experiment where 6 replicate populations evolved under each of 4 different thermal regimes: 32C, 37C (the same as in the LTEE), 42C, and daily alternations between 32C and 42C (Bennett et al. 1992, Bennett and Lenski 1993). In that way, we could focus attention on temperature-specific adaptations, which were Al’s main interest, rather than having such changes overwhelmed by adaptation to the lab environment.

My second example where LTEE-derived strains were ancestors for an experiment meant to address an extrinsic question is one of an abstract nature. In this study, we quantitatively partitioned the effects of adaptation, history, and chance on phenotypic evolution by founding 3 replicate populations from 12 different ancestors—each one a genotype sampled from a different one of the LTEE populations—and we then let these 36 populations evolve in a new environment, where we changed the identity of the limiting nutrient (Travisano et al. 1995). By measuring the fitness of the 12 ancestors and 36 derived lines in the changed environment, we were able to disentangle and quantify the relative contributions of adaptation, history, and chance to the observed outcomes (see figure below). That is, adaptation measured the mean tendency for fitness to increase, history reflected the effect of the different starting genotypes on the fitness achieved, and chance the variation in the resulting fitness among the replicates that started from the same ancestor.

Sniegowski, P. D., P. J. Gerrish, and R. E. Lenski. 1997. Evolution of high mutation rates in experimental populations of Escherichia coli. Nature 387:703-705.

Lenski, R. E. 2004. Phenotypic and genomic evolution during a 20,000-generation experiment with the bacterium Escherichia coli. Plant Breeding Reviews 24:225-265.

De Visser, J. A. G. M., C. W. Zeyl, P. J. Gerrish, J. L. Blanchard, and R. E. Lenski. 1999. Diminishing returns from mutation supply rate in asexual populations. Science 283:404-406.

Blount, Z. D., C. Z. Borland, and R. E. Lenski. 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl. Acad. Sci. USA 105:7899-7906.

Bennett, A. F., R. E. Lenski, and J. E. Mittler. 1992. Evolutionary adaptation to temperature. I. Fitness responses of Escherichia coli to changes in its thermal environment. Evolution 46:16-30.

Bennett, A. F., and R. E. Lenski. 1993. Evolutionary adaptation to temperature. II. Thermal niches of experimental lines of Escherichia coli. Evolution 47:1-12.

Travisano, M., J. A. Mongold, A. F. Bennett, and R. E. Lenski. 1995. Experimental tests of the roles of adaptation, chance, and history in evolution. Science 267:87-90.

[The figure below appeared in Science (Travisano et al. 1995), and it is reproduced here under the doctrine of fair use.]

Adaptation, chance, history image

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On Time and Space

The long-term evolution experiment (LTEE) began in 1988, and the E. coli populations are approaching 60,000 generations.  That’s a long time for an experiment, and I hope it continues for much, much longer.

But when I give talks about the LTEE, I also try to remind people that 26 years is only a drop in the proverbial bucket of evolutionary time.  If you were to add these experimental populations to the tree of life—or even to a tree showing only other E. coli strains—they would not be visible to the eye because the branches they represent—tiny twigs, really—would be so short (in time) and so close (in genetic distance) to their ancestors.

On Time and the LTEE

Life has existed on Earth for roughly 3.5 to 4 billion years.  That’s about 140 million times longer than the LTEE has existed.  Expressed the other way around, this experiment has been running for about 0.0000007% of the time that life has been evolving on our planet.

As I said, a mere drop in the bucket of time …

That’s a somewhat mixed metaphor, though, with “a drop in the bucket” being a statement about space and relative volumes, not about time.  And that got me wondering about the spatial scale of the LTEE relative to the spatial scale of the biosphere.

If the LTEE is just 0.0000007% as old as life on Earth, what fraction of the space—of the total biovolume—of life on our planet exists in the confines of the LTEE?

On Space and the LTEE

That’s a harder a question to answer.  We know the volume of the LTEE:  there are 12 flasks, one for each of the evolving populations, and each flask contains 10 milliliters (mL) of liquid medium.  (In medicine, by the way, a drop has been defined as 1/20th of a mL, so each flask in the LTEE contains 200 drops.)  If we sum across the populations, then the LTEE occupies 120 mL.

