Category Archives: Science

An Engineering Perspective on Accelerating Vaccine Distribution

by Daniel Lenski


Key Points:

  • We can continuously prioritize vaccination of the most at-risk populations, and at the same time immediately offer remaining vaccine doses to lower-priority recipients.
  • We can plan for optimal inter-dose timing for 2-dose vaccines without holding back half the supply of those vaccines.
  • We can build a system to maintain consistent levels of availability and prioritization in all regions of the country for the months and years it will take to produce enough vaccines for near-universal immunization.
  • The field of industrial engineering offers well-studied and proven techniques to accomplish this.

The rapid design, development, and validation of multiple safe and highly effective vaccines for COVID-19, by scientists in the US and around the world, has been a stunning achievement. Our challenge has now pivoted to the task of inoculation against this pandemic disease.  How quickly can we produce and deliver sufficient vaccines to maximize protection of life and health, offering the hope of returning to more normal lives and economies? 

Vaccine manufacturers AztraZeneca, Pfizer, and Moderna expect to produce enough vaccine to inoculate only one-third of the world’s population in 2021,[1] and the US expects to receive enough to inoculate only 50 million Americans by the end of March.[2]  Production will certainly limit the rate at which we can achieve a high level of immunity in our populations, but the slow roll-out of vaccination in the USA underscores how much we must also optimize delivery.

As of January 11, 25.5 million doses have been provided by the US government, but only 9 million doses (35%) have been injected into the arms of willing and available recipients.[3] Vaccine stockpiles continue to grow, indicating clearly that delivery, not manufacture, is currently the bottleneck. Delays have been ascribed to insufficient guidance and funding for states and cities, limited and confusing schedules for vaccination appointments, and confusion about current prioritization schemes.  In an example of the floundering, one week ago New York governor Andrew Cuomo instituted harsh penalties both for providers who vaccinated ineligible recipients and for providers who allowed doses to expire or otherwise go to waste. These requirements of maximally efficient utilization and strict prioritization are both logical and important, but they are inevitably in tension with each other. In the absence of clear and simple guidance for how to resolve this conflict, it is unsurprising that these dual mandates have led to a very low pace of vaccination in New York.

How should a program of mass vaccination operate amid a deadly pandemic? What should its goal be, day in and day out, from individual vaccine providers to cities and states to the country as a whole? The goal, as I see it, is to get vaccine doses into every willing recipient[4] while consistently ensuring that the most vulnerable, at-risk groups have prioritized access, and at the same time dispensing vaccines as fast as they can be produced.  A vial of vaccine sitting in a freezer for days or weeks, awaiting the arrival of a high-priority recipient, does no good for anyone. In contrast, vaccinating any human right away will eliminate a vector for the spread of the disease, and move all of us one step closer to ending the pandemic.

The field of industrial engineering can provide us with crucial techniques and tools to sustain a balance between rapid delivery and prioritization for the months and years ahead.  From 2015-2020, I worked with semiconductor factories in the US and around the world providing advice and software.  Our services addressed problems such as: maximizing throughput by identifying and removing bottlenecks in multi-step processes; making efficient use of scarce resources, including time, labor, and raw materials; prioritizing completion of urgently-needed output; and adhering to constraints in the relative timing of critical steps to ensure quality and reliability.

The problems of vaccine delivery are strikingly similar: for maximum efficacy, the Pfizer vaccine’s two doses should be delivered 21-28 days apart[5]; it’s critical to vaccinate at-risk groups early on[6]; all currently approved vaccines require storage and transportation in expensive deep freezers, must be thawed in multi-dose batches, and expire wastefully if not dispensed quickly.

