New plans and new HIV stories

I love making plans and I love the beginning of the new year and the new semester. I actually think that the yearly rhythm of semesters and breaks is a huge benefit of being in academia.

Today I spent some time thinking about the writing I plan to do in the coming semester and the talks I will be giving in the near future. The first talk that’s coming up is an invited talk at Stanford. I am very honored to be an invited speaker at the CEHG symposium alongside Anne Stone (ASU) and Graham Coop (UC Davis). I want to try and use the opportunity of the talk to think about what stories I want to tell and I plan to write the story up for publication in addition to talking about it at Stanford.

So what are the stories I want to tell? There are many! But here are some thoughts:

“The evolution of HIV evolution.”

Recently I’ve given seminars in which the overarching storyline was “The evolution of HIV evolution.” I focused on the evolution of drug resistance within patients and explained how drug resistance evolution used to be very fast, but became slower over time. When people were treated with a single drug (AZT) in the late 80s and early 90s, the virus would evolve drug resistance very quickly and the treatment would quickly become useless. Freddy Mercury probably died because of a very fast evolving virus. Over time, treatments improved (using combinations of drugs and using better drugs) such that it became harder for the virus to evolve and nowadays, drug resistance evolution is so slow in patients on treatment, that it is no longer a big worry, and people can stay on the same drugs for many years.

A slide from a talk I gave at SMBE and ESEB in the summer of 2017.

“HIV does it all”

Here is another storyline. Any field in science needs some systems that are looked at in detail. In evolutionary genetics, these systems are fruitflies, yeast, humans, mice etc. HIV is a great system as well in part because we know so much about it and data is abundant. One of the things we have learned in recent years, thanks to work by people like Richard Neher and Kathryna Lythgoe, is that HIV evolution can surprise us again and again. For example, HIV evolution, even in absence of drugs, can be fast within patients and slow at the epidemic level. It can be happening with a lot of recombination, or showing clonal interference (unpublished, Kadie-Ann Williams and PSP), and sweeps can be hard or soft. Within host populations can be panmictic or structured. So if everything can happen, how can we make sense of this all?

“Drugs to prevent HIV”

I like the story of how drugs were and are used to prevent HIV infection. In the 90s and well into the 2000s, drugs were used to prevent mother-to-child-transmission of the virus during child birth. In fact, this was one of the big successes in the world of HIV before treatment was really working to keep infected people alive. Nowadays, drugs are available for HIV-negative people who are at increased risk of HIV infection. Pre-exposure-prophylaxis (Prep, marketed as Truvada) is probably contributing to the shrinking of the HIV epidemic in San Francisco as many of the HIV-negative gay men in the city are taking Prep. When drugs were used to save babies, they were uncontroversial, but when they are used to save gay men, they continue to be controversial and there are many places where Prep is not available (for example, in my home country, The Netherlands).

How is this story linked to evolutionary genetics? When someone is taking drugs to prevent HIV, but they end up getting infected anyways, there may be a high risk of drug resistance evolution (this happened in the babies, in their already infected mothers, and it is happening occasionally in Prep users). Also, at an epidemic level, if a large part of the population is on Prep, this may lead to sub-epidemics of drug-resistant viral strains. There is some interesting modeling work by Sally Blower on these questions.

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OK, that’s enough brainstorming for today. I’ll develop one of these stories into a presentation for CEHG and for an article to be published somewhere. If you have any questions or suggestions, let me know!

 

 

Some recommended and not-recommended science-related books

Last year I read some really cool books that are somehow related to my work. I also read books that were so annoying, I didn’t even finish them. I wanted to share some of my thoughts here.

Jim Ottaviani, Maris Wicks: Primates

Lovely comic book about three women researchers who study primates (Jane Goodall, Dian Fossey, and Biruté Galdikas). Great gift idea! Link to book

Steven Strogatz, The joy of X.

Highly recommended! Great book with essays about fun math. Made me want to learn more. Link to book.

Jennine Capó Crucet: Make Your Home Among Strangers

I very much enjoyed this novel about a young cuban woman who is the first of her family to go to college. It’s an easy read, but it has some insights that may be useful for those of us who teach. Link to book.

Vanessa Woods: Bonobo handshake

Well written memoire by traveler, writer and bonobo researcher Woods, with a lot of background on Congo and neighboring countries. The descriptions of awful violence during the wars in Congo may be upsetting to some. Link to book.

