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63,000 Strong

Ever wonder about those big numbers posted in a window in that tall building on the east side of Farm Lane, across from the entrance to the MSU Dairy Store?

Right now, the digits read 63000. That’s the number of generations in an experiment that’s been running in my lab for over a quarter century.

We call it the LTEE, which stands for the Long-Term Evolution Experiment. There are 12 populations of E. coli bacteria in the experiment, and they all started from the same strain.

Every day—weekends and holidays included—a member of my team takes 1% of the cells in each population and puts them in a flask with fresh food. Over the next 24 hours, the population grows 100-fold and then runs out of food. These dilutions and renewals go on day after day, week after week, month after month, year after year, decade after decade. I hope the experiment will continue long after I’m gone, so that someday someone can write “and century after century.”

Bacteria grow by binary fission: 1 cell makes 2 cells, 2 cells make 4, 4 make 8, etc. So the 100-fold growth in the fresh medium represents about 6.6 doublings, or generations, every day. (There’ve been some interruptions since the LTEE began in 1988, but not many.)

Now consider a bacterial cell that gets a mutation in its DNA that lets it acquire more food and grow a little faster. That cell will leave more descendants than its competitors—that’s adaptation by natural selection. Over time, the bacteria are becoming stronger and fitter in their flask-worlds.

By watching the 12 populations evolve, we can answer questions about the dynamics and repeatability of evolution in a group of organisms—bacteria—that are essential for life on Earth as well as important players in health and disease. We measure the growth rates of the bacteria, we sequence their DNA, and we see just how much evolution can achieve even in short order.

Oh, about the sign. Zachary Blount is a talented postdoc who works on this project, and he likes to have fun with science. He put up the window display which, if you look closely, has a picture of Charles Darwin on the left, “The E. coli Long-Term Evolution Experiment” over the number, and “Generations and Counting” to the right. Every 1,000 generations or so, Zack updates the sign.

63K window

[Photo credit: Zachary D. Blount]

Note:   This piece first appeared at eastlansing.org after an invitation from Alice Dreger to explain the numbers in the window to our community.

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Erdös with a non-kosher side of Bacon

Erdös number

Paul Erdös was a prolific and important mathematician. He also had hundreds of collaborators from around the world who coauthored papers with him.

Years ago, Casper Goffman explained an idea, called the Erdös number, that describes the “collaborative distance” between Erdös and someone else, where that distance is defined by the smallest number of steps based on coauthored papers. Erdös himself has an Erdös number of 0, while the 511 mathematicians who wrote papers with Erdös have an Erdös number of 1. One of these people is Persi Diaconis, a professional magician and Stanford mathematician specializing in probability theory.

Over 9,000 people have Erdös numbers of 2, meaning they wrote a paper with one or more of Erdös’s coauthors but never wrote a paper with Erdös himself.  Two of these people are Berkeley professors Bernd Sturmfels, in the field of algebraic geometry, and Lior Pachter, a computational biologist.  (Sturmfels coauthored three papers with Diaconis, and he wrote other papers with two more people with Erdös numbers of 1.  Pachter wrote several papers with mathematician Daniel Kleitman, an Erdös coauthor.)

In 2007, I coauthored a paper with Pachter and Sturmfels in which we analyzed epistatic interactions to describe the geometric structure of a fitness landscape:

Beerenwinkel, N., L. Pachter, B. Sturmfels, S. F. Elena, and R. E. Lenski. 2007. Analysis of epistatic interactions and fitness landscapes using a new geometric approach. BMC Evolutionary Biology 7:60.

So that paper gives me an Erdös number of 3.

Bacon number

A group of students later came up with the idea of a Bacon number, a Hollywood version of the Erdös number that equals the smallest number of film links separating any other actor from Kevin Bacon. (Bacon had been previously described as the “center of the Hollywood universe” after a 1994 interview in which he said he worked with everybody in Hollywood or someone who’s worked with them, according to Wikipedia.)

So Kevin Bacon has a Bacon number of 0, while actors who have appeared in a film with him have Bacon numbers of 1. An actor who appeared in a film with any actors who appeared with Bacon, but not in a film with Bacon himself, have a Bacon number of 2.

Morgan Freeman has a Bacon number of 1 based on a 2013 documentary film called “Eastwood Directs: The Untold Story.” (You missed that one? Me, too.) Well, a couple of weeks ago, I appeared in an episode of the show “Through the Wormhole with Morgan Freeman.”

Erdös-Bacon number

Now there’s a really special number called the Erdös-Bacon number, which is the sum of a person’s Erdös and Bacon numbers. Not many people have an Erdös number, and not many have a Bacon number. And very few people have an Erdös-Bacon number because you have to have written a math or science paper and appeared in a film, and of course with known connections to Erdös and Bacon along both paths.

