Episode 8: Bryan K Lynn on Math and Microbes (Applying Game Theory to the Evolution of Cooperation in Bacteria)
Image: Lung cells infection by the bacterium Pseudomonas aeruginosa, from Wikimedia Commons.
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Charles: This is Assigned Scientist at Bachelor’s – I’m Charles and I’m an entomologist.
Tessa: And I’m Tessa and I’m an astrobiologist.
Charles: And today we have another guest. Bryan Lynn. Hi Bryan.
Bryan: Hey, how’s it going?
Charles: Pretty good. What is your background in science? How did you get into science? What are your interests in science?
Bryan: Sure. Uh, so my background in science. Well, I study the evolution of cooperation and I got into it starting with studying math. So I was a math undergrad. Um, but once I got to some of the upper level courses, I found, I really enjoyed the intersection of math and biology. And in particular, I really liked game theory and evolutionary applications of math.
And so then I sort of transitioned from pure math to math plus science.
Charles: Was there something in particular that got you interested in the biological applications of math?
Bryan: Yeah, I think there was a part of me that always wondered a little bit, like why do I care about this math? And so when I’m proving that one infinity is larger than another, it was hard to see what are the applications of this.
And like, maybe it’s a little interesting, but didn’t seem tangible. And so when we got to math bio, it felt more like I could see the way these equations… where it’s in the world in front of me. So it started at a community college. Yeah. In Minnesota, Minneapolis Community and Technical College, and then I transferred to the University of Massachusetts, Boston campus.
Charles: So in your math program, did they require you to take an applications of math course, like as higher level courses?
Bryan: I was doing a bachelor’s of science rather than like a bachelor of arts. And so there was a certain number of science classes that were required. And so I tried to do the ones that both qualified as a math elected, and also a science elective. And so that’s how I ended up taking things like a game theory and evolution or intro to math.
Charles: For people who don’t know, could you describe what game theory and evolution really is – like how game theory is relevant to evolution.
Bryan: Yeah. Yeah, absolutely. So I think at its core game theory is really a math model of strategy and decision making.
And so that applies well to behavior. So you see a lot of its applications in economics for that reason, but we apply it to evolutionary biology in a sense that, you know, we tend to think of evolution being driven by physical traits. So you have Darwin’s idea of survival of the fittest and his finches, if you have the right beak, but we can also think of behavior as being a driver, like of behavioral traits as driving evolution.
So I particularly look at cooperation as a, as a trait. Um, and we’ll see how it evolves. We do this through thinking about cooperation as hard to describe evolutionarily. It requires a cost to the individual to cooperate. And so if survival of the fittest is the true driver, we would expect cooperation not to exist, but we see it in all forms of life, from humans to bats, to bacteria. And so the interest is really in why does it exist and how does it exist when it theoretically shouldn’t?
Charles: I would imagine for a lot of people, the concept of bacteria cooperating with each other is unintuitive because we tend to think of bacteria as something that doesn’t experience intentionality at all. Can you describe what cooperation among bacteria would look like?
Bryan: Yeah, definitely. It is a sort of anthropomorphic concept to apply it to us such as well, not a simple organism, but a simpler one. So if we think about like on the human scale, something like sustainable fishing, we call it a public goods game.
Uh, and theoretically, every fishermen would be best off if they fished as many fish out of the ocean as possible. Right. They would make more money that way. But if every fishermen does that, we run out of fish. So there’s this tension between the individual self interest and the collective good.
Uh, bacteria do communicate. It’s not through words, but through signals, um, and having signal receivers. And when enough of them get together and enough of this signal is present, they will do something called quorum sensing. And so the quorum sensing, they start producing a public good. So like the bacteria, I work with, Pseudomonas aeruginosa, produces an enzyme that helps for metabolic digestion.
So it’s sort of like, this knife that will cleave the food into smaller bite size pieces. And so, but we have some strains that won’t produce the enzymes. So we think of those as our non-cooperative ones because they get to benefit from the enzyme that’s produced by others, but they’re not taking on the cost of production.
