EBRC In Translation
EBRC In Translation
13. Automating Biology w/ Doug Densmore
In this episode, we interview Dr. Doug Densmore, a professor at Boston University and co-founder of Lattice Automation, Asimov. and Biosens8. We talk to Doug about developing laboratory workflows as a service for engineering biology, finding your own management style, and treating automation and algorithms as first-class scientific citizens.
Links and notes for the episode:
Lynn Doucette-Stamm Runs the BU COVID testing facility
Programming Biology
Stem Pathways
Asimov
Lattice
biosens8
International Workshop on Bio-Design Automation
EBRC SPA Mentorship Program
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Episode transcripts are the unedited output from Whisper and likely contain errors.
Hello and welcome back to EBRC in Translation. We're a group of graduate students and postdocs working to bring you conversations with members of the engineering biology community. I'm Ross Jones, a postdoc in Peter Zanscher's lab at the University of British Columbia. And I'm Cook Zee Lee, a postdoc in Fu Zhong Zhang's lab at Washington University in St. Louis. Today we are joined by Dalgas Densmore, an associate professor from electrical and computer engineering at Boston University. Thank you so much for joining us today. Thank you guys for having me. To start off, can you tell us about your journey of becoming a professor and what drew you to synthetic biology in particular? Yeah, thanks for the intro and thanks for having me. We'll start with an announcement, maybe announcing this first on this podcast. I am a professor now, so I'm not even an associate professor. Oh, that's amazing. That's relatively new news this month. So I might as well get the intro right. Might as well play that up. Makes me feel old more than anything else. But my journey into synthetic biology really began, and it's just one of those crazy happenstance things that years ago, I got my PhD in electrical engineering at UC Berkeley, and I did completely embedded system design. And my PhD was really how we have formal methods to verify that systems do what they're specified to do. So take the example of like a smart braking controller in a car. If you write a specification of that and then you make a system, you want to make sure the system breaks in the time that was specified. So we made a whole bunch of tools and things to do that in graduate school. But at some point, somebody, a chemical engineering friend of mine told me about a talk that Chris Voight gave when he was at UCSF in making genetic circuits. In particular, it was something about some logic gate at that time in a bacteria. And a friend just told me about this. He said, Doug, you make gates for electronics. You should look into this bio thing. And I kind of said, yeah, whatever. I jotted it down, and I never thought I'd return to it. And then I finished my PhD, and my girlfriend, who's my wife now, was finishing her PhD in mechanical engineering, but had a couple years left. And I said, well, I'm not going to try to do this long distance thing. I better stay at Berkeley and do something. And I got a fellowship, something called the UC Chancellor's Postdoctoral Fellowship, which basically is a fancy way of saying you have a fellowship. You don't have to listen to your advisor. You're kind of independent. And I said, well, I will do something new. And I was looking through my notes and I saw this note about this guy at UCSF. And so I reached out to him just to learn about what the CAD problems were in that space. And he really was like, CAD problems, we don't even have any CAD. And so I went over to UCSF from Berkeley and chatted with him. And what I liked about him is he could have blown me off. He could have said, whatever, this postdoc's coming over or whatever. But he didn't. And he got it. He got why electronics were possible because of engineering and because of the rigor that went into design automation. And he was eager to apply that to biology and not just lip service. He wanted to actually do it. So I spent some time with his postdocs and people learning more because I knew nothing. And eventually it just became easier for me to collaborate with a professor at Berkeley named Chris Anderson. And Chris was like, I don't really know how to engage with you really, but why don't you co-run an iGEM team? So he had an iGEM team he was doing and I ran the software version. So in 2008, I ran the first UC Berkeley iGEM software team and we were successful and yada yada. And I can go on. I don't want to ramble on, but that was kind of the genesis. This me figuring out that the Bay Area was a good place to engage the synthetic biology community and then using a postdoc that I did two years at Berkeley, one year at the Joint Bioenergy Institute, which is a research institute in Emeryville, California that's kind of run by Jay Keasling. I spent some time there with Nathan Hilsen, who's a researcher there building software tools for synthetic biology. So that's kind of how I got into it. Yeah, you worked with Chris on Cello, which is an amazing paper and has since sort of led to the formation of Asimov. Would you say that this is sort of the origin of it going way back to those initial conversations? Yeah, and I think that was a vision that we both had. He had the, to his credit, he had that vision and knew that he could build the biology to do it. But building the biology and just doing it isn't the point. It's not, the point is not that one lab does it and he's like, I built genetic circuits. The point is not only do you do that, but then there's a methodology on top of it and a set of design flows and processes. And he knew that given my background, I would be someone that could bring that software and kind of what we call digital logic design background to it. So it really was a true collaboration that did start as early as 2007, 2008, when we first met. So I was really glad that was kind of the culmination of many years of research. Yeah, that's really cool. And speaking of Asimov, we're wondering if you could tell us a little bit about how that venture got going and, and the other companies that you founded, including Lattice and Biosense 8, or I don't know if it's called Biosense 8 or Biosense. It's Biosense 8, you're right. And the only one I can really talk intelligently about is Lattice, but I can, I'll speak to a little bit of each of them really briefly. I don't need to start a company. I don't mean that like financially, or I mean that like free time wise, like I have plenty of things to do. But making software and tools for synthetic biology academically can only be so rewarding