On this podcast I talk with Dr. Brian Chen who recently left a life of research and academia for the venture based world of insurtech. Listen to how Brian is making the transition, and what discoveries in science have “WOW’d” him along the way. Also hear how Brian get introduced in “feeling Minnesota” during his first week on the job!
WATCH BRIAN SAIL ACROSS THE ICE —— CLICK PICTURE HERE ———————–>
Jon Sabes: So today with me is a doctor Brian Chen. Brian recently joined the company but we wanted to podcast interview him because he’s making a transition from academia to business. So how’s that going so far
Brian Chen: It’s pretty cool. It’s exactly as I was expecting and more. I think besides meeting awesome people in this wonderful town of Minneapolis, Minnesota, I think there’s a lot of really cool and dynamic and exciting things going on in business at all levels, not just scientifically. So, it’s really cool to see how the science translates and I think that’s something that we don’t do enough in academia, but it’s the name of the game here in business
JS: So was that something you always planned to do with your career?
BC: I never even imagined that my life would lead me to this but I think I’ve always been kind of a innovative and entrepreneurial thinker and so a lot of my friends always thought that I would have my own startup or do something totally different from science. So, it’s interesting how this kind of all worked out where the science and the business can all come together and we’re developing something new. We have that, you know, dynamic and creative environment
JS: Cool. So, here are. You end up here. You hold a PhD in epidemiology. What is epidemiology, Brian, and, so, please, for all of us, you know, so we can understand it
BC: Yeah, so common misconception, it is not the study of the epidermis your skin. It is not a skin disorder. It is the study of epidemics and how they spread. And so it began when, I think yellow fever started at the CDC but even before that the first epidemiologist was this gentleman in England named John Snow. He found that he noticed that there was a cholera outbreak and he somehow figured out that it was spread in a pattern, a geographical pattern and he figured out that it was the water supply, that it was certain water supplies that were being contaminated and it was leading to drinking water for certain neighborhoods. And he found that pattern and it took the handle off the pump of that and stopped the outbreak supposedly. People have since thought that maybe the outbreak was already curtailing by itself and then…
JS: Maybe he wasn’t so ingenious after all, but nonetheless there’s epidemiology, the sp
reading of epidemics.
BC: Right and how do you identify the pattern and stop it and so that’s kind of evolved from looking at infectious diseases now to chronic diseases and use the same principles.
JS: So right, same principle, chronic aging, disease that, yeah, wow.
BC: It’s really just now it’s kind of a methodology of how do you study human beings not in a controlled environment not in an experiment. People who are free-living, how do you get some truth out of that and so there’s a lot of little techniques that involve statistics, involve study design. So, that’s what we do.
JS: That’s a great segue, right, you ended up working under Dr. Horvath over at UCLA. Dr. Horvath is a biostatistician, right, so what are those tricks and where does that come from and will ultimately lead us to epigenetics, right, and where that happens.
BC: Yeah, yeah, I started working with Steve Horvath back and before epigenetics became a popular. He was well known for doing network analysis, so looking at biology and genes as biological networks, genes interacting with each other and he was an innovator in the methodology and creating software and working out the mathematics behind how you actually analyze that. And then on the side he was also building prediction algorithms using machine learning, the latest cutting-edge technology that people are probably doing in Silicon Valley and Google, but he was applying it to biological systems and so he became a leader in that and then I think somehow we stumbled upon the epigenetics, the DNA methylation and then he was able to then take the prediction algorithms meld it with the epigenetic data and that’s how you came up with this epigenetic clock. And luckily I stumbled into his lab looking at network analysis which still might be relevant but now where we’ve been collaborating for the past, since 2012, the past five years on the epigenetic stuff.
JS: I noticed you’re pretty much a prominent author on all of his work so you’re clearly been in the in the middle of it in the thick of it with him. And I was also amazed when I, too, stumbled into Steve’s office about a year and a half, almost two years now. Amazing. That the idea between these statistics, right where I was thinking, oh he identifies specific places on a gene and he measures things but it was kind of the opposite, right, he lets the data do the talking, writing and I was like, what? We look at a scatter diagram and we draw a line and we say, yep, those are the places. We have no idea you kind of what they may relate to but they are the places.