Tessa: So basically free riders
Bryan: Yes, exactly. Yeah. So we have our cooperating bacteria and our cheating bacteria, but it’s all just comes down to this enzyme production.
Charles: Could you just describe quorum sensing?
Bryan: Yeah, definitely. So, uh, in the genome we have basically two genes. Uh, we’ll just have a little bit. There’s this, um, there’s one gene that would sort of produce this signal. And another one that will receive it when the bacteria is producing the signal, there’s these signal molecules that end up in, do you think of like in the test tube or whatever environment they’re in, um, once enough of those signals are present that they can start receiving them.
So, um, if there’s only a few, the odds of the cell sort of receiving that signal is going to be smaller, but once it’s concentrated enough, um, they’re more likely to receive the signal from another gene, then they’ll start this process of quorum sensing, which basically just turns on a bunch of other genes.
Tessa: It’s basically how you go from a bacteria sort of living solo on its own to say forming a biofilm or sort of a colonial structure.
Bryan: Yes, exactly.
Tessa: My master’s degree did a lot with microbial ecology. So this is slightly in my wheelhouse,
Charles: I guess I have maybe two questions that you can pursue. One is how did you end up specifically doing what you’re doing now and/or what does it actually look like to research game theory and evolution regarding cooperation? Like what does your lab time look like? What is the research set up? Like, what is the process behind that?
Bryan: Sure. Yeah. So for the first question, how I sort of ended up here, I feel like a bunch of chance and luck. Maybe my current advisor, I had stumbled upon a paper of his while doing my undergrad thesis.
And when I was looking at grad schools, I applied here and it’s for mentioned him as being someone I wanted to work with. And that happened to work out here, project for me and my department. You know, funds students through TA, so he didn’t need to have a grant in order to take me on. So I feel like that was a little bit of reading papers and a lot of stuff, timing and luck.
But my day to day using like a, the microbial population is new to me when I got to grad school. So I had to learn a lab stuff. And so I actually studied this using a chemostat, which is basically a giant test tube that ha two hoses in it, a one that puts more media or bacteria food into the test tube, and one that pumps the mixture out at equal rates to the volumes of it. And so this replicates in a way, an actual ecological system, because the natural system will have an input of nutrients and another flow of migration and death. So I use the chemostat to, to show that our cooperating bacteria can exist on its own and that when we get cheaters involved, um, we get what we call a tragedy of the commons, uh, which is basically a population collapse.
Cause these free riders have this growth advantage and eventually there’s too many of them to sustain this population. And so I’ve been pairing this with, uh, like a system, like a math system of equations. We’re trying to show that the results are the same, both theoretically and empirically are not the same, but similar at least.
Tessa: That’s actually really, really cool because Hey, again, having been around people who do game theory. I hear a lot of discussion on it, but I haven’t seen that many examples outside maybe anthropology circles of doing… or economic circles of like doing actual lab experimentation to verify sort of the predictive behavior.
Bryan: Yeah. That’s one of the things that motivated our research questions is a lot of the game theory models that existed, or like the ones that maybe John Nash is famous for, these agent-based models.
So you think of just like individuals randomly bumping into other individuals, but maybe that works with humans, but it’s hard to imagine bacteria, it’s just individual bacteria bumping into other individual bacteria. Like I don’t know that that really captures their behavior. And so we’re, we’re using more of a mechanistic approach and considering their, uh, biological processes. And so that’s, that’s sort of the, the stamp that I’m trying to put on it.
Tessa: What software or framework are you using for the modeling portion of that?
Bryan: Yeah, so the models themselves are like a system of, uh, differential equations are specifically like Jody’s, which if anyone says taken like an OT class, you might have had to do the like solution in a bucket problems, which is kind of what I’m doing.
If the solution isn’t saltwater, but bacteria, and then to get like visual. Images. I, I use Python. I’m a fan of Python, but we’ve also done some, yeah. We’ve also done some like standard math proofs of some of the more theoretical concepts that maybe aren’t completely testable in the chemostat.
