Tag Archives: time travel

Five More Years

The E. coli long-term evolution experiment (LTEE) began in 1988, and it has run for over 32 years with only occasional interruptions. The latest interruption, of course, reflects the temporary closure of my lab during the ongoing coronavirus pandemic. Fortunately, one of the advantages of working with bacteria is that we can freeze population samples and later revive them, which will allow us to resume their daily propagation when it is prudent to do so.  Indeed, we’ve frozen samples of all 12 populations throughout the LTEE’s history, allowing “time travel” to measure and analyze their fitness trajectories, genome evolution, historical contingencies, and more.

Even as the experiment is on ice, the lab team continues to analyze recently collected data, prepare papers that report their findings, and make plans for future work. Their analyses use data collected from the LTEE itself, as well as from various experiments spun off from the LTEE.  Nkrumah Grant is writing up analyses of genomic and phenotypic aspects of metabolic evolution in the LTEE populations.  Kyle Card is examining genome sequences for evidence of historical contingencies that influence the evolution of antibiotic resistance. Zachary Blount is comparing the evolution of new populations propagated in citrate-only versus citrate + glucose media. Minako Izutsu is examining the effects of population size on the genetic targets of selection, while Devin Lake is performing numerical simulations to understand the effects of population size on the dynamics of adaptive evolution.  So everyone remains busy and engaged in science, even with the lab temporarily closed.

Today, I’m excited to announce two new developments.  First, the National Science Foundation (NSF) has renewed the grant that supports the LTEE for the next 5 years. This grant enables the continued propagation of the LTEE lines, the storage of frozen samples, and some core analyses of the evolving populations. The grant is funded through the NSF’s Long Term Research in Environmental Biology (LTREB) Program, which “supports the generation of extended time series of data to address important questions in evolutionary biology, ecology, and ecosystem science.” Thank you to the reviewers and program officers for their endorsement of our research, and to the American public and policy-makers for supporting the NSF’s mission “to promote the progress of science.”

Second, Jeff Barrick joins me as co-PI on this grant for the next 5 years, and I expect he will be the lead PI after that period.  In fact, Jeff and his team will take over the daily propagation of the LTEE populations and storage of the sample collection even before then. I’m not planning to retire during the coming grant period. Instead, this transfer of responsibility is intended to ensure that the LTEE remains in good hands for decades to come. In the meantime, Jeff’s group will conduct some analyses of the LTEE lines even before they take over the daily responsibilities, while my team will continue working on the lines after the handoff occurs.

Several years ago I wrote about the qualifications of scientists who would lead the LTEE into the future: “My thinking is that each successive scientist responsible for the LTEE would, ideally, be young enough that he or she could direct the project for 25 years or so, but senior enough to have been promoted and tenured based on his or her independent achievements in a relevant field (evolutionary biology, genomics, microbiology, etc.). Thus, the LTEE would continue in parallel with that person’s other research, rather than requiring his or her full effort, just like my team has conducted other research in addition to the LTEE.”

Jeff is an outstanding young scientist with all of these attributes. Two years ago he was promoted to Associate Professor with tenure in the Department of Molecular Biosciences at the University of Texas at Austin.  He has expertise in multiple areas relevant to the LTEE including evolution, microbiology, genomics, bioinformatics, biochemistry, molecular biology, and synthetic biology. He directs a substantial team of technicians, postdocs, and graduate students, which will provide ample coverage for the daily LTEE transfers (including weekends and holidays). Last but not least, Jeff has participated in the LTEE and made many contributions to it including:

  • Participated in propagating the LTEE lines and related activities while he was a postdoc in my lab from 2006 to 2010.
  • Authored many papers using samples from the LTEE, including almost all of them that have analyzed genome sequences as well as several recent papers examining the genetic underpinnings of the ability to use citrate that evolved in one lineage.
  • Developed the open-source breseq computational pipeline for comprehensively identifying mutations that distinguish ancestral and evolved genomes.

Someone might reasonably ask if the LTEE will work in the same way when it is moved to another site. The answer is yes: the environment is simple and defined, so it is readily reproduced. Indeed, I moved the LTEE from UC-Irvine to MSU many years ago, the lab has moved between buildings here at MSU, and we’ve shared strains with scientists at many other institutions, where measurements and inferences have been satisfactorily reproducible. As an additional check, Jeff’s team at UT-Austin ran a set of the competition assays that we use to measure the relative fitness of evolved and ancestral bacteria, and we compared the new data to data that we had previously obtained here at MSU. The two datasets agreed well, in line with the inherent measurement noise in assessing relative fitness. Fitness is the most integrative measure of performance of the LTEE populations, and it is potentially sensitive to subtle differences in conditions. These results provide further evidence that, when the time comes, the LTEE can continue its journey of adaptation and innovation in its new home.