BC: So, that’s kind of a difference between I think academia and our application here. I think in academia you’re always looking for the mechanism, what gene is causing this? So, you do a different type of analysis when you’re trying to identify the biology, the mechanisms behind it. If your goal is to do prediction though, that’s when Silicon Valley, like prediction algorithms, machine learning comes in. It can predict really well, it doesn’t tell you anything about the biology or at least not much, but it does what it does and you can do both if that’s what you’re after.
JS: Yeah, yes, that’s cool. So epigenetics. How does that differ from genetics? What is that?
BC: So, genetics is your DNA sequence, largely unchanged. You’re born with it, so if you take a blood spot of a newborn and when they’re 80 years old, it is essentially exactly the same and also when you look across cell types, you take a sample out of your blood, your saliva, your muscle, you’re going to get the same genome. Your genetics are going to stay the same, the DNA sequence is unchanged in every cell type but your epigenetics is different across the different cell types to a large extent and the epigenetics. As an analogy, let’s say the genetics is static like a keyboard of a piano. Everyone has the same piano keys but yet you can write different songs from it and so the piano player, if you will, is the epigenetics. It could take the same building blocks and paint different pictures, make different songs,
JS: It’s a changing thing and part of the fun of what we’ve been working on has been learning the actual underlying science. I mean a chromatin, right, you know maybe for everyone out there this DNA sequence that is in every single cell as you described. I believe that everyday it’s about three-and-a-half feet long when fully unraveled and then it’s wrapped up in these things called chromatin, do I have this right? And then these methylation, what we read, how it unwinds like a scroll almost to allow for a different a song to be played as you were describing.
BC: Yeah, so DNA, you could think it’s, we always think of the double helix that Watson and Crick identified, but really it’s balled up like a like a rubber band ball and it essentially, you can say, breathes, opens up into different sections which allows a space for proteins and things that do stuff to get in there and transcribe and actually use the DNA and then it will close up and then it’ll open up in different spots. And so it’s constantly changing that way and that’s kind of where what the epigenetics are, it takes these proteins called histones that bind onto your DNA and then they create this three-dimensional structure. The methylation is one part of it but then there’s other marks. There’s acetylation, there are kinases. There’s a whole bunch of proteins that add different marks on and we’re not able to measure all of those very accurately right now. But the methylation we are and so right now we’re seeing that there’s a lot of signal in the methylation itself and so you, when the technology advances, who knows what we’re going to be able to do. And all this under the umbrella of epigenetics.
JS: Incredible. You know I I was in a conference call the other day with some scientists. Of course I’m learning. But even in the methylation, they’re still learning. There’s methylation, iteration one; methylation, iteration two; and so on and so forth. So even these kind of on-off switches go multiple times in and, am I right, where people don’t know how far that goes and it’s a really fascinating area of science right now.
BC: Yeah, totally, and scientists are still learning, too. You’re on the cutting edge. So, methylation can occur, we’re only looking at methylation that occur on cytokines, a certain base pair, concerned base of the DNA, but methylation could also occur on histones and they could occur on other proteins and so we don’t know what those are doing in terms of changing the shape of the DNA.
JS: So a lot to still be learned.
BC: Oh yeah, sure, we’re just at the very tip of the iceberg
JS: Wow, so in your research what have you come across that has surprised you the most. I’m amazed and surprised every day but for someone who’s been in science and going deep into it, maybe in your own research and then outside of your research what has blown your mind?
BC: Yeah, so my research has historically been focused on genomics in the broadest sense, so looking at your static DNA sequence but also looking at your gene expression your or the mRNA and I’m looking at the methylation and looking at metabolites, so small molecules that are floating around your body in your blood and I’m pretty astounded by most things that I touch I would say are kind of null associations. I’m kind of the wet blanket in research where everything I touch, I’m like, everybody else finds an association but I don’t. But with Steve Horvath’s epigenetic clock it seems that this is something that’s pretty robust and everything that I touch it seems like it still stands up and I’ve talked to number of our collaborators at Johns Hopkins University and other institutes. They all were very skeptical at first but they are also surprised at how well it works across different samples and you don’t see that very often in science even though we you know we tout these great discoveries, there are only a few things in a lifetime where you’re like, wow, this really, really works.
JS: So, I’ve got to get disclaimer out there for our listeners. That was not canned, right. I had no idea that that was going to be the answer. I’m pretty psyched that it is. Wow that’s that’s pretty awesome. So go outside your field. You read science journals, what did you read about that just like really blew you away?