Tessa: So I guess, what are you hoping to discover doing this? I mean, aside from seeing if. The behavior of the actual community of bacteria kind of falls somewhat along the line of the predicted behavior based off of game theory, like, is there a specific result you’re hoping to find is our hypothesis you’re trying to prove or disprove, et cetera.
Bryan: So. The, the main focus here is really trying to morph these empirical and theoretical approaches. It would be, I guess, in a perfect world, something that really cool is to stumble upon some sort of mechanism that sustained cooperation. That’s something that folks that study it. Are particularly interested in, um, we hear a lot about like policing as a mechanism for sustaining cooperation. That would be like the IRS to our taxes as a public goods game, or, you know, things like kin selection or concepts folks might be familiar with.
So if I were to run an experiment and you know, the tragedy didn’t occur, but something else happened that would, I would be very excited about that.
Tessa: Basically trying to figure out why out in the wild you don’t see these. Factory populations growing and then crashing due to too many free riders, just like all the time.
Bryan: Yeah, exactly. You know, so the one I work with is a lung pathogen and it seems like certain mutations that would cause free riders to occur are very common, but we don’t see, you know, these bacteria just killing themselves off and people not having it anymore. So it would be neat to sort of get to the crux of that
Tessa: Speaking more speculatively, could there be like a potential down the road, a medical aspect of that, of like, okay, how can we get these bacteria to turn on each other if they’re infected?
Bryan: Yeah, definitely. It would be pretty cool. Especially with bacteria having antibacterial resistance, if we could just have them kill each other instead of dealing with our medicine, that that could be effective.
Charles: Well, speaking of, so obviously when doing sort of modeling and experimentation there is a huge amount of simplification that needs to be done to be able to actually like isolate effects and see what’s actually happening. But is there ever any sense that I guess, is there ever the sense is, is there other common criticism that.
Perhaps the lab work that you’re doing. And I assume people who are doing similar things to you is inevitably to simplified to actually capture the complexities of what might be happening in a real world environment. If that makes sense.
Bryan: Yeah. And I think yes and also, no, I think the modeling and experiments typically start out with the simplest case always.
And then we build up on that, but bacteria, like working with the bacteria themselves, they still evolve and, and do their own thing. And so if I could stop them from evolving, it would make my experience a much simpler and probably easier for wrangling data. But they still just do what they want.
Charles: Well, thinking of evolution within the controlled environment. Are there ever any studies that compare sort of the course of evolution in the controlled environment versus the course of evolution in the real world environment? Like, are there ever – just pulling something from random, like, are there ever genetic studies looking at evolutionary changes over generations within the chemo stat versus like genetic changes over generations from within lungs.
Bryan: That’s a great question. I don’t actually, I’m not aware of anything like that. I wonder if one of the harder parts about that is if you’re studying the pair, the bacteria are gonna evolve much faster than the human. So it would take a long time to get all the lung data.
But I wouldn’t be surprised to find out if someone is collecting that data.
Charles: Well, just curious to see if there are any, I guess what I’m really trying to get at is whether there are, whether there’s evidence that there is sort of a predictable path of evolution common across different environments. If that makes any sense, like if we would expect to see the same changes within the chemo stat, as we would see in the real world environment of for instance, somebody’s lungs.
Bryan: Sure. Yeah. That’s a great question. I don’t know the answer to that either. I, I imagine that as genome sequencing is becoming more and more ubiquitous, that data would probably be available soon. If not now, I don’t know how easy it is to remove this bacteria from lungs.
Like I don’t actually work on humans.
Charles: That’s actually… I didn’t consider that. Cause you can’t just cut open…. Cause this is entomology poisoning in my brain. You can’t just cut a person open.
Bryan: Yeah. They tend to not like that,
Charles: But you can cut a bug open. There are very limited ethical restrictions placed on entomology, which is good in the sense that it makes things really easy for us, but bad in the sense that, I mean, the insects probably don’t like it.