The tradeoff between prioritization and maximally efficient use of time and materials, illustrated by Governor Cuomo’s orders, is a glaringly obvious one to industrial engineers. If a strict sequential order is followed, the next recipient may not be available in time to use the next vaccine dose (leading to expiration and waste), while giving the vaccine on a purely first-come-first-served basis will maximize utilization but hinder rapid access for the highest-priority recipients. It is clear, however, that some members of high-priority vulnerable groups are either unable or hesitant to receive COVID-19 vaccination right now, while some members of low-priority populations are willing and eager to receive it immediately, but cannot due to lack of both eligibility and information about availability. Industrial engineering offers simple mechanisms to achieve an efficient and dynamic balance between these competing demands, such as by creating multiple priority queues at each vaccination provider and switching from higher- to lower-priority recipients immediately when the former are not present.

Experience from manufacturing can also clarify the problem of delivering second doses with optimal timing. Available quantities of Pfizer and Moderna vaccines in the USA were effectively halved by the initial plan to reserve a second dose of vaccine for each patient as soon as their first dose is administered. Some experts have recently suggested distributing all available 2-dose vaccines as first doses,[7] reasoning that rapidly dispensing single doses will save more lives than a predictable but slow pace of second doses, while virologists warn that the reduced efficacy of single doses could have grave consequences in the longer term, by allowing vaccine-resistant variants of the SARS-CoV-2 virus to evolve and spread. In fact, neither reserving second doses nor abandoning their correct timing is necessary. Because future delivery of vaccine supplies to the USA is relatively predictable (at least in terms of the lower bound), an optimal steady-state solution is for providers to limit the rate at which they dispense first doses to half the rate at which they expect to receive future doses, which will leave them with sufficient supplies to consistently vaccinate patients returning for their second doses during the optimal time window.[8]

Beyond the failure to balance between rapidly dispensing available vaccines and prioritizing them, along with a sub-optimal approach to reserving second doses, vaccine distribution in the USA appears gummed up by a pernicious combination of insufficient information about when and where COVID-19 vaccines are available, and complex paperwork and administrative requirements.[9]

If the incoming Biden administration were to ask me to design a plan for rapid distribution of COVID-19 vaccine, my proposal would include the following elements:

  • A national database to track vaccine inventory and rates of dispensation at the level of each provider, in near real-time. This will be crucial for determining the appropriate rates at which to resupply providers with more vaccine doses, so as to sustain and maintain inventory of vaccines across the country without developing geographical and temporal imbalances in inventory.
  • First-come-first-served vaccine dispensation at the level of individual providers, with the crucial addition of multiple queues for patient intake, so that the most vulnerable can always receive the vaccine before others, no matter when they decide to get it.
  • Training for all vaccination providers to implement the queuing system uniformly and consistently, along with minimal and consistent administrative requirements.
  • A website to track wait times for each queue, at each provider, in near real-time. The availability of wait times at nearby locations will likely be crucial to motivate a continuous high rate of vaccine delivery, by allowing many Americans to seek out the vaccine on short notice when wait times are short for their eligibility cohorts.

Ending the COVID-19 pandemic through mass vaccination will present an extraordinary range of challenges for physicians, public health officials, scientists, politicians, and society at large. The tools of industrial engineering certainly cannot help with many of these challenges; however, they can help us achieve and sustain one crucial goal at all scales: getting vaccine doses into every available, willing human being as fast as they can be produced, while continuously ensuring that the most vulnerable people have the most rapid and streamlined access to the vaccine. I know that President-Elect Joe Biden’s COVID-19 task force will include epidemiologists, physicians, and virologists.[10] I would encourage him also to appoint experts in industrial engineering and operations research, who can provide strategic guidance and tactical advice to speed up and smooth out nationwide vaccine distribution.


Appendix: A Specific Proposal

If the incoming Biden administration were to ask me to design a national vaccination program with the above goal of dispensing vaccines as rapidly as they are manufactured, while also continuously guaranteeing preferential access to prioritized populations, here’s what I’d propose. To simplify, I’ll assume that our present vaccine distribution bottlenecks are indeed overwhelmingly a “last mile” problem,[11] and that there are no major logistical impediments to reliably delivering vaccine supplies to providers anywhere in the country within timescales of 1-2 weeks.