Bill Nye: Undeniable

The topic of this book, evolution, is dear to my heart, but I didn’t manage to finish it. It is simply not well written / edited. Link to book

Frank Ryan: Virolution

This book was definitely worse than Bill Nye’s book! It is not well written and it is full of nonsense about evolution. Disappointing, because it would have been nice to have a good popular book on viruses and evolution. Here Carl Zimmer explains why the book is not recommended: link to book review.

 

 

 

 

15 papers on contemporary evolution in human viruses

In the fall semester of 2014 I taught a reading seminar for master students at SF State on contemporary evolution in human viruses. This blog post contains a list of the papers we read in the seminar.

I posted about this seminar previously here (about the seminar format) and here (no powerpoint allowed), and here (about being nervous for a talk).

The students’ work can be read and seen here (about H1N5), here (polio outbreak), here (Dengue), here (Ebola), here (HIV in court), here (doing my own homework), here (the origin of HIV), here (on bad small things) and here (Hep B).

These are the papers we read:

1. Fast evolution of drug resistance in HIV patient the 1980s

Resumption of HIV antigen production during continuous zidovudine treatment. Lancet. 1988 Feb 20;1(8582):421.
Reiss P, Lange JM, Boucher CA, Danner SA, Goudsmit J.

2. HIV: Doctor infects his ex-girlfriend, phylogenetic evidence in court

Metzker, Michael L., et al. “Molecular evidence of HIV-1 transmission in a criminal case.” Proceedings of the National Academy of Sciences 99.22 (2002): 14292-14297.

3. Very contemporary: the genomics of the West-African Ebola epidemic

Gire, Stephen K., et al. “Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.” Science 345.6202 (2014): 1369-1372.

4. Using phylogenetics to determine origin of Dengue-3 outbreak in Australia

An explosive epidemic of DENV-3 in Cairns, Australia. PLoS One. 2013 Jul 16;8(7):e68137. doi: 10.1371/journal.pone.0068137. Print 2013. Ritchie SA1, Pyke AT, Hall-Mendelin S, Day A, Mores CN, Christofferson RC, Gubler DJ, Bennett SN, van den Hurk AF.

5. Classic paper from Beatrice Hahn’s lab on origin of HIV-1

Gao, Feng, et al. “Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.” Nature 397.6718 (1999): 436-441.

6. Timing the start of the HIV-1 pandemic

Korber, Bette, et al. “Timing the ancestor of the HIV-1 pandemic strains.”Science 288.5472 (2000): 1789-1796.

7. Where did the polio outbreak in Dominican Republic and Haiti come from?

Kew, Olen, et al. “Outbreak of poliomyelitis in Hispaniola associated with circulating type 1 vaccine-derived poliovirus.” Science 296.5566 (2002): 356-359.

8. Within-patient evolution of vaccine-derived polio virus

Martín, Javier, et al. “Evolution of the Sabin strain of type 3 poliovirus in an immunodeficient patient during the entire 637-day period of virus excretion.”Journal of Virology 74.7 (2000): 3001-3010.

 9. Hepatitis B within-patient evolution

Lim, Seng Gee, et al. “Viral quasi-species evolution during hepatitis Be antigen seroconversion.” Gastroenterology 133.3 (2007): 951-958.

10. Permissive mutations and the evolution of drug resistance in Influenza

Bloom JD, Gong LI, Baltimore D. Permissive Secondary Mutations Enable the Evolution of Influenza Oseltamivir Resistance. Science (New York, NY). 2010;328(5983):1272-1275. doi:10.1126/science.1187816.

11. Controversial experiments on H5N1 Influenza

Airborne transmission of influenza A/H5N1 virus between ferrets. Science. 2012 Jun 22;336(6088):1534-41. doi: 10.1126/science.1213362.
Herfst S1, Schrauwen EJ, Linster M, Chutinimitkul S, de Wit E, Munster VJ, Sorrell EM, Bestebroer TM, Burke DF, Smith DJ, Rimmelzwaan GF, Osterhaus AD, Fouchier RA.

12. Influential study on treatment to prevent HIV

Grant, Robert M., et al. “Preexposure chemoprophylaxis for HIV prevention in men who have sex with men.” New England Journal of Medicine 363.27 (2010): 2587-2599.

 13. HIV drug resistance in women in Africa who were treated to prevent mother-to-child transmission

Eshleman, Susan H., et al. “Nevirapine (NVP) resistance in women with HIV-1 subtype C, compared with subtypes A and D, after the administration of single-dose NVP.” Journal of Infectious Diseases 192.1 (2005): 30-36.