Before you read further:  What’s your quick intuition?  Is the LTEE larger on this spatial scale than on the temporal scale?  Or is the LTEE smaller?

Volumes and Numbers

How should we estimate the volume of Earth’s biosphere?  Here are three back-of-the-envelope approaches to get a rough sense of the scale.

1)  Most of the Earth is covered by its oceans, which are full of life.  While life is not equally abundant throughout the oceans, none of that space is entirely devoid of life.  The total volume of Earth’s oceans is about 1.3 billion cubic km.  That’s a lot of mL!  A mL is a cubic centimeter, or cc, and that’s 1/(100^3) = 1 millionth of a cubic meter.  A cubic meter is 1/(1000^3) = 1 billionth of a cubic kilometer, and the oceans contain over a billion of those cubic kilometers.

So the 120 mL in the LTEE correspond to 120 / (1.3 x 10^9 x 10^9 x 10^6), or about 9 x 10^-22 of what  the oceans contain.  That’s just 0.000000000000000000009% of the volume of the oceans.

By this calculation, then, the temporal scale of the LTEE is ~75 trillion times greater than its spatial scale, when both are expressed relative to nature.  If the LTEE is “a drop in the bucket” with respect to time, then that drop has to be diluted by a factor of 75 trillion with respect to the oceans.

2)  Let’s try another quick-and-dirty calculation.  Most life, in the oceans and on land, is near the Earth’s surface.  The surface area of our planet is about 510 million square kilometers.  If we take just the top meter, that’s equivalent to 510/1000  = 0.51 million cubic kilometers.  That’s about 1/2600 of the volume of the ocean.  But even this conservative estimate of the volume of the biosphere makes the relative scaling of the LTEE with respect to time and space differ by a factor of 30 billion.

3)  Here’s one more approach—it’s based not on the volume of the physical environment but, instead, on the number of organisms in the LTEE and in the biosphere.  When grown to stationary-phase density in the LTEE environment (i.e., when the limiting resource, glucose, is depleted), the ancestral bacteria could achieve a maximum density of ~5 x 10^7 cells per mL.  Most populations have evolved so that they now produce slightly fewer, but larger, cells; and one population has evolved the ability to use the citrate that is also in the medium, and it now reaches a density that is several times greater than the other populations.  In any case, given 10 mL of medium for each population, and 12 populations, the total population size across the LTEE is on the order of 10^10 cells.

And how many cells exist in the Earth’s biosphere?  Whitman et al. (1998, PNAS) estimated that there are more than 10^30 prokaryotes—bacteria and archaea combined—in the biosphere, and they make up the great majority of all living things.

So by this approach, using the number of cells as a proxy for the spatial scale, the size of the biosphere is over 10^20 (a hundred-million-trillion) times larger than the LTEE.  We’re back into the trillions in terms of the relative scaling of the temporal and spatial scales of the LTEE.

On Time, Space, and the LTEE

By all three approaches, then, the LTEE is vastly older with respect to the history of life on Earth than it is large with respect to the size of Earth’s biosphere.

The LTEE really is a long-running experiment, as experiments go.

But the LTEE is a “drop in the bucket” with respect to how long life has been evolving on Earth.  And it is a vastly more miniscule “drop in the bucket” when compared to the spatial extent and number of living organisms on our planet.

Maybe I should give the LTEE a new name—the “incredibly tiny but relatively long-term evolution experiment.”

[Photo of a water drop on a leaf taken by tanakawho and shared on Wikipedia (en.wikipedia.org/wiki/File:Water_drop_on_a_leaf.jpg).]

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Lenski Interview with The Molecular Ecologist

John Stanton-Geddes asked me some great questions for a series on “People Behind the Science” at The Molecular Ecologist blog.  He gave me permission to repost the interview here.

1) Did you always think you’d become an evolutionary biologist?

No!  I always enjoyed being outdoors (sports and hiking), but I didn’t have any particular interest in biology.  However, my mother (who dropped out of college when she married, but then co-authored a sociology textbook with my father) was very interested in biology.  She would give me articles she had read and enjoyed from Natural History and elsewhere.