First, establish a national database of vaccine-dispensing providers, and a mechanism to log daily inventory for each provider. Apportion newly-manufactured vaccine among the states and territories, and from there down to the level of individual providers. The first round of apportionment will take some guesswork; in the interests of speed and simplicity, my strong inclination would be to apportion the first round simply by population. Subsequent rounds should be adjusted up and down based on past demand and current inventory, in order to prevent geographical and temporal imbalances in inventory.

Second, each provider should dispense vaccines on a first-come-first-served basis, but with multiple priority queues with extremely simple selection criteria. Age is the simplest and most easily documented criterion, and so I have used only that below. Other criteria, such as health-risk factors, occupation, and race or ethnicity have been proposed. However, more complex prioritization runs the risk of slowing down the process for everyone[12], by turning “eligibility determination” into the rate-limiting step. Something like the following:

  • Monday-Wednesday: 6 queues. One for recipients over 80 years age, one for 70+, one for 60+, one for 50+, one for 40+, and one for everyone else.
  • Thursday: 5 queues. 80+, 70+, 60+, 50+, everyone else.
  • Friday: 4 queues. 80+, 70+, 60+, everyone else.
  • Saturday: 3 queues. 80+, 70+, everyone else
  • Sunday: 2 queues. 80+, everyone else.

These queues should literally be lines that people who want the vaccine wait in, clearly marked according to the age criteria. During operating hours, patients should be free to join the appropriate queue at any time. Providers should accept and vaccinate all available patients from higher-priority queues before accepting any from lower-priority queues, but should immediately switch over to lower-priority queues if a higher-priority queue is empty. Example: it’s Tuesday, and there are 10 people in the 80+ queue, 30 in the 70+ queue, and 100 in the everyone-else queue. Providers should vaccinate all of the 80+ patients, then immediately start vaccinating all of the 70+ patients, then immediately start vaccinating “everyone else.” If two more 80+ patients arrive after that initial queue has emptied, they would be accepted and vaccinated immediately. Available vaccine doses should be logged daily to the national database. Acceptance of patients from each queue should be logged in real-time so that it’s possible to publish intake rates in real-time for each and every provider.[13]

This scheme is intended to achieve the following results:

  1. No matter when a higher-priority person decides to get vaccinated, they’ll be able to get it with less waiting than all lower-priority individuals.
  2. Lower-priority individuals will not have to wait to receive the vaccine unless higher-priority recipients are waiting for it right now.
  3. Wait times will be relatively measurable and predictable, encouraging people to drop in and get vaccinated when lines are short, and stay home when lines are long for their priority groups.
  4. Vaccine will be dispensed continuously during the operating hours of each provider, ensuring minimal wasted or expiring doses. (Round-the-clock operation should be able to eliminate this entirely.)
  5. This weekly cycle is intended to prevent overcrowding of lower-priority patients if there’s sustained high demand from higher-priority groups. For example, given the above prioritization scheme, few 35 year-olds will want to line up on Tuesdays. However, those who do will probably have very good reasons to endure a long wait for the possibility of vaccination, e.g. an immune-compromised family member. By later in the weekly cycle, the wait times and intake rates for the younger age groups should be more predictable based on previous days.

[1]    https://www.nature.com/articles/d41586-020-03370-6

[2]    https://www.nytimes.com/live/2020/12/15/world/covid-19-coronavirus

[3]    https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html

[4]    Appropriately spaced when 2 doses are required, and excepting those with contraindications.

[5]    https://www.biopharma-reporter.com/Article/2021/01/07/WHO-weighs-in-on-COVID-19-vaccine-second-dose-delay

[6]    Modeling from Israel indicates that vaccinating the most vulnerable 7.5% of the population would reduce overall death rates by 75%. https://twitter.com/dwallacewells/status/1340397154683269123

[7]    https://www.washingtonpost.com/opinions/2021/01/03/its-time-consider-delaying-second-dose-coronavirus-vaccine/

[8]    This problem gets more complex when the rate of future availability is unpredictable, or when there’s a large build-up of current inventory.