Evolve, LTEE, evolve!

LTEE flasks repeating

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Happy birthday, Charles and Abe

Charles Darwin was born into wealth and privilege in England 210 years ago, while across the ocean on the same day Abraham Lincoln was born to a poor family in Kentucky.

Besides the coincidence of their birthdays, there are other interesting connections. Lincoln is known, of course, for preserving the Union and freeing slaves through the Emancipation Proclamation. But Lincoln also signed the law that established the National Academy of Sciences, which provides pro bono scientific advice to the federal government. And while Darwin is known for his work on evolution, he was also a prominent overseas voice in the abolitionist movement. During the voyage of HMS Beagle, Darwin had a heated argument with the captain, Robert FitzRoy, who defended the institution of slavery.

Darwin was onboard the ship as a gentleman naturalist, but the voyage was far from easy. Planned as a 2-year expedition, it was almost 5 years before 27-year-old Darwin returned to England in 1836. He was frequently seasick and, back home, often ill. Nevertheless, his observations, specimens, and notes laid the groundwork for his thinking that culminated with On the Origin of Species in 1859. That book presented Darwin’s evidence for descent with modification (what we now call evolution), and it put forward a mechanism—natural selection—that explains how species acquire traits that fit them to their environments.

Many of us first encounter the idea of evolution as children, when we see pictures or fossils of dinosaurs and other long-ago creatures. But evolution isn’t confined to the past; it continues to occur all around us. Some ongoing evolution causes problems for our health and wellbeing, such as pathogenic microbes evolving resistance to antibiotics. In many cases, though, evolution is used to solve problems in agriculture, biotechnology, and engineering. For example, Frances Arnold won a 2018 Nobel Prize in Chemistry for her work using evolution to generate valuable enzymes with improved and even new functions.

In my lab, we study evolution in action using bacteria, taking advantage of their rapid generations. We can freeze and later revive living cells, allowing us to compare organisms from different generations—in essence, time travel! In an ongoing experiment I started in 1988, we’ve watched 12 populations of E. coli evolve for over 70,000 generations. We can quantify the Darwinian process of adaptation by natural selection, and we’ve sequenced the bacteria’s genomes to understand the coupling between adaptation and genotypic evolution. We’ve even seen the emergence of a new metabolic function that transcends the usual definition of E. coli as a species.

It’s amazing just how much evolution has taken place during a few decades in these small flasks. It leaves me with awe at what evolution has achieved over the last four billion years on our planet … and with wonder about what more will unfold in the fullness of time.

LTEE flasks repeating

This post was written for the National Academy of Sciences Facebook page, where it also appears.

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When We’re Sixty Four (Thousand)

From the E. coli in the LTEE to the People of the Lab

[To be sung along to this Beatles classic]


When we get older, losing our fimbriae,

Many years from now,

Will you still be sending us our thiamine,

Birthday greetings, Erlenmeyer wine?

If we were mutants, crazy and fit,

Would that make you snore?

Will you still feed us, will you still freeze us,

When we’re sixty-four?


You’ll be older too,

And if you say the word,

We’ll evolve with you.


We could be handy, helping your pubs,

When your grants are gone.

You can write a paper by the fireside,

Weekend days give no time to hide.

Colonies growing, dotting the plates,

Who could ask for more?

Will you still feed us, will you still freeze us,

When we’re sixty-four?


Every summer you can buy a freezer when the space gets tight,

If it’s not too dear.

Save our clonal mix,

Plus and minus progeny,

Ara One to Six.


Keeping the notebook, pipetting each drop,

Track trajectories.

Indicate precisely what you think will change.

Hypothesize, test, unlimited range.

Give us your data, sequence and store,

Evolving evermore.

Will you still feed us, will you still freeze us,

When we’re sixty-four?



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Thirty Years

No, the LTEE did not suddenly jump forward by almost 3 years. That milestone will be reached on February 24, 2018.

Next Friday is the end of the semester at MSU and, for me, it will mark 30 years that I’ve been on the faculty: six at UC-Irvine, and 24 here at MSU. (I also taught for one semester at Dartmouth as a sabbatical replacement, while I was doing a postdoc at UMass.)