 14. Evolution of Acyclovir resistance in Varicalla-Zoster Virus

Morfin, Florence, et al. “Phenotypic and genetic characterization of thymidine kinase from clinical strains of varicella-zoster virus resistant to acyclovir.”Antimicrobial agents and chemotherapy 43.10 (1999): 2412-2416.

 

15. Soft and hard sweeps during evolution of drug resistance in HIV

Loss and recovery of genetic diversity in adapting populations of HIV. PLoS Genet. 2014 Jan;10(1):e1004000. doi: 10.1371/journal.pgen.1004000. Epub 2014 Jan 23.
Pennings PS1, Kryazhimskiy S2, Wakeley J3.

Using deep sequencing data to estimate selection coefficients in HIV

Messer, P. W., & Neher, R. (2011). Estimating the strength of selective sweeps from haplotype diversity data. Genetics.

I recently reread this paper by my colleagues Philipp Messer (used to be my office mate at Stanford) and Richard Neher (who works on the population genetics of HIV, just like I do). I thought it’d be worth writing a short blog post about this paper because it has some really nice ideas but it is quite technical and you may not have read it.

Selective sweeps in HIV

Selective sweeps happen in HIV when the virus fixes immune escape mutations or drug resistance mutations. Often, we don’t have good enough time series data to determine the frequency path of the beneficial mutation (i.e., how fast does the beneficial mutation increase in frequency in the viral population). Without frequency path it is hard to quantify the selection coefficient of the beneficial mutation; how much fitter are they than the virus they replace?

The authors of the paper present a new method to estimate the selection coefficient of a beneficial mutation. The method requires deep sequencing data from a population in which a beneficial mutation has recently gone to fixation. The method is applied to HIV sequences from patients in which a drug resistance mutation or an immune escape mutation has just gone to fixation. It seems to me that the method may be especially useful for drug resistance mutations because they may go to fixation rapidly and at unpredictable times, so that it is hard to follow their frequency path. The proposed method just requires a sample after fixation has happened.

The idea

The method is based on the following idea: If the selection coefficient of a beneficial mutation is very high, then the selected allele will quickly reach a high frequency without accumulating many new mutations. But if the selection coefficient is not so high, then it will take more time for the selected allele to reach a high frequency, during this time it will accumulate new mutations.

New, neutral, mutations that occur on the background of the beneficial mutation, will be dragged to a higher frequency by the beneficial mutation. If a new mutation occurs on the background of the beneficial mutation very early when there is only one copy of the beneficial mutation, then the frequency of the new mutation will always be the same as the frequency of the beneficial mutation. They likely fix in the population together. If, however, the new mutation occurs when there are already 8 copies of the beneficial mutation, then the new mutation will likely reach approximately 12% frequency (like the red fraction of the population in the figure).

This figure shows how earlier mutations on the background of the beneficial mutation reach higher frequencies. (Fig 1 A in the paper)

In a fast sweep, the “5 copy moment” goes by quickly

For a new, neutral, mutation on the background of the beneficial mutation to ultimately reach frequency 20% in the population, it needs to occur when the beneficial mutation is present at approximately 5 copies. The new mutation then occurs on one of the 5 copies, and is thus present on 20% of the viruses with the beneficial mutation. If the beneficial mutation fixes, the new mutation will have a population frequency of around 20%. In a slow sweep, the beneficial mutation may spend several generations at around 5 copies, whereas in a fast sweep, the “5 copy moment” goes by quickly. A mutation that happens when there are 10 copies may reach 10% freq, at 100 copies 1%. If we have many sequences from the population (say, 1000), we can look at all the new mutations and their frequencies and determine how fast the sweep went, or what the frequency path of the beneficial mutation was. If we know the frequency path, we can estimate the selection coefficient of the beneficial mutation.

Richard and Philipp used their method on HIV data because these data are deep enough to do this.

This is a sweep of a drug resistance mutation. The inset shows the genetic distances between the most common haplotypes in the dataset. All haplotypes have just one new mutation, except haplotype 13 which has 2. The main figure shows the ranks of the haplotypes on the x-axis vs their abundance (relative to the haplotype that had no new mutations) on the y-axis. Haplotype 1 (with 1 new mutation) has approximately frequency 0.05, so it must have occurred when there were around 20 copies of the beneficial mutation. The estimated selection coefficient is 0.07. This is figure 6 A in the paper.