[9]    https://www.newsweek.com/senior-citizens-wanting-covid-vaccine-face-51-step-online-registration-process-1560622

[10] https://www.forbes.com/sites/judystone/2020/11/09/president-elect-biden-names-new-covid-19-task-force–whats-the-enthusiasm-about/?sh=723ade8a458f

[11]   https://www.reuters.com/article/health-coronavirus-vaccine-challenges-tr/analysis-covid-19-vaccines-raise-hope-but-the-last-mile-challenge-looms-idUSKBN28P124

[12] https://www.wsj.com/articles/vaccination-by-age-is-the-way-to-go-11610476439?mod=hp_opin_pos_3

[13]  Let’s say it’s Monday at 10 am. I should be able to pull up a page for the pharmacy at the corner of 10th & Elm street, and see that in the last hour:
80+ queue: 12 patients accepted, est. 2 currently in line (→ ~10 minute wait time)
everyone-else queue: 24 patients accepted, est. 20 currently in line (→ ~50 minute wait time)


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They’re back!

They’re back! After a six-month interruption, Devin Lake restarted the long-term evolution experiment and the 12 LTEE lines from the 73,000-generation freezer samples. Now the bacteria are back in their home-sweet-homes: Erlenmeyer flasks with DM25 medium and the shaking incubator set to 37C.

We’re keeping the lab at very low occupancy, and using masks and physical distancing when more than one person is present in a room) until this damn SARS-CoV-2 pandemic is under control.

MSU also has a spit-based surveillence program in place for those entering campus buildings. Each sample is split, and then put into two pools for PCR testing. With each individual’s sample split into two pools, the testing can identify which individual in any reaction that proves to be positive is the source of the virus. That person is then notified and told to isolate and get a definitive diagnostic test.

[Both photos below courtesy of Devin Lake]

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How to Write a Response to Reviewers in Ten Easy Steps

This post is intended for early-career scientists who have just received the reviews for one of their first journal submissions.  It’s probably most relevant for papers that received generally positive reviews, and which require only minor or moderate revisions.*