Holy cow: 30 years. Where did all that time go?

Well, a lot of it was spent advising, supervising, and mentoring graduate students. And those have been some of the most interesting, enjoyable, and rewarding professional experiences that I can imagine.

In fact, this afternoon Caroline Turner defended her dissertation – congratulations Dr. Turner! Her dissertation is titled “Experimental evolution and ecological consequences: new niches and changing stoichiometry.” It contains four fascinating and meaty chapters, two on the interplay between evolutionary and ecological processes in the LTEE population that evolved the ability to grow on citrate, and two on evolved changes in the elemental stoichiometry of bacterial cells over experimental time scales.

Caroline is the 20th student to complete her Ph.D. with me serving as the advisor or co-advisor. Here they all are, with links to their professional pages or related sites.

  1. Felisa Smith, Ph.D. in 1991 from UC-Irvine.
  2. John Mittler, Ph.D. in 1992 from UC-Irvine.
  3. Mike Travisano, Ph.D. in 1993 from MSU.
  4. Paul Turner, Ph.D. in 1995 from MSU.
  5. Greg Velicer, Ph.D. in 1997 from MSU.
  6. Brendan Bohannan, Ph.D. in 1997 from MSU.
  7. Phil Gerrish, Ph.D. in 1998 from MSU.
  8. Farida Vasi, Ph.D. in 2000 from MSU.
  9. Vaughn Cooper, Ph.D. in 2000 from MSU.
  10. Danny Rozen, Ph.D. in 2000 from MSU.
  11. Kristina Hillesland, Ph.D. in 2004 from MSU.
  12. Elizabeth Ostrowski, Ph.D. in 2005 from MSU.
  13. Bob Woods, Ph.D. in 2005 from MSU.
  14. Dule Misevic, Ph.D. in 2006 from MSU.
  15. Gabe Yedid, Ph.D. in 2007 from MSU.
  16. Sean Sleight, Ph.D. in 2007 from MSU.
  17. Zack Blount, Ph.D. in 2011 from MSU.
  18. Justin Meyer, Ph.D. in 2012 from MSU.
  19. Luis Zaman, Ph.D. in 2014 from MSU. (Charles Ofria was the primary advisor.)
  20. Caroline Turner, Ph.D. in 2015 from MSU.

There are also 8 doctoral students at various stages currently in my group at MSU including Brian Wade (Ph.D. candidate), Mike Wiser (Ph.D. candidate), Rohan Maddamsetti (Ph.D. candidate), Alita Burmeister (Ph.D. candidate), Elizabeth Baird, Jay Bundy, Nkrumah Grant, and Kyle Card.

My own advisor – the late, great Nelson Hairston, Sr. – said that he expected his graduate students to shed sweat and maybe even occasional tears, but not blood. I would imagine the same has been true for my students.

Thirty years, holy cow. Time flies when you’re working hard and having fun!

Added November 4, 2015:  And now #21 in my 31st year, as  Mike Wiser successfully defended his dissertation today!


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Window Dressing

The window to the lab has been updated, courtesy of Zack Blount.

62K window dressing

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Happy 27th birthday to the LTEE!

The title says it all:  today is the 27th birthday of the long-term evolution experiment (LTEE) with E. coli.

Well, the title doesn’t really say everything. I also want to give thanks to the many people—not to mention the trillions of bacteria—who have made it possible for the LTEE to keep on going and giving.

So thank you to all of the students, postdocs, and colleagues with whom I’ve collaborated on this project. There are too many to list here, but you will find their names on the papers that have come from the LTEE. I’ll call out just two, on this occasion, for special thanks. Dom Schneider has been an amazingly talented and generous collaborator for so many years—in fact, our first co-authored paper on the LTEE dates back to 1999. And Neerja Hajela has worked with me for 20 years now, and she is the most organized, dedicated, and all-around wonderful technician and lab manager that one could ever have.

Special thanks, too, to Madeleine Lenski, who has tolerated my long-term affair with the LTEE, and who wisely advised me to keep it going on one or two occasions when I was looking in other directions.

[The image below shows the abstract from the first paper on the LTEE, which appeared in The American Naturalist in 1991. It is reproduced here under the doctrine of fair use.  Some of the conclusions have changed a bit as the LTEE has had more time and we’ve gathered more data—that’s science!]

Abstract 1991 LTEE


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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.


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.


  • 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?


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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