I went to Oberlin College, where I thought that I might major in government.  But I disliked my first government class.  I also took a team-taught biology class for non-majors.  All of the instructors spoke on topics about which they cared deeply, and I was hooked!  I took more biology courses, and I was especially drawn to ecology because there were so many ideas and questions.  At that time, I wrongly viewed evolutionary biology as a more descriptive, old-fashioned field with fewer questions that one might still address.  (By the way, several other evolutionary biologists were at Oberlin when I was there including Deborah Gordon, Joe Graves, Kurt Schwenk, and Ruth Shaw. Not bad for a small school!)

I went to graduate school at the University of North Carolina, where Nelson Hairston, Sr., was my advisor.  Nelson was interested in the interface of ecology and evolution, and that opened my eyes.  I was also influenced by Janis Antonovics, then at Duke University.  I took his Ecological Genetics course, and he served on my committee.  Janis had written a paper in which he argued that “The distinction between ‘ecological time’ and ‘evolutionary time’ is artificial and misleading.”  That really got me thinking.  I tried to develop a couple of field-based projects that would address evolutionary questions, but I didn’t know what I was doing and they failed.  In the end, my dissertation project was pure ecology.

By then, though, I knew I wanted to pursue evolutionary biology.  While we were finishing our doctoral projects, a fellow grad student Phil Service and I spent a lot of time discussing model systems for studying evolution.  For his postdoc, Phil chose to work with Drosophila.  I recalled an undergrad course in which we read about elegant experiments with microbes that addressed fundamental questions, such as one by Salvador Luria and Max Delbrück showing that mutations happen at random and not in response to selection.  Meanwhile, in a graduate seminar, we read a paper by Lin Chao and Bruce Levin on the coevolution of bacteria and viruses.  I wrote Bruce to ask if he might have an opening for a postdoc.  Lucky for me, Bruce knew Nelson and invited me for a visit.

2) You’ve described the theme of your research as “the tension between chance and necessity”. Can you comment on how chance and necessity have shaped your career?

The ancient Greek philosopher Democritus said, “Everything existing in the universe is the fruit of chance and necessity.”  In my long-term evolution experiment with E. coli, we can explore the tension between chance and necessity because we have replicate populations started with the same ancestor and evolving under identical conditions, and because we can replay evolution from different points along the way.  But it’s difficult, if not impossible, to tease apart the roles of chance and necessity with a sample size of one, which is the life that each of us has experienced, and without the ability to replay our own lives.  (On that last point, let me recommend Replay, a science-fiction novel by Ken Grimwood.)

I would say, though, that most people who have had some success in their adult lives also started out very lucky.  We were fortunate to be born at times and in places where we had food, familial love, education, and opportunity.

3) Reading your blog it’s clear that you are a student of the philosophy and history of science. Do you think we should include more history and philosophy in scientific training? Any advice on something we should all go out and read?

I do think that the history and philosophy of science deserve more emphasis in science and education than they usually receive.  But I didn’t have any formal education in those areas.  Instead, I became interested in these issues through teachers, mentors, colleagues, and my own explorations.

For something to read in this area, I suggest Darwin’s Century by Loren Eiseley.  (Originally published in 1958, it was republished in 2009 by Barnes & Noble.)  The book discusses the fascinating history of evolutionary thought in the decades before and after the publication of The Origin of Species.  I first read Darwin’s Century in a course at Oberlin taught by James Stewart.

4) If you were starting your career today, what would you study? 

If I were starting today, and at my present age, I might choose to study the history of science, especially evolutionary biology and its antecedents.

But if I were starting out young, as one usually does, I’d like things to unfold as they did.  It might be tempting to skip the rough patches, but dissatisfaction with my early research led me to make the switch to microbial evolution.  Would I have enjoyed this lab-based work as much, if I hadn’t discovered that I was not nearly as good at fieldwork as many of my peers?

5) How close have you come to giving up as a researcher and doing something completely different?