Use the method to study new infections?

I wonder whether this method can be used to see how quickly a new HIV infection is growing in a person if we’d have deep sequence data from a newly infected person.

How a collaboration on imperfect drug penetration got started

Almost three years ago, in early 2012, I attended a talk by Martin Nowak. He talked about cancer and one of the things he said was that treatment with multiple drugs at the same time is a good idea because it helps prevent the evolution of drug resistance. Specifically, he explained, when treatment is with multiple drugs, the pathogen (tumor cells in the case of cancer) needs to acquire multiple resistance mutations at the same time in order to escape drug pressure.

As I listened to Martin Nowak’s talk, I was thinking of HIV, not cancer. At that time, I had already spent about two years working on drug resistance in HIV. Treatment of HIV is always with multiple drugs, for the same reason that Martin Nowak highlighted in his talk: it helps prevent the evolution of drug resistance.

However, as I read the HIV drug resistance literature and analyzed sequence data from HIV patients, I found evidence that drug resistance mutations in HIV tend to accumulate one at a time. This is contrary to the generally accepted idea that the pathogen must acquire resistance mutations simultaneously.

There seemed to be a clear mismatch between data and theory. Data show mutations are acquired one at a time, and theory says mutations must be acquired simultaneously. One of the two must be wrong, and it can’t be the data![1]

Interesting!

After Martin Nowak’s talk, I went up to him and told him how I thought data didn’t fit the theory. Martin’s response: “Oh, that is interesting!” (Imagine this being said with an Austrian accent). “Let’s meet and talk about it.”

So, we met. Logically, Alison Hill and Daniel Rosenbloom, then grad students in Martin’s group, were there too. I had already met with Alison and Daniel many times, since they were also working on drug resistance in HIV.  John Wakeley (my advisor at Harvard) came to the meeting too.

Between the five of us, we brainstormed and fairly quickly realized that one solution to the conundrum was to assume that a body’s patient consisted of different compartments and that each drug may not penetrate into each compartment. Maybe we found this solution quickly because Alison and Daniel had already been thinking of the issue of drug penetration in the context of another project. A body compartment that has only one drug instead of two or three would allow a pathogen that has acquired one drug resistance mutation to replicate. If a pathogen with just one mutation has a place to replicate, this makes it possible for the pathogen to acquire resistance mutations one at a time.

We decided to start a collaboration to analyze a formal model to see whether our intuition was correct. Over the following three years, there were some personnel changes and several moves, graduations and new jobs. Stefany Moreno joined the project as a student from the European MEME Master’s program when she spent a semester in Martin’s group. When I moved to Stanford, Dmitri Petrov became involved in the project. Next, Alison and Daniel each got their PhD and started postdocs (Alison at Harvard, Daniel at Columbia), Stefany got her MSc and started a PhD in Groningen, I had a baby and became an assistant professor at SFSU. No one would have been surprised if the project would never have been finished! But we stuck with it and after many hours of work, especially by the first authors Alison and Stefany, and uncountable Google Hangout meetings, we can now confidently say that our initial intuition from that meeting in 2012 was correct. Compartments with imperfect drug penetration indeed allow pathogens to acquire drug resistance one mutation at a time. And, importantly, the evolution of multi-drug resistance can happen fast if mutations can be acquired one at a time, much faster than when simultaneous mutations are needed.

Our manuscript can be found on the BioRxiv (link). It is entitled “Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance.” We hope you find it useful!

[1]Of course, it could be my interpretation of the data!

Stefany Moreno (in large window), Alison Hill, Daniel Rosenbloom and myself in one of the many Google Hangout meetings we had.

Heb B study graphical abstract using paper and pens

One of the most fun things about teaching a grad seminar last semester was reading the homework assignments. Seriously!

Before I move on to the next semester (teaching genetics for undergrads), I wanted to share one more homework assignment. This one by Emily Chang, a graduate student in Scott Roy’s lab. The paper about viral quasispecies in Hep B was one of the harder ones for the students, but this graphical abstract very neatly sums up the main results. I also love that Emily used old fashioned paper and pens to make the abstract, knowing that using fancy drawing software isn’t needed to communicate science.