Cornell mathematics professor Steven Strogatz has an Erdös-Bacon number of just 4, having appeared in a TV documentary film with Kevin Bacon called “Connected: The Power of Six Degrees.” Of course, that film is about the very sort of mathematical links we’re talking about here!

So someone just suggested to me that I now have an Erdös-Bacon number of 5. If so, that would put me ahead of such luminaries as Carl Sagan and Richard Feynman! Awesome!!

The fine print

As I was looking into this exciting possibility, I discovered a website called “The Oracle of Bacon.” It seems to be the semi-official arbiter of Bacon numbers, and it says: “We do not consider links through television shows, made-for-tv movies, writers, producers, directors, etc.”

That documentary about Cliff Eastwood, with both Morgan Freeman and Kevin Bacon in it, apparently doesn’t qualify.  So Morgan Freeman’s Bacon number rises to 2 (via many different paths through his many major films).

Even worse, though, my Bacon number evaporates entirely, since my link to Kevin Bacon goes through my appearance on a television show with Morgan Freeman.

So there you have it. I have an officially non-kosher Erdös-Bacon number of 5.

I guess I can live with that.

But if Kevin Bacon, Morgan Freeman, or any of their Hollywood friends invites me to appear in a real film, I’ll probably accept!

~~~

Note:  It looks like Steven Strogatz’s Erdös-Bacon number of 4 is also compromised because his Bacon number is through a TV movie.  You need to use non-default settings for it to show up on The Oracle of Bacon website.  But maybe it’s less non-kosher, since it was a TV movie, not just a TV show.

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Tiny Things that Live in Little Bottles

As I mentioned in my previous post, it can be a fun challenge to explain your scientific research to people who aren’t scientists.

A week or so ago I came across a website that challenges you to explain something complicated using only the thousand most commonly used words.

So here’s my effort about our long-term evolution experiment with E. coli:

My team works with really tiny things that live in little bottles. We watch the tiny things change over time – over a really long time. The tiny things that do the best have learned to eat their food faster and faster, before the other guys can eat their lunch, so to say.  Well, the tiny things don’t really learn, but it’s kind of like learning – and even better, the best ones pass along what they learned to their kids.  A really cool guy came up with the idea of how this works more than a hundred years ago. My team’s work shows he got it pretty much right. But there’s a lot of stuff he didn’t know, and we’re figuring that out, too.

Several other biologists followed up including Nicole King, Graham Coop, and Josie Chandler (the links are to the simple-words-only descriptions of their own research).

Give it a try, and add your contributions in the comments below!

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A Day in the Life of …

Today was a great day – busy and wonderful. Pretty typical, I’m happy to say, though a bit busier than usual but all of it great.

Woke up to beautiful Spring day in East Lansing and walked 1.7 miles to work at MSU.

Did the usual email stuff.

Worked on getting ready for teaching for a class on evolutionary medicine taught by my colleague Jim Smith. Today’s focus will be the paper by Tami Lieberman et al. on the evolution of Burkholderia dolosa in cystic fibrosis patients during an outbreak in Boston. Last night I re-read the paper for the umpteenth time, and I still enjoyed it. Today I organized a series of questions for the students – a very interactive and smart group – around three parts.

Part I: Some background about CF, the inheritance of this disease, the frequency of the disease, how that frequency allows one to estimate the frequency of carriers, why the allele might be so common (not understood), side questions about sickle-cell anemia and why it’s so prevalent, and why, if it’s inherited, the paper we read is all about infections.

Part II: Preparing slides so we could work our way, figure by figure and panel by panel, through all of the main points in Lieberman et al.  (Reminder: Explain to students how scientific papers are often written around figures.  Once the figures and tables are there, then start on the results, etc.)

Part III: Follow up questions about the paper, the system, the interface of epidemiology and evolutionary biology, prospects for the future of this field and the students’ careers (most in this class are premed, many with a research bent), etc. And whatever questions they might want to ask of me.

Sometime in the middle of doing all that: Chatted with second-year grad student Jay Bundy, who is reading some of Mike Travisano’s terrific earlier papers on the LTEE. Specifically, why do we sometimes express fitness as a ratio of growth rates (measured in head-to-head competitions) and sometimes as a difference in growth rates?

Also in the middle of doing all that: Had phone conversation with former Ph.D. student Bob Woods, now also an M.D. specializing in infectious disease, about a faculty job offer he has (congrats, Bob!), some of the issues he needs to clarify or negotiate, and some of the amazing work he’s now doing on the population dynamics and evolution of nasty infections.

Email from grad student Mike Wiser that our paper, submitted to PLOS ONE, has been officially accepted. We had posted a pre-submission version at bioRxiv – now it’s gone through peer-review and revisions and is accepted for publication. Congrats, Mike!