The job market was tough when I was a postdoc, and I had a growing family to support.  So after a slew of applications and rejections, and a period of uncertain funding, I started to think about other possibilities.  Luckily for me, things turned around before I had to make a switch.  (You can read more about it in my blog post, The Good Old Days.)

6) What’s the meaning of life?

I think that some understanding of evolution—at a basic level accessible to anyone with an open mind and a decent education—gives perspective about our place, both as individuals and as a species, in the grand sweep of time and space.  Recognizing the transience of my personal existence fills me with awe and respect for the continuity of life and ideas.  And belonging to a species that is profoundly altering the world that enabled the continuity of life reminds me of our responsibility for ensuring its future.

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Fifty-Thousand Squared

I’ve been thinking a lot about the long-term evolution experiment (LTEE) with E. coli lately – even more than usual.  One impetus has been the paper by Mike Wiser, Noah Ribeck, and me that appeared today (14-Nov-2013) in Science (online publication in advance of print).  Another reason is that I’m working on the competitive renewal for the NSF grant that funds this experiment.

The Experiment that Keeps on Giving

Both of these have got me thinking about the long-term fate of this long-term experiment.  Should the experiment continue?  For how long should it continue?  Who will take it over when (or before) I retire?  And after that person retires, then what?  How will they sustain it?  Will they rely on the usual competitive grants?  Would an endowment be more suitable?  How does one raise an endowment?

I like to say that the LTEE is the experiment that just keeps on giving.  Between the element of time, the inventiveness of the bacteria (even in their simple, confined, little flask worlds), and the many talented students, postdocs, and collaborators who have worked on the LTEE, there seems to be no end to the insights this experiment can provide into fundamental questions about evolution.  Why shouldn’t this experiment keep on giving, even after I’m gone?

When I started the LTEE in February of 1988, I had no idea that it would continue for more than 50,000 generations.  I had previously done some other experiments that went for a few hundred generations, and I intended this one to run for at least 2,000 generations.  That would deserve the “long-term” moniker.  Although we made some fitness measurements along the way, most of the hard work comes after a milestone is reached.  That’s when one begins the intensive assays to quantify the changes that occurred.  And while we performed those assays, we continued the daily transfers.  So by the time the first paper was prepared, submitted, reviewed, revised, and published in December of 1991, the LTEE was past 5,000 generations.  And so it has gone:  new milestones, new questions, new assays, new data, new analyses, and new papers.

And the new questions keep coming based on new hypotheses of students, postdocs, and collaborators (occasionally even me), new technologies such as genome sequencing, and new observations of what the evolving bacteria have done.

Fitness Unlimited

This latest paper is an interesting one because it uses our most old-fashioned assays – the kind that was the heart of the LTEE when it started, and which also formed the core of that first paper back in 1991.  That is, the results are based on measurements of relative fitness, coupled with new models – both descriptive and dynamical.  (Although this blog post emphasizes the descriptive model, the Science paper also presents new theory showing that the descriptive model can be derived from a dynamical model of evolution that incorporates two phenomena – clonal interference and diminishing-returns epistasis – that are known to occur in the LTEE and other studies of evolving asexual populations.)

Fitness is the central phenotype in evolutionary theory; it integrates and encapsulates the effects of all mutations and their resulting phenotypic changes on reproductive success.  Fitness depends, of course, on the environment, and here we measure fitness in the same medium and other conditions as used in the LTEE.  We estimate the mean fitness of a sample from a particular population at a particular generation by competing the sample against the ancestral strain, and we distinguish them based on a neutral genetic marker.  Prior to the competition, both competitors have been stored in a deep freezer, then revived, and acclimated separately for several generations before they are mixed to start the assay proper.  Fitness is calculated as the ratio of their realized growth rates as the ancestor and its descendants compete head-to-head under the conditions that prevailed for 500 … or 5000 … or 50,000 generations.

The exciting new result is that the fitness of these evolving bacteria shows no evidence of an upper bound or asymptote.  A two-parameter power law fits the data much better than does a two-parameter hyperbolic model.  According to both models, the rate of fitness increase decelerates over time, as it clearly does.  However, the power-law model has no asymptote, whereas the hyperbolic model has an upper bound.