Graphical abstract by Emily Chang

Students write about a vaccine-derived polio outbreak

Last week we read a paper about an outbreak of vaccine-derived polio virus in Dominican Republic and Haiti in 2000 – 2001. Such outbreaks are uncommon, but they do happen. For me, this paper made clear that no vaccine is 100% safe. As an evolutionary biologist, I find it exciting that it may be possible to study how the attenuated virus evolves to become virulent again.

Here is some of the homework from the students in my class.

Make a graphical abstract of the paper

Cameron Soulette

What kind of data are used in the paper?

Most of the data used in this paper were viral isolates obtained from the stool samples of two patients in the Dominican Republic and Haiti. These were collected (presumably) by the authors because the patients were exhibiting signs of Acute Flaccid Paralysis (AFP). Individuals can have nonpolio AFP, but these two patients were exhibiting characteristics that led the authors to believe their AFP was caused by wild poliovirus, of which very few infections had been observed since the 1980’s.

Nucleic acid probe hybridization identified Vaccine-Derived Polio Virus (VDPV) in these samples. They then performed a sequence characterization of the major capsid surface protein VP1 and compared the isolates from samples to wild-type. The authors looked for more polio cases in the area, and obtained 31 more samples from which they isolated VDPV. They used bioinformatic approaches to analyze their data, including maximum-likelihood and neighbor-joining trees. Using these methods they were able to figure out the timeline for this outbreak of the virus.

Jennifer Gilbert

How much impact did this paper have?

According to Google scholar, this paper has been cited 441 times with the most recent being this year. I found two articles about the paper: one from the Telegraph and the other was a story from Reuters Health (I could not find the original article, but I found it on two separate forums). I think this paper is very influential, but it also has the potential to be used in ways that the authors did not intend. The paper emphasizes the need for continued vaccination and increased surveillance to further the effort of poliomyelitis eradication. However, it seems that this paper has also been picked up as fodder for those in the anti-vaccination movement (one of the forums was hosted by a group called The American Iatrogenic Association – a group focused on raising awareness for illness/ injury caused by physicians).

Links to articles:

http://www.telegraph.co.uk/news/worldnews/centralamericaandthecaribbean/dominicanrepublic/1387873/Vaccine-sparked-polio-outbreak.html

http://medtech.syrene.net/forum/showthread.php?466-Vaccine-Confirmed-as-Source-of-Polio-Outbreak-in-Haiti-Dominican

https://groups.yahoo.com/neo/groups/iatrogenic/conversations/topics/356

Bradley Bowser

Doing my own homework

This week I decided to do some of my own homework. Just for fun.

It’s a graphical abstract of a classic paper we read in class.

Turns out, making a graphical abstract is no easy task! Next week, there’ll be students’ work here again.

What I found most surprising about this paper is that they had to sequence the chimps’ MtDNA to find out what subspecies they were. I would have expected that experts could simply look at a chimp and know what subspecies it is.

Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.
Gao F, Bailes E, Robertson DL, Chen Y, Rodenburg CM, Michael SF, Cummins LB, Arthur LO, Peeters M, Shaw GM, Sharp PM, Hahn BH. Nature. 1999 Feb 4;397(6718):436-41.

Genomics of the Ebola outbreak in Sierra Leone

I am teaching a graduate seminar at SF State on contemporary evolution of human viruses. Colleagues advised me to pick the papers for the entire semester beforehand, to reduce work during the semester. I didn’t do that, however, because I wanted to be flexible and choose (partly) based on what the students liked or what the students had trouble with. The result was that in the second week of class, I could hand out a brand new paper on the 2014 Ebola outbreak. Now that is contemporary!

The only trouble is that from now on, every other paper I choose will seem old; a Dengue outbreak in 2008? How ancient!

Here is some of the homework by the students in my class. I hope you enjoy reading it.

The context and main question of the paper

This paper focused on identifying the transmission route of the Ebola virus disease (EVD) outbreak throughout West Africa, whether the outbreak continues to be supplied by new vectors, and how the virus has changed to infect humans. The scientists used parallel viral sequencing and they ended up generating 99 EBOV genome sequences from 78 confirmed EVD patients. Phylogenetic comparison of all genomes from earlier outbreaks, suggests that the 2014 EBOV likely spread from Middle Africa within 10 years. Patients sharing intrahost variation showed specific transmission patterns in West Africa, and this suggests that transmission of viral genetics may be common.