BC: Yeah, well I’m interested in the aging process and that’s why well I was at the National Institute on Aging because that’s what I’m interested in and I think still there is this idea of heterochronic parabiosis.
JS: Whoa, unravel that one!
BC: So there are their group of researchers at Stanford University and then at Harvard who found that you could take kind of Frankenstein experiments. You take mice; a young mouse and old mouse, you can connect their blood vessels through their torso and so they’re connected, they’re Siamese twins and what you find is that you could take an old mouse and an old mouse, and Siamese twins them, do that for two young mice and then you what you find is that the ones that are heterochronic – hetero meaning different, chronic meaning times; different times, the young and old – what you find is that the young mice start to get traits of older mice, they don’t heal as fast, they start to get aging diseases and then the older mice get the opposite. So there’s some sort of rejuvenation. And there’s been a lot of debates in that zone, exactly what is causing that rejuvenation and people are actively, actively trying to identify what it is and there’s, yes it’s a hot topic right now in the field of aging, and there’s a New York Times article on this, maybe two or three years ago, and they found that you don’t even have to connect the blood systems, you can just…
JS: Hang around old people? If they want to hang around me maybe I should be concerned or I should be hanging around younger people.
BC: If you hang around younger people you probably act younger.
JS: What are you doing this weekend? Maybe I should hang around you?
BC: I’m not that much younger (laughs).
JS: Young enough.
BC: So people have found out that you could actually take the serum of a young mouse and inject it into an old mouse regularly and get the same benefits, essentially.
JS: Wow, okay.
BC: So, now they’re doing that in human clinical trials where they’re basically doing that.
JS: Wow, okay.
BC: And this is the only time where I’ve ever seen a clinical trial where you have to pay $8,000 to participate in it!
JS: That’s amazing. They’re conducting it in Los Angeles, aren’t they? Beverly Hills?
BC: No, Silicon Valley.
JS: Of, course! Sure.
BC: There are plenty of Facebook employees willing to pay that,
JS: Yeah, Mr. Zuckerberg is probably there right now. He’s like, young forever, baby! Oh gosh, that’s cool. Alright, well that works. I’m wowed. So you’re in Minnesota now, at least for the week, but tell me about Minnesota. We had a little fun, an activity yesterday. Tell the audience just so we can talk about fun.
BC: So yesterday it was a cold and blustery day and when most people would think that is a good time to sit indoors and maybe get some work done, Jon Sabes decided that, hey, this is a great time to do, you know, what, to do ice sailing?
JS: Iceboating.
BC: Yeah, so we went out onto Lake Minnetonka
JS: That was it. You got it, yeah.
BC: Yeah and we had an ice boat set up and luckily we had a couple of sailors on board and we took off and when I say take off, you could see the gusts of wind blowing snow across the frozen lake which, by the way, I’ve never set foot on.
JS: Well, if you’re going to get on a lake that’s the right way to do it right?
BC: I mean, we were flying across the lake at speeds that no human being should travel at without a seatbelt.
JS: For the record, innovation, the fastest man made machine prior to the invention of the internal combustion engine was?
BC: The iceboat.
JS: There you go. We had a nice wind. It was north-northwest, probably consistently 20 miles per hour gusting up to around 30 and an ice boat will do two to three times the speed of your wind, so yeah Brian got a great introduction to feeling Minnesota and for anyone who wants to see a picture or video, I’ll be posting that on my blog, jonsabes.com
BC: And he will be offering free rides to anybody who wants to invest in GWG.
JS: Why not? Or come to work for us.
BC: It’s a perk. No one else will offer that. Facebook can’t even offer that.
JS: Guaranteed. Well it’s been fun talking to you Brian. I appreciate your insights and wow, on two counts, on the success of the Horvath clock and on wowing me anyways which is somewhat difficult to do on something really cool going on in science. Again welcome to Minnesota and look forward to having more Minnesota experiences together and we’ll be back on this podcast. We’ll revisit them, okay?
BC: Alright, looking forward to it. Thanks for having me on.
JS: Cheers. Hey, thank you for tuning into the Jon Sabes Innovating Life Insurtech podcast. Today we heard from Brian Chen, an amazing scientist and an incredible contributor to the world in which we live. I hope you enjoyed the podcast today and you’ll tune back at jonsabes.com where we’ll always have interesting and intelligent conversations.