Bryan: Yeah. I have a similar experience working just purely with the bacteria. You know, a lot of my friends and my department are always having to get permission and permits and whatever, and I can just show up and kill a whole population if I need to.
Charles: It’s just the tyrant of the lab. Just killing off populations at whim.
I wonder also, is there ever the question of scalability, like you can see these effects in bacteria, but I imagine an easy cynical perspective to have was, would be bacteria are very small. And behaviorally, they’re pretty simple. So what does bacteria have to do with, you know, bugs or even humans
Bryan: Yeah, and I think you’re getting at one of the sort of main differences here is that humans or some other more intelligent organisms have the ability of choice. So I might choose to cooperate under some circumstances and not others, where with the bacteria it’s genetic. And so they’re either always cooperating or. Never cooperating unless, you know, another mutation occurs that sends them back one way or the other, uh, so that it does create a more simplified model.
But since there’s not a whole lot of work out there that really pairs these experiments with the math starting simple fields, it feels right.
Charles: It sounds a lot like the application of game theory to evolution and cooperation. When applied to quote unquote more complicated organisms, such as humans is edging sort of dangerously close to evo psych territory, and I guess I would just ask, do you have a feelings about that?
Bryan: I always have feelings. Yeah. I mean, I can see how it might sound that way. I guess one of the, the name. Features of like game theory is that it is rooted in it’s like it is a strictly mathematical analysis. So isn’t, it can be a little bit arbitrary in creating and defining path matrices, which is sort of the route it took in the original days.
And so in that sense, you’d say, well, this person A and person B both cooperate, what is like their benefit amount. And I’m like, what is their cost amount? But that’s one of the reasons I’m what I’m doing is getting away from that model a little bit. And instead of going towards this mechanistic approach that says, well, what is their actual like growth rate?
And we create like a growth equation and like, that’s their fitness rather than just saying, Oh, well I think you’d benefit equal to five. And like, let’s study that.
Tessa: So you can actually quantify sort of these. Factors or, you know, penalties or rewards for cooperating or not a bit better than you can with traditional game theory, it sounds like.
Bryan: Yeah. Yeah, exactly. So for example, game theory talks a lot about like a cost benefit ratio. So it’s, what is the cost of this behavior versus what you get out of it? And so I can take a look and like measure the growth of our cooperators on their own and our cheaters on their own and say, well, the benefit of cooperating is exactly like this much more growth, you know.
Tessa: I’ll be interested to see how, you know, being able to quantify these things turns out, especially how it changes over time. I know one of the people who graduated out of my lab, uh, Cole Mathis was a big proponent of what he referred to jokingly as drunk game theory, which was the idea that the rules of whatever game you are constructing can change over time. The titular example being while you know, the cost benefit analysis of.
Buying all your friends around a beer at the bar will seem different to you. If you’re one drinking or if you’re several drinks in and that in turn can change the whole structure of the game and the expected the outcome.
Bryan: Yeah. Drinking at the bar, changing our cost benefit ratios sounds like a fun project.
Tessa: Apparently there was a lot of field research involved.
Charles: Remember bars.
Bryan: Yeah, yeah.
Charles: What are sort of next steps for you? Like what are you. What is the timeline of what you are working on now and sort of, what are you hoping to do?
Bryan: Sure. I mean, um, ideally, I think the timeline is probably a couple, maybe two more years of chemo stat work, but it’s hard to say, um, my last two experiments where I had both like a 10% cheater population in my chemo stat, uh, we got some new mutants that developed.
And so now I’m sort of honestly, side quest of figuring out are these new mutants cheaters, or are they cooperatives? Are they in the middle? Um, it’s, it’s hard to really know what my timeline is because bacteria like to mutate, uh, but future goals, uh, I am sort of on a traditional track, uh, staying in academia and so probably a postdoc of some sort and then hopefully a job teaching and researching somewhere.
Tessa: With the new slate of X-Men movies coming out, do you get to relish saying Newtons in everyday conversation on a regular basis?