Something new found in this study was that in contrast to previous EVD outbreaks, human-reservoir exposure is unlikely to have contributed to the growth of this epidemic. In addition, the EBOV catalog of mutations will aid in future studies. One main question that this paper addresses is whether or not future studies can monitor viral changes and adaptation, and understand how to contain this expanding epidemic.

Ryan Marder

The main conclusion of the paper

As this paper was largely descriptive in nature, I am wary to try to define the main scientific conclusion. With regard to concrete discoveries, however, their data suggests quite strongly a single point of origin for the outbreak of Ebola virus disease (EVD) in Sierra Leone, involving two different strains of the virus introduced simultaneously. Additionally, they document with high fidelity possible transmission links between groups of patients.

More important is the demonstration of the utility and information density available through the types of rapid sequencing and analysis employed in this work. Although not a protocol paper, the authors have produced a technical tour de force with a great deal of insight into the disease dynamics involved in the recent Ebola outbreak. I am sure that, as sequencing costs continue their steep decline techniques of this sort will only become more common, and the community will begin to adopt standard practices for these types of studies.

This sort of adoption and standardization will have broad implications for the future of disease mitigation. Tempered by the human genome project’s underwhelming applicability to medical breakthroughs, I remain optimistic that as genetic data is more readily applied to patient treatment, it is likely that information of this kind will contribute to tangible medical interventions which will directly benefit patients around the world.

Graham Larue

The devil’s advocate

The paper mentioned that when the first Sierra Leone case of Ebola virus disease (EVD) was confirmed, the tracing led to 13 more sick females who attended the burial of a traditional healer. It was misleading to seem the females are more prone to contract the disease than the males because the gender ratio of the funeral attendees wasn’t provided.

It was informative but boring to read when a bunch of numbers were given like single nucleotide polymorphisms (SNPs) between the 2014 EBOV genome sequences and the previous EBOV outbreak, and the numbers of intrahost single nucleotide variant (iSNV) in Sierra Leone patients. The wording was a bit confusing sometimes. One ethical issue could be sequencing for other pathogens when the 35 EDV suspected cases turned out negative for EBOV.

Emily Chang

Make a graphical abstract of the paper

Nicolas Cole

Two tweets about the paper

Arturo Altamirano (@articluateartie)

2014 NESCent Evolution Video Contest: the finalists

For the fourth time, NESCent organizes the NESCent Evolution Video Contest. For me, this was a good motivation to make a new video just in time to send it in.

Now twelve video’s were chosen by NESCent to be shown next week at the Evolution meeting in Raleigh. I am happy that my video (number 10) is amongst the finalists! Have a look at the videos and if you’re in Raleigh next week, go and vote for your favorite! (Saturday 21 June, 8-10 p.m., Room 402, popcorn provided!)

All the entries can be found on the NESCent website. Here are the twelve finalists:

1. Sex-y Science: Sex Ratios in Patchy Populations

Allison Neal

Sex-y Science: Sex Ratios in Patchy Populations from Allison Neal on Vimeo.

2. Exaptations versus Adaptations

Renske Onstein

Evolution: Exaptations versus Adaptations from Renske Onstein on Vimeo.

3. Please Tap Again

Ana Endara

Video not available 😦

4. Bird Clines

Osmond, et al (Univ. of British Columbia)

Bird Clines from Luc Luc on Vimeo.

5. Using Fitness Landscapes to Visualize Evolution in Action

Randy Olson and Bjørn Østman

Using fitness landscapes to visualize evolution in action from Bjørn Østman on Vimeo.

6. Selfish Gene

Shankar Meyer, Guillaume Vandenesch, Adrien Bernheim

Selfish Gene from François Maginial on Vimeo.

7. Genetic Drift with Origami Ducks

Flo Débarre

Genetic Drift with Origami Ducks from Flo Débarre on Vimeo.

8. Drift

Will Ryan, et al (Florida State University)

Drift from Julia Kunberger on Vimeo.

9. The Adaptive Radiation of Darwin’s Finches

Andrew Hendry

The Adaptive Radiation of Darwin’s Finches – NSESCent/Evolution 2014 edit from Andrew Hendry on Vimeo.

10. Selective Sweeps in HIV

Pleuni Pennings

Selective sweeps in HIV from Pleuni Pennings on Vimeo.

11. The Genetics of Mouse Burrowing

Ariana Kam

The Genetics of Mouse Burrowing from Ariana Kam on Vimeo.

12. Dinosaur

Lori Henriques and Joel Henriques

Dinosaur from Lori Henriques on Vimeo.