  1. Copy all of the reviewers’ comments into a new document.  If the editor has substantive comments—especially ones that guide you to which reviewer comments are most important—then copy those, too.
  2. Write a short note of thanks to the reviewers and the editor at the top, above all of their comments.  See, you’re already making progress!
  3. Use a different font (bold or color) for your responses, to make them easy for the editor (and reviewers, if the paper is returned to them for re-review) to find.
  4. Draft a quick response to each comment.  Some responses will be easy, while others will take more time.  I suggest tackling the easy ones first, because it helps you see that you can make good progress even when there are several pages of single-spaced comments (as there often are).  You can polish your responses or change them later, as needed.  Each quick response is a way of recording your initial reaction and the difficulty (small or large) of addressing a specific comment or suggestion.  However, do NOT start editing the actual paper. That should wait until you have got a complete draft of your response letter, which will serve as a “road map” for revising the paper.
  5. Reviewers often begin by providing a synopsis of your paper. Thank the reviewers (again, after the synopsis) for their summary and kind remarks, when appropriate. There’s no need to write anything more after the synopsis, unless there’s a substantive misunderstanding of an important point that comes up again in a later comment.  In that case, you might say something like “Thank you for this summary of my/our paper. However, you may have misunderstood one point, about such and such, that I/we address in response to your comment below.”
  6. Try to view every comment as constructive. Reviewers may well be mistaken on some points, but rarely (in my own experience) do they say things that are clearly inappropriate. (However, it does occasionally happen.)  That means that you should accept their suggestions, if they improve the paper.  At the same time, you can push back (gently) against a suggestion that you don’t accept, because either (a) it’s not feasible and/or beyond the scope of your study to address it, or (b) you have a different opinion on the issue at hand.  In any case, your response should explain why you don’t accept a particular suggestion.  If (a), then that probably only needs to be said in the response, rather than requiring a change to your paper. Or perhaps you could add something to the paper about the value of future work to examine that issue. If (b), use your response to explain why you disagree and add something like this: “In the revised text on page NN, we have clarified our reasoning on this point.”  Of course, that means you really do need to clarify the issue in the text — but not yet (see point 4 above).
  7. Pay special attention to comments where two (or more) reviewers comment on the same issue.  If they both agree (for example, they tell you to clarify or delete some passage), then that is almost certainly something you should do. You should cross-reference the reviewers’ comments when appropriate. You can simplify your responses by saying, for example, “Reviewer #2 made a similar point …” and “We addressed this point in our response to the related comment from Reviewer #1.” If two reviewers made opposing suggestions, then you should also state that fact. Explain how and why you’ve followed one recommendation or the other; if possible, find a way to strike an appropriate balance. For example: “While Reviewer #1 thought we should delete this passage entirely, Reviewer #2 suggested we emphasize the point and provide more context from the literature. After considering both possibilities, we made the following changes: First, we added a sentence with historical context and several references. We then clarify that the relevance of this earlier work to our own study is speculative, and that it is therefore an issue worth exploring in future work.”
  8. Once you’ve got a draft response, share it with your co-authors (if any) to see whether they are on board with your resonses or have other suggestions.  Once everyone is in agreement, then use your response letter as a “road map” to edit your paper.  Track your edits, so your co-authors (and the editor and reviewers, if requested in the editor’s instructions) can focus their attention on the relevant sections.
  9. You’ll probably find that some text is trickier to edit than you thought in your draft response.  For example, new sentences to address a reviewer’s concern might disrupt the existing flow.  That’s the scholarly life: careful working and reworking are needed at every stage. Also, be cognizant that changes in one section might require (or at least suggest) changes elsewhere to maintain consistency.  For example, if your paper’s Introduction says there are two scenarios (or models or hypotheses or possible outcomes), and a reviewer then suggests adding a third scenario to the Discussion, then maybe you need to edit the Introduction as well. Or perhaps you decide to leave the Introduction as it is, but then clarify in the Discussion that, while you presented two alternatives in the Introduction, your new results (or whatever) raise a third possibility. The point is that changes to one part of a paper may require additional changes elsewhere. Here’s one obvious, and simple, case: If you delete a passage that cites references, you may need to remove them from the Literature Cited (unless you cite them elsewhere) and/or renumber other references, depending on the journal format.
  10. Once you and coauthors are satisfied with the revised text, then go back and edit your responses to reviewers. Clarify the changes that you actually made (as opposed to those you initially imagined you would make). When appropriate, add page numbers (from the revised paper) so the reviewer and/or editor can see how you rewrote important passages.
  11. Bonus advice! Check out the journal’s instructions for authors.  Make sure all of your paper’s formatting, references, figures, and such conform to those instructions before submitting the revised version.  This will save you from having to submit a re-revised version.

There, you’re done!  Congratulations and good luck!!

*If your paper was rejected, I’m sorry for that.  If it’s any comfort, it happens to everyone.  And yes, it’s often upsetting, even to senior scientists, though many of us have gotten pretty used to it.  Take some time to “blow off steam” by going for a long walk or whatever works for you. Then step away from the paper and reviews for a few days. When you return, you might wonder whether it’s worth appealing the decision to the journal. My general advice would be that it’s rarely worth appealing. I’ve tried it only once or twice, without success. I realize now there are so many fine journals, and so many ways to share papers (preprints, Twitter announcements, etc.), that it’s better to move on and try somewhere else. Of course, you may well want to revise your paper based on the reviews that led to its rejection. Even those reviewers who recommend rejection often have useful comments and advice.

 

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Celebrating Black in STEM

I have been very fortunate to know and work with outstanding Black scientists throughout my career.  Here are a few of them.