Got a draft of the fourth and final chapter of Caroline Turner’s dissertation. The first three chapters are in great shape. Congrats, Caroline! With teaching looming, I had only time to review the figures, tables, and legends on this one, and made some small suggestions. On to the text tomorrow … It’s a beautiful body of work on two fascinating aspects of the interplay between ecology and evolution that have emerged in the LTEE and another evolution experiment that Caroline performed. Stay tuned for these papers!

Took a phone call from an MSU colleague who has friend with a child in high school who is interested in microbiology, who is visiting MSU, and who wanted to see the lab. Yikes, I gotta run teach! But postdoc Zack Blount kindly agreed to give a guided tour as I headed off to teach.  Thanks, Zack!

Beautiful day continues as I walk to teach in another building. Touch base with Jim Smith about what I plan to cover.

Two straight hours of teaching (one 5-minute break) in an overly hot room. Almost all of it interactive, with me asking questions and the students conferring in small groups and then responding. Very interactive, very bright students! The two hours were nearly up, with little time for my third, post-paper set of questions. But all of the students stayed (despite the beautiful weather, hot room, and the dinner hour at hand) an extra 15-20 minutes for a couple of my questions and some great ones from them about the LTEE and the future prospects for microbial evolution in relation to medicine.

It’s 6:20 pm: I’m mentally exhausted but equally invigorated. Beautiful Spring day continues as I walk home. I’m greeted by our lovely hound, Cleopatra. Exercise and feed her. Then an even more lovely creature, Madeleine, returns home and I greet her.

Check email before dinner. Find that paper with grad student Rohan Maddamsetti and former postdoc Jeff Barrick has been provisionally accepted, pending minor revisions, at Genetics. We posted a pre-submission version of that paper, too, at bioRxiv. Though we still need to do some revisions, I think it’s fair to offer congrats to Rohan and Jeff, too!

Time to crack open a bottle of wine and have some dinner. Fortunately, some of the pre-packaged dinners are pretty tasty and healthy, too, these days ;>)

Refill wine glass. Sit down and start to write a blog on a day in the life of …

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The LTEE is Ending Today

April Fools!

Onward to 2,500,000,000 generations

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Favorite Examples of Evolution

When the cold bites, When the review stings, When the news is sad, I simply remember these evolving things, And then I don’t feel so bad! — with apologies to Rodgers and Hammerstein

Over on Twitter, the biology students from George Jenkins High School in Lakeland, Florida, asked me and many others: “What’s your favorite example of evolution?”  There are so many fascinating examples that it’s hard for me to pick just one. So, here are half a dozen examples that are among my favorites.

  • The discovery by Neil Shubin and colleagues of Tiktaalik, an extinct fish (pictured below) from the Devonian that was poised to give rise to terrestrial vertebrates. You can read about this work in Shubin’s award-winning book, Your Inner Fish, which was also made into a PBS show.
  • The discovery by Svante Pääbo and colleagues of the Denisovans, an extinct lineage of humans, based on sequencing a complete genome from the finger bone of a girl who lived tens of thousands of years ago.
  • The analysis by Tami Lieberman, Roy Kishony, and colleagues of the genetic adaptation of an opportunistic species of bacteria to the lungs of patients with cystic fibrosis. I’ve blogged about that paper here.
  • Here’s one from the long-term experiment in my own lab — the evolution of the ability to use citrate that arose in just one of the 12 populations and after more than 30,000 generations. There are nice summaries of this work in Carl Zimmer’s blog here and here.
  • A study by Hod Lipson and Jordan Pollack on the evolution of robots. I remember hearing about this paper and being shocked: “Wait a second. Robots are expensive, and most things go extinct during evolution. How could they even afford do this?” I had to read the paper to realize they were evolving virtual robots in a physical simulation of the real world. They then built and tested the winners in the physical world. And indeed, the robots worked as they had evolved to do.
  • Applying the mechanisms of evolution to artificial systems is a fascinating approach useful for both biology and engineering. One of my favorite basic-science uses of this approach was a paper where we used digital organisms – computer programs that self-replicate, mutate, and compete for resources – to show how very complex functions could evolve if simpler functions were favored along the way. These simpler functions provided building blocks for the more complex functions, illustrating how evolution works by tinkering and borrowing already existing structures and functions and using them in new ways. Incidentally, this work involved collaboration between a computer scientist (Charles Ofria), a philosopher (Rob Pennock), a physicist (Chris Adami), and a biologist (me).

Readers: Please feel free to add your own favorite examples of evolution in the comments section below.

[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.] Tiktaalik

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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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