Even more striking and important, to my mind, is that the models differ in their predictive power.  We fit these two models to truncated datasets that included only the first 20,000 generations of data and asked how well they predicted the fitness values observed over the next 30,000 generations of data.  The unbounded power law beautifully predicts the fitness trajectory that it had not seen, whereas the asymptotic hyperbolic model underestimated later measurements.  The underestimation of the asymptotic model becomes progressively worse as the temporal data are more and more truncated; that is, the evolving bacteria consistently pass right through the “limit” predicted from previous data.  By contrast, even with only 5000 generations of data, the power-law model very nicely predicts the fitness trajectory all the way out to 50,000 generations.

A Thought-Experiment

How long can this continue?  In our paper, we present the following thought-experiment.  I’ve overseen 50,000 generations of the LTEE in my scientific life; now imagine another 49,999 generations of scientists, each one overseeing 50,000 more bacterial generations. That’s 50,000^2 generations, or 2.5 billion generations in total.  (It will take about a million years to get there.)  You’re probably thinking that the unbounded power-law model must predict some crazy high fitness that would imply a ridiculously fast growth rate.

In fact, the power law predicts that fitness relative to the ancestor will increase from ~1.7 after 50,000 generations to ~4.7 after 2,500,000,000 generations.  If the bacteria eliminate the lag time associated with the transition from starvation to growth each day (which they have already largely done), then a fitness value of 4.7 implies that the bacteria will have to reduce their doubling time from the ancestor’s ~55 minutes to ~23 minutes.  That’s very fast given that the LTEE uses a minimal medium where cells must synthesize everything from glucose, ammonium, and a few other molecules.  But it’s not so fast that it suggests the bacteria would violate some biophysical constraint.  Indeed, some bacteria can grow twice that fast, albeit in a nutrient-rich medium.

What Does the Future Hold?

I’d really like science to test this prediction!  How often does evolutionary biology make quantitative predictions that extend a million years into the future?  Maybe the LTEE won’t last that long, but I see no reason that, with some proper support, it can’t reach 250,000 generations.  That would be less than a century from now.  If the experiment gets that far, I’d like to propose that it be renamed the VLTEE – the very long-term evolution experiment.

And this prediction about the future fitness trajectory is not the only – or even the main – reason to keep the LTEE going.  Some important things in evolution simply require a lot of time.  In my presidential address to the Society for the Study of Evolution this past summer, I highlighted three findings where it proved to be important that the LTEE had continued for many years (and, if I’d had more time, I could have added more to that list).  First, it takes a very long time series to distinguish between asymptotic and non-asymptotic fitness trajectories.  Second, it took over 30,000 generations before the most dramatic phenotypic change occurred in one of the populations, which evolved the ability to use citrate – which has been present in the medium of the LTEE throughout its duration – as a second source of energy.  Third, postdoc Zachary Blount is currently studying whether the refinement of that new function is leading to changes that would qualify the citrate users as a new, or incipient, species.

What other new traits might the bacteria evolve?  Could they evolve some means of genetic exchange?  Might the within-population competitive interactions ever take a turn toward predation?  Who knows?  Only time will tell – and only if we allow time, the bacteria, and future generations of scientists to do the work of evolution and science.

Some Additional Links

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To Edinburgh and Back

I had a terrific visit to Scotland last week.  The graduate students in the Institute of Evolutionary Biology at the University of Edinburgh invited me to speak there.  Thanks especially to Manon Ragonnet and Sam Lewis for organizing and hosting my visit.

It seems a bit extravagant to fly overseas for a one-hour lecture.  But there was much more to my visit than just the lecture.  I spent a day and a half in meetings with students and faculty, where we discussed their projects and plans, and I was impressed by the quality of science all around.  Before my visit, I hadn’t realized how many groups in Edinburgh were working on microbial evolution, including the evolution of infectious diseases and experimental evolution.

I spent my last day in Scotland with over a dozen graduate students hiking around and up North Berwick Law, a once-volcanic peak near the Firth of Forth.  The weather was beautiful during my first two days in Edinburgh, but for this day out, we were treated to “authentic” Scottish weather.  Through the fog and rain, though, we could make out the shoreline and a couple of the nearer islands.  Better yet, the company was great, full of small talk and big ideas.