I met Joe Graves when we were undergraduates at Oberlin College.  We took an evolution course together.  I remember discussing with Joe our mutual fascination with evolution and wondering how we might go about studying it.  I met up again with Joe at UC-Irvine, where we were both conducting evolution experiments—Joe using fruit flies, and me with bacteria.  Joe and I reconnected once more when he and I became founding members of the BEACON Center for the Study of Evolution in Action.  Joe now studies bacterial evolution, and we are becoming scientific collaborators as well.

JoAnn White was an ecologist at UNC, studying the life-history and population dynamics of periodical cicadas.  She served on my doctoral advisory committee and was a highly successful faculty member.  Unfortunately, she left academia, even though she had tenure, because it was too frustrating. I was honored that she asked me to write a reference letter when she moved to a new profession.  But it was a terrible loss for academia to lose such an outstanding scientist and role model as JoAnn White.

Paul Turner was one of my first graduate students.  He joined my lab in the Department of Ecology and Evolution at UC-Irvine, and he moved with me to MSU, receiving his Ph.D. in 1995.  Paul has impressed me in many ways, not only as a superb scientist and mentor, but also in his upbeat outlook on life.  Somehow he manages to smile and laugh about the challenges of being a departmental chair and interim dean, even while running a lab that conducts ground-breaking research.

Lynette Ekunwe was my lab manager and technician for seven years after I moved to MSU. She helped to sustain the long-term evolution experiment with E. coli after its move from UC-Irvine, and she helped run my lab group as it grew in size. Lynette moved to Jackson State University when her husband, the late Steve Ekunwe, took a faculty position there. After the move, Lynette earned a doctorate in public health, and she now works in the field of epidemiology.

I first met Scott Edwards when he was a graduate student at UC-Berkeley. I suspected that he was a rising star, and I was right.  Although Scott and I have not collaborated on actual science, we’ve worked together in other ways.  Scott and I served successive terms as Presidents of the Society for the Study of Evolution, and he has been a valued member of the External Advisory Board for the BEACON Center.

Shenandoah Oden was an undergraduate from Detroit when she joined my lab in the 1990s.  She worked with postdoc Santiago Elena on measuring the fitness effects of random insertion mutations in E. coli, leading to a paper in Genetica. What I remember best about Shenandoah is a question she asked me right after Brendan Bohannan presented his dissertation seminar: “How do scientists come up with the questions they ask?” I told her that was the best question that any student had ever asked me.  It reminded me of how Joe Graves and I, when we were undergrads, wondered how we might study evolution. To Shenandoah, I explained the importance of personal curiosity and mentors in finding questions that are both interesting and answerable.

Marwa AdewaMaia Rowles and Kiyana Weatherspoon were three excellent undergraduate researchers in my lab, all of whom were mentored by Zachary Blount. Maia and Kiyana were coauthors on a paper in the Proceedings of the Royal Society, London B, which reported the results of what we call the “all-hands project”—one in which a generation of lab members performed a set of parallel assays to measure the subtle changes in fitness in late generations of the long-term evolution experiment with E. coli. Marwa now works in the field of veterinary medical research, while Maia and Kiyana are pursuing careers with a biomedical focus.

Judi Brown Clarke was, until very recently, the Diversity Director for our BEACON Center. In that role she generated and managed many successful programs that introduced hundreds of students at all levels to evolution and provided them with opportunities to engage in scientific research. She also was a great listener and valuable source of advice for many of us when we faced personal challenges and setbacks. An Olympic medalist, Judi recently became the Chief Diversity Officer at Stony Brook University.

