Free Podcast Transcript || ScienceMag: Human pheromones lightly debunked, ignoring cyberattacks, and designer chromosomes
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00:00 Sarah Crespi: Support for this podcast comes from Toyota and their new 2017 Highlander, with its sleek, aggressive design, improved powertrain for better performance and fuel efficiency, plus advanced tech features the whole family will love. There’s more to discover in the new 2017 Highlander. Visit toyota.com for details.
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00:27 SC: Welcome to the Science Podcast for March 10, 2017. I’m Sarah Crespi. In this week’s show, Sarah Richardson joins Alexa Billow to talk about a global project to build yeast chromosomes from scratch, and David Grimm is here with a roundup of stories from our daily news site.
00:51 SC: Now we have David Grimm, editor for our daily news site. He’s here to talk about some recent online stories. First off, we have a story on using photos to predict floods. Traditional flood predictions have been based on monitoring storms and data from electronic sensors, but sensors aren’t everywhere. You know what is everywhere? Cellphones.
01:13 David Grimm: That’s right.
01:14 SC: So Dave, people taking pictures of rivers and streams, what are some examples of this kind of behaviour being useful for monitoring a weather?
01:23 DG: Well, you can imagine somebody takes a picture of rising flood waters and tags it, especially on a photo sharing app like Flickr with “rising flood water”, or “the water’s coming”, or “the water’s rising”, or “we’re about to be flooded” or something like that. And so there’s a wealth of information out there; tons of pictures about water, potential floods tagged with terms that might be useful like “flood”, but also tagged with location data as well so you know exactly where this incident might be developing. And scientists have actually used this in the past with Hurricane Sandy in Flickr photos, where researchers noticed a strong correlation between the change in atmosphere pressure during the storm in images uploaded to Flickr that were marked with tags that were related to the storm.
02:07 SC: This new study is looking to predict rather than look at the past. And again, using Flickr, how did the researchers in this case harness the photo service to catch imminent flooding?
02:19 DG: Well, right, so in this case they wanted to see, “Well, can we use Flickr not just to monitor a storm in progress or a flood in progress, but actually can it be predictive?” And what they did was they turned to neural networks, and this is sort of a computer model that can learn to spot patterns in large sets of data. And they had a really large set of data. They had images and videos that were uploaded in Flickr during a 10-year period that were tagged with general water-related words like river and water, and they were matched to known flood events. They wanted to see can the computer match all these things up and use them to retroactively predict flooding. And they found that it actually could.
02:57 SC: And this is going to be better than the current methods like paying attention to the weather and monitoring with electronic sensors?
03:05 DG: Not quite. It’s better in the sense that it’s definitely not more accurate and it doesn’t provide a lot of really critical information like how high the water’s rising, how much rain has fallen, but what it does provide the current methods don’t is this really hyperlocal approach because you can have sensors and you can have satellites, but they’re not necessarily going to be able to get into the nitty gritty of a specific, maybe street in a specific community that may not have sensors. And therefore, if you got a lot of people around with cellphones, you may be able to get data in some of these places where we don’t otherwise have data.
03:38 SC: Okay, my one concern with this. Well, I have a few, but one is people could then trick the system and just post a bunch of pictures and tag them as “flooding”, and then the system would think that there was a flood when there wasn’t.
03:51 DG: But why would people do that?
03:54 SC: Okay, that does take us to our next story, which is also about hacking. For this one, I can’t keep up with all of the hacking that is going on and the data releases these days. Did you know that millions of US Federal employees had their personnel files hacked? Did you know who did it?
04:13 DG: I didn’t.
04:13 SC: I didn’t know that. It turns out that calling out the perpetrators of these types of cyber attacks, by the way it was China, it turns out that calling out the perpetrators of these cyber attacks may not always be the best political move. And to test this out, researchers used game theory. Can you walk us through how they would set this up Dave?