Bryan: Yeah, I really enjoy it. I mean, the second X-Men movie was always my favorite. So I like to imagine them as some of the originals.
Charles: The second X-Men movie is the best X-Men movie. So the only good thing about X3: The Last Stand is that they got Kelsey Grammer to be Hank, which was the right decision.
Bryan: Yeah. Good.
Tessa: I have not actually seen X three
Charles: It’s… you’re not missing anything except Kelsey Grammer as Hank McCoy.
Tessa: Good, good to hear. I’m sure someone’s made like a super clip of that on YouTube and I can just see the good parts and ignore everything else.
Bryan: Yeah. That’s the way to go for sure.
Charles: The only thing is that when they have them all suit up, they like, don’t give him a shirt and it’s like, what are we, what are we trying to accomplish here? Cause it’s not like Kelsey grammar covered in blue hair is prime beefcake material.
Bryan: Maybe it depends on the audience.
Charles: You know, you’re not wrong, but if there is anybody for whom Kelsey Grammer dressed as blue hair, Hank McCoy is beefcake material. It’s me. And it didn’t work on me. So I can’t imagine that it would have for anybody else.
Bryan: That’s fair.
Tessa: I think it sounds really, really cool what you’re doing, Bryan, and I hope to see what you come up with and that we can actually having done my share of microbial modeling, although it was mostly focused on nutrient and energy flows, the fact that you’re actually going out there and getting quantified.
Hard numbers for some of these factors or whatever is really nice because there’s a lot of times when I was doing it for my master’s thesis, I’d would be just like, Well, I’m going to use this number because it doesn’t cause my model to crash. So we’re going to assume it’s this number and it would be nice to have like something actually really grounded in reality to pump, you know, pop in there.
Bryan: Yeah. It’s nice for me to be grounded in reality in at least one facet of my life.
Charles: What kind of research do you want to do after you do this research?
Bryan: Yeah, that’s a good question. Um, I. I really enjoy studying cooperation. Um, and I also really enjoyed math. So anything that intersects that is I think, going to be a place that I enjoy, maybe I’ll end up studying a higher organism or maybe I’ll stay in theory. I don’t really have a strong, strong preference.
Charles: If you did end up working on a higher organism, would your methodologies have to change to account for the more complicated ability to make choices? Of basically any organism that can actually intentionally disperse.
Bryan: Yeah, definitely. And I think that could be done modeling wise. It wouldn’t be too challenging to show that I did just have another state variable or like category that they would populations would sort of bounce between the two. Um, so you could like switch from tutor to not cheater. So the real problem is quantifying the behavior.
And so like, Two distinct categories. Are, can you quantify it in the three distinct categories or four, or is it, you know, the more behaviors, the more complicated the system will be. Um, but an animal that’s well studied that has some good historical data. I think it would be pretty reasonable to sort that out.
Charles: I mean, the other thing that is on my mind is that I most commonly hear about the concept of tragedy of the commons when it comes from people who hate communism, who try to use it as a reason, uh, communism is impossible, but as you said, you normally, when you end up having a certain majority of cheaters that leads to this population collapse because everyone is taking advantage of the commons and that, but not contribute to it. So, I mean, what I really want to ask is why will communism succeed? But what I’m actually going to ask is… the question is really, we don’t have a mechanism for how cooperation persists, right?
Bryan: There’s been several that have been suggested. And so what I call it be really neat is finding like a new one. But the idea, like I mentioned, policing earlier, um, which is really just like a form of like punishing cheaters. Um, but you can take think of the flip side and like you can punish bad behavior.
You can reward extra work. Good behavior is also just the idea of changing your behavior based on who you’re interacting with. So you might operate more with. Friends and colleagues need do strangers.
Charles: Are there ever any instances where it looks like the population is leading towards this tragedy of the commons population collapse, but then it ends up bouncing back towards cooperation. Like, are there instances where cheaters or sort of dominating the population, but then a cooperative majority can become reinstated?