I met Jay Bundy in 2013, at the Evolution meeting in Snowbird, Utah.  Who was this student who was asking so many thoughtful, insightful questions of the speakers?  I ran into Jay as we rushed between talks, introduced myself, and learned that he was a masters student at Penn State.  He wasn’t sure if he was interested in microbes, but I encouraged him to think about joining BEACON.  Jay came to MSU, first as a BEACON staff member contributing to education and outreach activities, and then as a graduate student in the Department of Integrative Biology.  He also contributed to the all-hands project.  However, he switched from studying bacteria to digital organisms, and he’s now performing and analyzing experiments to quantify how the duration of history in an evolving lineage’s previous environment influences its subsequent evolution in a new environment. Stay tuned for Jay’s findings—he’s working on a huge paper. Jay is as deeply thoughtful about science and life as I imagined when I first heard his questions at the Evolution meeting.

I also met Nkrumah Grant in 2013, when he visited MSU while exploring possible graduate programs.  He immediately impressed me with his personal story of overcoming obstacles.  Nkrumah explained to me his love of science as a child, and how he had gotten discouraged and derailed before undertaking a concerted effort to pursue his dream of science and scholarship.  And pursue it he did … and continues to do.  From a G.E.D. to a Ph.D.  Co-author on the all-hands project, co-first-author on a paper just published in eLife, and three more papers posted to bioRxiv in the last few weeks.  He also just defended his dissertation, giving a beautiful public seminar followed by an engaging, collegial exam.  Nkrumah has done all this and more while being a dedicated father and working tirelessly to promote equity and inclusion in science.

Last but not least, Ali Abdel Magid and Jalin Jordan are two of the current generation of superb undergraduate researchers in the lab. Ali is working with Nkrumah on the evolution of bacterial cell size, while Jalin works with Kyle Card on the evolution of antibiotic resistance.  Both Ali and Jalin are also working toward future careers in medicine. This summer, they are reading work that integrates evolution and medicine including the landmark book, Why We Get Sick, by Nesse and Williams, and the path-breaking paper by Tami Lieberman et al. on the evolution of bacteria in the lungs of CF patients.

My science is better, and my life richer, because of all these people, and many more.  How much better science would be, and how much richer all of our lives would be, if we would open more doors, listen more carefully, and live, learn, and work together.

***

After reading a draft of this essay, Nkrumah Grant, Joe Graves, and Jay Bundy all asked me to say more about this:  How can we achieve the aspirations expressed in my closing sentence above? 

I plan to reflect more on their vital question.  In the meantime, I invite readers who have ideas to put them in the comments below.

EDIT: I should also acknowledge two other influences: A high-school teacher, Mrs. Clayton, who taught a Black History class that I took, and who introduced me to Frederick Douglass, whose autobiography I read with awe and admiration.

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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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Time to restart the LTEE, this virus be damned

The LTEE ran for over 32 years and more than 73,000 generations, without missing a beat. Then this stupid coronavirus came along and made me shut down the lab and stop the experiment. Well, I think it’s high time for everyone to return to the lab and get back to work.

We’ve wasted a hell of a lot of time here.  The LTEE lines were frozen on March 9th.  That’s 23 days ago, for crying out loud.  Do you know how many generations have been lost?  With 100-fold daily dilution and regrowth, that’s ~6.7 generations per day.  So we’ve already lost over 150 generations. And with 12 populations that’s a net loss of more than 1,800 generations.

Another way of looking at it is that each population produces around half a billion new cells each day.  So that’s 23 x 12 x 500,000,000 cells that went missing. You get the picture, that’s a sh*t-load (a technical term for those of us who study E. coli) of baby bacteria that never got born!

I’ve gotten in enough trouble already with a certain crowd for our claim to have observed evolution. If they find out we’ve denied these adorable baby bacteria their existence, there’s no telling what letters they might send me.

Plus, speaking as a scientist, I have this premonition that something really big would have happened during those missing generations. I’ve been expecting them to evolve the ability to produce palladium from citrate. They could then use the palladium for cold fusion, which would surely get some attention. Stupid virus!

Heigh-ho, heigh-ho, it’s back to work I go.  I sure hope you have a nice day at home.

Calendar April

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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.

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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.

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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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