04:35 DG: Well, yeah, they were basically trying to figure out can we run these simulations where not only do we know that A is attacking B, but that A and B maybe have different vulnerability. So it may make sense in some cases for B to blame A for the attack, for B not to blame A for the attack. And what game theory does, is it allows this modeling of these types of competition among people, organizations, or governments, in this case it’s probably governments, to figure out which strategy makes the most sense.
05:05 SC: What’s new here is that they added in not just two adversaries trying to steal data and then conceal that they did that, but the political strengths and weaknesses of the players. And with that information incorporated, what did they figure out about reporting on cyber attacks?
05:22 DG: Well, they found out that in cases where at least the model suggest that in cases where the attacker’s domestic and international political standing won’t take much of a hit from being blamed, it actually makes sense for the victim to pretend the attack didn’t happen or at least not to cast blame. Because in some cases it can actually make the victim look weak, it can make the victim look like maybe they don’t have good defenses. Also, they don’t know who to blame, and they’re saying well maybe it was this, maybe it was that. It also makes the victim look weak because it suggests the victim doesn’t have the capability to know who attacked them. Also by blaming someone and by admitting you got attacked, it reveals something about your intelligence capabilities, your defensive capabilities. And do you really wanna give that away if blaming an attacker is really not gonna have much of an impact?
06:11 SC: What are some real-world examples of where maybe no one wanted to blame anyone else in a case of a cyber attack?
06:19 DG: Well, there was a case in 2009 and 2010 where Iran was attacked by the so-called Stuxnet computer virus, which attacked its nuclear facility. And the government didn’t blame United States and Israel, who were believed to be behind it or alleged to be behind it, because they recognized… Well, ostensibly they recognized that the two countries would be able to brush off any recriminations and it would just make Iran look weak in the long run.
06:46 SC: What can government officials do with this? We do know the identity of most of these attackers through the press.
06:54 DG: Well, at the very least, this could help policy planners figure out where to put their resources. Does it make more sense to cast blame, to focus and put resources into recriminations? Or does it make more sense to put those resources into, say, just beefing up security instead?
07:10 SC: Last up, we have one of my favorite kinds of stories, a debunking.
07:15 DG: We know you love a good debunking, Sarah.
07:17 SC: The hunt for human pheromones has a long record in science. Lots of dirty T-shirt smelling studies [chuckle] have reported to show the effects of these chemicals on human brains or their behavior. Now, a new study has come out that attempts to shut down speculation about two human pheromone candidates. What are they, Dave?
07:40 DG: Well, I’m not gonna embarrass myself by trying to pronounce the full name but the shortened versions are AND and EST, and these are supposed pheromones. Ever since about the ’90s, they’ve come to prominence for being potential human pheromones. AND is found in male sweat and semen. EST is found in women’s urine.
08:04 SC: When subjects in the study were gonna talk about now got a whiff of these chemicals, what were they asked to do?
08:12 DG: Well, they were asked to stare at a couple things. First, very gender-neutral faces, so the kind of face you would look at and you really couldn’t tell if it was a man or a woman. And the idea is, if you’re given a pheromone, if you’re a guy, a heterosexual guy, you’re gonna think maybe that face resembles more of a woman’s face and vice versa. But they also looked at images of the opposite sex. Again, these were heterosexual subjects, and were asked to rate how attractive they felt they were. And again, the idea would be that if you’re given pheromones, you’re gonna think these faces are more attractive.
08:44 SC: And so, running through all those images and all these smells, what did the researchers find?
08:49 DG: They found no effect.
08:50 SC: Okay, so there we go. But this is a pretty contentious area of research. What are some of the objections to this study and its results?
09:00 DG: Well, one of the experts felt like there weren’t enough trials. He also took issue with this idea that actually part of the methodology of the trial was to take cotton balls that were soaked in these pheromones and tape them to people’s faces. And he was concerned that maybe the tape was a confounding factor, maybe the tape had its own smell. What’s really interesting with these types of studies is even the people behind it don’t say like, “Case closed. There’s no human pheromones.” They’re basically just saying, “These two supposed pheromones probably aren’t pheromones, but we still think there’s pheromones out there, and maybe we just need to do a better job of looking for them.”
