Tag Archives: experimental design

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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Do you teach a biology lab that has been disrupted by the coronavirus outbreak?

The following is a guest post written by my colleague, Rob Pennock.


Do you teach a biology lab that has been disrupted by the coronavirus outbreak?  If so, you may want to consider using the Avida-ED experimental evolution platform as a virtual replacement.

Avida-ED logo

To limit the spread of the coronavirus, many colleages and universities have suspended in-person classes, and instructors have had to scramble to replace them with on-line instruction.  Biology faculty who teach laboratory-based courses find it especially difficult or impossible to do their planned lab exercises.  Avida-ED may provide a valuable substitute for some classes.

Avida-ED is an award-winning educational application developed at Michigan State University for undergraduate biology courses. It is aimed at helping students learn about evolution and the scientific method by allowing them to design and perform actual experiments to test hypotheses about evolutionary mechanisms using evolving digital organisms.  Funded by the NSF, Avida-ED is the educational version of a model system used by researchers to perform evolution experiments–including many that have been published in leading scientific journals (see some examples below).  Avida-ED is not a simulation, but an instantiation of the evolutionary mechanisms and process that allows for real experiments.  Avida-ED produces copious data that can be analyzed within the application or exported for statistical analysis.  Avida-ED has been used in classrooms across the country and around the world for over a decade.

Here are more reasons that Avida-ED may provide a useful, quick replacement for your lab:

  • Avida-ED is free.
  • Avida-ED requires no special registration or configuration.
  • Avida-ED is accessible on-line and runs locally in your web browser.
  • The user-friendly interface requires little technical training to use.
  • It includes ready-to-use exercises to teach a variety of evolutionary concepts.
  • It can also be used for open-ended labs where students design and perform their own experiments.
  • It can be used to teach principles of experimental design and scientific method.

See the Avida-ED web site for:

  • Link to the Avida-ED application launch page.
  • Model exercises (under the Curriculum link).
  • The Avida-ED lab book.
  • Quick start user manual.
  • Background information about digital evolution.
  • Articles about Avida-ED, including effectiveness studies.

The Avida-ED team is working to provide instructional videos for the core exercises from train-the-trainer workshops that we have offered in previous summers, where we teach faculty how to use the software in their own classes.  We can also provide instructor support materials for some exercises offline for certified instructors.  A mirror of the Avida-ED site is available in case the primary site goes down.


Lenski, R. E., C. Ofria, T. C. Collier, and C. Adami.  1999.  Genome complexity, robustness and genetic interactions in digital organisms.  Nature 400: 661-664.

Wilke, C. O., J. Wang, C. Ofria, R. E. Lenski, and C. Adami.  2001.  Evolution of digital organisms at high mutation rates leads to survival of the flattest.  Nature 412: 331-333.

Lenski, R. E., C. Ofria, R. T. Pennock, and C. Adami.  2003.  The evolutionary origin of complex features.  Nature 423: 139-144.

Ofria, C., and C. O. Wilke.  2004.  Avida: A software platform for research in computational evolutionary biology.  Artificial Life 10: 191-229.

Chow, S. S., C. O. Wilke, C. Ofria, R. E. Lenski, and C. Adami.  2004.  Adaptive radiation from resource competition in digital organisms.  Science 305: 84-86.

Ostrowski, E. A., C. Ofria, and R. E. Lenski.  2007.  Ecological specialization and adaptive decay in digital organisms.  American Naturalist 169: E1-E20.

Clune, J., R. T. Pennock, C. Ofria, and R. E. Lenski.  2012.  Ontogeny tends to recapitulate phylogeny in digital organisms.  American Naturalist 180: E54-E63.

Goldsby, H. J., A. Dornhaus, B. Kerr, and C. Ofria.  Task-switching costs promote the evolution of division of labor and shifts in individuality.  Proceedings of the National Academy of Sciences, USA 109: 13686-13691.

Covert, A. W. III, R. E. Lenski, C. O. Wilke, and C. Ofria.  2013.  Experiments on the role of deleterious mutations as stepping stones in adaptive evolution.  Proceedings of the National Academy of Sciences, USA 110: E3171-E3178.

Goldsby, H. J., D. B. Knoester, C. Ofria, and B. Kerr.  2014.  The evolutionary origin of somatic cells under the dirty work hypothesis.  PLoS Biology 12: e1001858.

Fortuna, M. A., L. Zaman, C. Ofria, and A. Wagner.  2017.  The genotype-phenotype map of an evolving digital organism.  PLoS Computational Biology 13: e1005414.

Canino-Koning, R., M. J. Wiser, and C. Ofria.  2019.  Fluctuating environments select for short-term phenotypic variation leading to long-term exploration.  PLoS Computational Biology 15: e1006445.


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