I spoke on “Time Travel in Experimental Evolution” – a 50-minute overview of the long-term evolution experiment, or LTEE, in which we’ve observed and analyzed more than 50,000 generations in the lives of 12 populations of E. coli, all founded from the same ancestral strain and all evolving in, and adapting, to the same, simple lab environment.

That works out to some 1,000 generations per minute, so I had to speak really fast.

Seriously, though, it’s a challenge to decide what to include, and hence what to exclude, from an experiment that’s been running for more than 25 years.  Besides presenting some results, I need to explain the original motivation for the experiment; describe its structure, rhythm, and a few key methods; and leave time to summarize the findings and acknowledge the people who’ve done the work.

I also need to have both a narrative and a theme that flow through the talk.  In my talk, the narrative revolves around time travel:  how we use the rapid generations of bacteria, the ability to freeze and revive cells to compare organisms that lived at different times, and the opportunity to replay evolution by restarting from past generations.  The theme is the tension between chance and necessity – between idiosyncratic and repeatable events – and what these 12 populations can tell us about the processes that govern evolution.

I always include some of the latest results that are on my mind, but which haven’t yet been published or, in some cases, even fully analyzed.  Out of necessity, though, many exciting but older results don’t get mentioned in the ever-evolving versions of my talk.

There’s a longer story to be written about the LTEE, one that pulls together all the findings, old and new.  I keep holding off, though, because there’s always another big result or two just around the corner.  After we’ve nailed those down, then I can pull everything together.  But along comes another major new finding, and another …

I like to say that the LTEE is the experiment that just keeps on giving.  Between the element of time, the inventiveness of the bacteria (even in their tiny confined worlds), and the many talented and dedicated students, postdocs, and collaborators who’ve worked on the LTEE, there appears to be no end in sight.

Indeed, when I got home, I had an email from a journal editor, asking my coauthors and me to revise a paper.  Back to work!

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Linguistic Relativity

Sam Magruder traveled alone back in time.

But what would you call friends who time-travel together?  Decronies because they travel in time?  Or syncronies because they travel together?

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The Dechronization of Sam Magruder

Dechronization means time travel.  Think of synchronization as joining two things in time.  Now decouple them, and you have the idea of time travel.

The word dechronization is a neologism coined by the paleontologist George Gaylord Simpson (1902-1984).  But he did not use the word in the context of his research.  Instead, he coined the word for a short science fiction novel that he wrote, which was only published posthumously, in 1996, after his daughter discovered it years later.

The novel is called The Dechronization of Sam Magruder.  It’s about a “research chronologist” working on quantum theory.  In the year 2162, an experiment goes awry and Magruder finds himself back in the age of dinosaurs.  While struggling to survive, he keeps a diary (stone tablets, of course) and wonders whether he should interfere with future history by helping the scurrying little mammals – perhaps his own ancestors! – avoid being devoured by the dinosaurs.  Being a smart guy, Magruder leaves his tablets in fossil-prone sites where future scientists will be more likely to find them … which they do.

It’s a fun read, especially if you’re interested in quirky old books that have something to do with how scientists and philosophers think about the world.

If you find yourself in the possession of an autographed copy of The Dechronization of Sam Magruder, there are three possibilities.  Simpson really did figure out time travel … or it’s a forgery … or else I signed it*.

The book is readily available from on-line used-book sellers, and for only a few bucks in pristine condition.  The book has an introduction penned by Arthur C. Clarke and an afterword by Steven Jay Gould.  But perhaps the hideous dust jacket scared people away?

*I gave the presidential address at this summer’s meeting of the Society for the Society of Evolution, and I titled my talk “The Dechronization of E. coli: A 25-Year Love Story.”  I began with a quiz about what founding member of the society had written a science fiction novel.  Before I could even offer the first of several hints I had prepared, two graduate students simultaneously called out the answer.  The prize was Simpson’s book (luckily I had brought two copies), and I autographed one student’s copy.

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