09:36 SC: So, I don’t feel like this is a thorough debunking.
09:38 DG: Well, some would say it’s a thorough debunking of AND and EST. But others would say, “No, the study’s not powerful enough to tell us whether even AND and EST are not pheromones.”
09:49 SC: Alright, what else is on the site this week, Dave?
09:52 DG: Well, Sarah, we’ve got a story about what Neanderthal teeth can tell us about their diet. Also, a look back at some of the earliest star formation in the universe. And for Science Et Cetera policy blog we’ve got a story about the impact of US President Trump’s most recent immigration order and what impact that’ll have on science, or might have on science. Also a story about the upcoming March for Science, slated for 22nd April. Who’s participating, who’s not, and who’s still on the fence? So be sure to check out all these stories on the site.
10:27 SC: Okay, thanks Dave.
10:27 DG: Thanks, Sarah.
10:28 SC: David Grimm is the editor for our online daily news site. I’m Sarah Crespi.
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11:54 Alexa Billow: Synthetic genomics is an emerging science. First, it was the genome of a tiny bacterium. Now researchers are building the entire genome of a far more complex species. In Science this week, they present brewer’s yeast 2.0. It’s a leaner, meaner, slim down version of Saccharomyces cerevisiae and it’s built from the bottom up. Sarah Richardson and colleagues spearheaded the design of this new yeast genome. I’m Alexa Billow. Sarah, thanks for being here today.
12:22 Sarah Richardson: Thanks.
12:24 AB: First, can you put this work in context for us? Why would we wanna build a genome from scratch? What was your role?
12:30 SR: Well, there’s an old engineering rationale for this really. If you know how a radio works, you should be able to build radio from scratch. Cells aren’t radios, of course, but there are some hypothesis about genomes that are hard to test without large scale experiments like this. If some of the theories prove correct, we have a chance to breed an organism that is even more receptive to our suggestions than the yeast we started with. That would mean a lot to the bioengineers that are building chemical synthesis platforms, everything from the fermentation of alcohol, like brewing beer, to making vegan milk and spider silk in yeast instead of in cows or in spiders. There’s also the fact that it’s a big proof of principle. It’s a feat of technological and international organization to get this far. It’s in the grandest style of science cooperation. A lot of people worked together for a really long time to make this happen, and what I did myself was to write the computer algorithms that model the genome content and established an interface for that model that allows others to edit the genome so that they don’t have to program in order to interface with the genome the way I do. That piece of the work is called BioStudio.
13:42 AB: And as you touched on this, work is the product of a vast consortium and there’s significant contributions along the way from students, some of whom were undergrads. How did that work? What did they do?
13:52 SR: We had a lot of great undergrads. That was a lot of fun, it’s one of my favorite parts of the whole project is how much education and outreach we were able to do to traditionally, a tier of scientists that don’t get to make big contributions to this kind of work. Current technology doesn’t let us both reliably and inexpensively synthesize DNA in long continuous pieces instead, we make short pieces and stitch them together into bigger pieces. Those slightly bigger pieces are again joined and then quality tested before being passed up to more higher order assembly. So the undergraduates who joined our Build a Genome class for this project typically came in with ownership over a stretch of these genome, and their job was to assemble that piece. So in the process, we got to teach them all kinds of microbiological and molecular biological techniques. They got lectures from all of the collaborating faculty. Some of them even contributed software and algorithms to the underlying code infrastructure. At the end of the semester their output was an assembled piece of DNA. They can point to a piece in the genome and say I built that.
14:57 AB: How big are these pieces? How big are yeast chromosomes?