Bryan: So I haven’t discovered this with my bacteria yet, but that certainly is something that happened and in, in the world. Uh, so when of my favorite examples might be the Iceland Cod Wars, my favorite war, nobody died, but there was a lot of fishing for cod around the borders of Iceland and.
Boats would come over from the UK or Europe and basically fish there, their region. And so they just started driving their boats, head on into non Icelandic boats that were fishing there to drive them away. So that’d be a form of a form of policing and it ended up getting change in how much of waters are considered national territory.
So it expanded that property. Basically every country was for it that wasn’t the European countries trying to steal their Cod.
Charles: Is there anything that you wish people would ask you about, that’s like really cool or really interesting that you never get the opportunity to talk about. If there is, please refer to talk about it here.
Bryan: Ooh, that’s a good question. You know, actually I did go to culinary school before I got into science.
Charles: Well, why did you leave, um, the culinary world?
Bryan: Yeah, I left mostly because I wanted to work better hours for not terrible pay
Charles: And so you chose to become a graduate student?
Tessa: Good decision.
Charles: A lot of opportunities for growth.So many academic jobs on the market.
Bryan: Yeah. Old habits die hard, you know,
Charles: But I, I, in a weird way culinary school and like wet lab biology have a lot of the same skills.
Bryan: Yeah. I would agree. I often feel like I’m cooking. I just don’t get to eat what I make anymore.
Charles: It’s just like forbidden snacks. Have you been on the, um, I’m going to cut this out.
Cause this is just an unrelated tangent, but there is a subreddit called forbidden snacks and it’s like things that are not food that look like food.
Bryan: So this is where tide pods got their glory days.
Charles: And I feel like your situation is like a weird, like not the exact genre, forbidden snacks, but sort of a spiritual cousin… feels like cooking, but it doesn’t produce food rather than looking like food, but don’t eat it.
Bryan: Right, yeah. I would agree with that. I always, you know, what’s the difference between a protocol and a recipe,
Tessa: You know, this is why I’m glad I do all theoretical work. You never have to worry about this conundrum.
Charles: You have like a favorite science fiction area or are you more of a fantasy guy?
Bryan: Yeah. Um, I mean like the books I read are probably mostly fantasy or like true crime books because who wants to sleep at night on me? Yeah. Who needs it?
Charles: Well, I used to really like post-apocalyptic and dystopian fiction and nowadays it’s like, I can’t. I can’t.
Tessa: Yeah. Yeah. It’s like, there are enough bummers going around.
Charles: I don’t need, like, I don’t need a harrowing story about the effects of climate change because I’m living the harrowing story about the effects of climate change,
Bryan: Right, yeah. No, I feel that every like post-apocalyptic media thing, that’s coming out lately. I’m just like, well, I’m already in the beginning of one.
Tessa: The only thing I have to say is that consuming that media has actually it’s given me unrealistic expectations for how long it would take. Cause I thought, Oh, it’s the apocalypse. You know, the world will be over in a matter of weeks, but I mean, man, this is taking forever. Can we just cut to the chase already?
Charles: Well, this is an interesting… this may or may not be interesting thing, but one thing I think about constantly is what it would be like to be trans in like a post-apocalyptic novel post apocalypse, mostly because like where would you get hormones, you know?
Tessa: That’s a good question. I know estrodiol can be synthesized by… from soybeans.
Apparently. I don’t know the chemical…
Charles: So that’s that taken care of.
Tessa: Yeah. So I’m good. I don’t know about y’all.
Charles: I don’t actually know what testosterone is even synthesized from.
Bryan: Yeah, I don’t either. Um, I know that there are like certain foods that might increase your levels slightly. So maybe you just have a 90% like garlic diet.
Charles: See, this is what’s really tragic is that I love Fabaceae. Which is, you know, the, the family that soy comes from, but also like beans in general.
But that’s the other thing is that there’s the, you know, the like soyboy right wing nonsense, but there’s actually no robust evidence that eating large amounts of soy meaningfully increases your estrogen,
Tessa: Phytoestrogens are like, super weakly bonding to estrogen receptors. They essentially have no real pharmokinetic effect on the body. Trust me, I’ve researched this. Yeah.