15:01 SR: The yeast genome has about 12 million faces spread across 16 chromosomes. They range in size from 200,000 bases about to one million bases. So that’s 6,000 genes in 12 million bases. But the human genome is three billion bases, 20,000 genes on 24 chromosomes. The smallest human chromosome is still big enough to hold the entire yeast genome four times over. So the yeast genome’s big but it’s not the biggest thing we know about, and it’s hard to get that much DNA hooked up and running live so we’re still a very, very long way from doing anything like this for a human or even a mouse.
15:45 AB: You did slim the genome down slightly. You shrank the yeast genome by about 8%. You wanted to retain a phenotype that would act and behave more or less normally without getting sick. What is safe to toss in those circumstances? How much stuff can you lose before things start to go wrong?
16:05 SR: We’re hoping to find that out. That if the yeast that is growing to make beer has a certain set of nutritional requirements and a certain set of outputs we’re asking it for. But if you ask a yeast to do something different, the strengths it brings to beer might not be the best drinks to bring to, say milk making. So we would like to see which things are necessary for tasks we’ve known it to use, and which things might be necessary for new task we would like it to take on. So part of that is that there’s no single right answer about what stuff you can lose and how much you can lose before things go sideways because it depends on what conditions the yeast are living in.
16:48 SR: We consider this a platform for testing that, that we can now much more quickly and more effectively check to see how we can engineer yeast to survive in a variety of production environments. One of the things we wanted to test, it’s very difficult to test without doing this way, is to try and dump a lot of the introns, those are intervening sequences in genes, the pieces of genes that don’t code for proteins. We thought that maybe we could move some of those around. We thought we could change some of the coding sequence just a little bit to free up some of the tRNAs. And we thought we could move tRNAs around to remove them from chromosomes and to sideline them to something else. There’s a good reason for that. We took the tRNA genes out because they are hot spots for genome instability. Even in the regular yeast that we make beer and bread with, tRNAs are a spot where the genomes tend to break. What that represents to a bioengineer is some instability where you’ve asked yeast to do something and then randomly it gets broken because a tRNA gene got mutated. So what we’d like to do instead is to sideline that mutation rate so that it happens away from the genes that we’re bioengineering and see if that doesn’t improve the stability of bioengineered strains.
18:15 AB: Does that help you make it smaller? Or does that just make everything a little more stable?
18:19 SR: There are duplications of tyranny, so it does help us make it a little bit smaller, although that’s not the biggest bit of shrinkage. Our hypothesis is that this will help us make bioengineered strains of yeast more stable.
18:31 AB: What does the future hold for this kind of research? What can you do with yeast built from the bottom up that you can’t do with any old strain of yeast? And what other things would you want to be able to make?
18:44 SR: I think one of the coolest things we’ll be able to do with this yeast is to really test the hypotheses about genome structure. We watch genomes evolve and we see the end bit of genome evolution. You know, the genome that we see today is a genome that has been worked on by nature or by humans for many thousands of years, and we have questions about why is it that way? Why is it this way and no other way? And when you get a genome that you’ve built from scratch that you’ve added certain features to that allow you to test certain hypotheses, you can really start to check to see what’s going on with that genome in a way you couldn’t do in other ways. I’m very excited to watch people take it and go test how genomes work in a way that just wasn’t possible before. But I think there’s a lot of really great bio-production and bio manufacturing that we might make slightly easier in yeast going forward if we have even better control over how the yeast works and even better understanding of why it works that way.
19:47 AB: Sarah, thanks so much for talking with us today.
19:49 SR: It was a pleasure. Thank you.
19:51 AB: Sarah Richardson and colleagues publish just one of a package of papers about a synthetic yeast genome this week in Science.
20:03 SC: And that concludes this edition of the Science Podcast. If you have any comments or suggestions for the show, write us at email@example.com, or tweet to us @sciencemagazine. You can subscribe to the show on iTunes, Stitcher, and many other apps, or listen to us on the Science site. The show is a production of Science Magazine. Jeffrey Cook composed the music. I’m Sarah Crespi. On behalf of Science Magazine and its publisher, AAAS, thanks for joining us.