Charles: Yeah. For years I denied myself soy out of the thought that well, maybe, and I just wasted years of my life not eating like tofu.
Bryan: Yeah. I definitely did the same thing. You know, the early transition days when like every ounce of hormones seems to matter the most.
Charles: Yeah, like getting then getting back to being trans in science. Have you… do you think that being trans has really affected your experience of science?
Bryan: In some ways… I mean, I think the way I present myself, I probably get read as like a gay CIS man a lot.
Bryan: I can’t really talk about sport in a convincing way.
Charles: Who Among us can.
Tessa: Yeah. Yeah. I was about to say you’re in the, you’re in the right place here.
Bryan: Yeah. See, so it’s like default conversation piece and I think that’s the thing that like lets the dog out of the bag so to speak or however that goes. And so I…
Charles: I don’t really try to talk to straight guys that much.
Bryan: Yeah. I generally try to avoid them as well.
Charles: I mean, no offense to the straight guys that I like. Um, like my dad.
Bryan: But to answer your question. I think there’s always like a weird extra layer of coming outness. Um, and so it’s like maybe I get read as gay, but then if I have a good currently female presenting partner, then, uh, folks tend to ask strange.
And inappropriate questions or, you know, if I need a letter of recommendation and it’s for like a LGBT specific group, then that can feel a little awkward.
Charles: Are you pretty stealth in the academic environment, or are you like out to people? Are you out to certain people and not other people?
Bryan: Yeah. I’m not like intentionally not out to anyone, but, uh, I don’t like tie a trans flag around my neck to the lab. So if it comes up or if I like, sometimes it does come up, especially academics really are shooting for this like diversity and inclusion trainings. And so I like to sit in on those and see what people have to say. And so it will come up sometimes I don’t stop it from coming up, but I think there’s probably a good mix of people that don’t know still.
Charles: Tessa, I know that you’re, like, very out.
Tessa: Yeah. I mean, it varies. Um, I’ve definitely been to conferences where I’m pretty sure, like almost there no one knew, but I’m in a very small field and I was in an even before I transitioned. So, I mean, it’s not exactly concealed.
Charles: Have you gotten, have you gotten the like classic, oh, have you read this paper that like your brother published?
Tessa: I have not. I did have somebody later on realized that, Oh my God, you were the person who presented this talk about, you know, glacial microbial ecosystems, you know, way back in 2012 or whatever. And I just put it together. Cause I made an offhand comment about doing that.
And up until that moment, that person had not made that connection. That. You know, pre me and now me we’re the same person, but that’s about as close as I’ve gotten. I haven’t gotten the, Oh yeah. You should check out this thing that your brother wrote yet.
Charles: Yes. Make a little bingo card of like all the things that could happen and then mark them off when they do. And then it’ll be the most boring game of bingo ever. It’ll probably take 20 years, but…
Bryan: You get a prize.
Charles: But you get a prize. You have to give it to yourself because nobody else. Playing bingo with you, but…
Tessa: Bryan, what you’re working on sounds really, really cool. And I look forward to seeing what you come up with.
Bryan: Yeah. Thank you.
Charles: Bryan. Do you have any final thoughts?
No. Cats are great. Science is good.
Charles: Science is pretty good. Not as good as cats, but what could be?
Charles: Nothing.Okay. Well, that’s as good of an ending thought is I think anybody is ever going to get with anything they do in their entire lives. So I will say Bryan, if people want to find you online, where should they look?
Bryan: Yeah. So my Twitter is @abstractbryan, or, um, you can find me at a website, bryanklynn.com
Charles: So if people want to find me, I’m on Twitter @cockroacharles, and Tessa?
Tessa: I’m on Twitter @spacermase.
Charles: And the podcast is on Twitter @ASABpod or at our website, asabpodcast.com. Thank you for listening. Catch you on the flip side.