because at some point, those tools need to get to people. And at some point, those tools need to be polished and usable. And academia doesn't do a good job of polish and usable, and academia doesn't really just do a good job of distribution. In fact, in some kind of perverse way, academia is really incentivized by making things not accessible. Because if you're the one that does it, you're the one that's famous, you're the one who gets the paper published, there's different ways to have impact. But to that point, I wanted to get people to have my software tools, and people would engage with me and there was no clear way to engage with them in a PhD kind of environment. Because they would want things deployed, they would want bugs fixed, they would want things that don't lead to publications, and that PhD students didn't want to do. So in 2013, I spun out Lattice Automation, because I simply wanted a venue for people to get software. And I didn't need to make that company big. I didn't need to say, let's go after venture funding, and that I just went to the people who were already contacting me. And to our credit, since 2013, now to 2022, we have been consistently getting business in six month to a year contracts, doing custom software development. So it's a relatively small kind of lifestyle company. It works with cool projects, it works with diverse clients, and it makes software. And then people get that software and they use it. There's lots of examples I can give you. But then Asimov was really spun out of really, I give all credit to the other co-founders, Alec Nielsen and Raja Srinivas, who really have taken that company to the next level. And the genesis of that was, how can we take the ideas from Cello and make kind of a commercial entity? And the general idea of Cello at the highest level is that computation and software should be involved in genetic engineering, in synthetic biology. That there should be an equally important computational aspect to the design process. And so Asimov kind of initially was seeded with the idea of a Cello-like workflow, but now has grown into something much bigger. And it's the idea of engineered mammalian cell lines for therapeutics. And so the business model around that is a specific set of engineered cell lines. But what differentiates them from other people who would offer these cell lines is the fact that it will come bundled with this software environment that lets you explore how to compose these systems, how to use these cell lines, how to learn from the data. And then ultimately, internally, that data also runs a really efficient foundry process within Asimov to do all kinds of interesting things. So it's that whole idea, and it's become a much bigger company. And then finally, again, for those as I want to keep moving, BioSensei comes from the fact that I got involved in recent years in microfluidic design. And because the genesis of this at the highest level for the listeners is that if I made a genetic circuit, I put all of that DNA in the cell. And at some point, that amount of DNA inhibits the cell's function. Either it becomes toxic or the cell can't actually execute that program the way you'd expect. There aren't enough orthogonal signals within the cell. So from a CAD standpoint, I said, well, this is just a graph. But now the graph of interactions is too big for a single environment. We need to subdivide that graph. And that now becomes another cell. And the issue now is the wires between the cells. If you don't do them in a certain way, it's a broadcast that the protein comes out of the cell and just diffuses into the media. How would you control that in a more focused way? You would put that in an artificial environment like a microfluidic to control where the signal goes. And so I started to make lots of CAD software that would spit out a microfluidic as well. I work with a professor named James Galligan here. They do biosensors. I did the microfluidic design for those biosensors to be deployed as wearables. And so that's a wearable biosensor company. And that's just starting. So again, in each of those cases, the key kind of thing was I didn't start a company and then figure out what I wanted to do. It was that we had things that I was like, we have taken these about as far as we can take them academically. And I can keep publishing papers, but I'm not really motivated by that at this stage of my career. It was really like, let's see if we can actually get this to work. And that's why the company spun out. Well, this is very exciting. And thanks for sharing those brilliant project with us. So as someone who has split time in between being a professor and running companies, what does a day in life look like and what are the most exciting things you look forward to? That's a good question. A day in the life for me is talking to as many people that I can, that can amplify my efforts. So I really don't do anything anymore. Like when I was, I mean, uh, you can see in the back, if you're watching my zoom call, you can see in the background, I've got all these arcade games. You can't see it on the podcast, but I have a bunch of arcade games. I have over 60 classic arcade games, and this is the ones that have overflowing in my office. So what the point of that is I used to like to do things. I like the program. I'm an electrical engineer at heart. I like to tinker. I like to work on electronics. I like to build systems. So a day in my life used to look like building things, and now it doesn't. And so the day in my life, now I try to get as many other people to build. I don't want to spend days of my time on committees. I'm not really big into pedagogy development. A lot of people really want to focus on how they can grow their department or do things. And that's all really valuable, but I really like to talk to people who are like, how are we going to help build things? So I may have a meeting with lattice in the morning and say, what company engagements are we going after? What problems are you solving? Let me help you push those forward. Then I'll jump on calls with my students and say, where are these papers block? Let's whiteboard an algorithm idea. Let's whiteboard this. I might then have to talk with someone who's going to give us some funds for something. So I write a lot of grants. I polish papers at the end. I like making connections. I like collaborative work because I can't do anything without collaborations. I mean, even after, you know, how many, like 15, you know, how many years I've been doing this at this point, I can only answer about three questions about biology, three why questions, why, why, why deep before I don't know anymore. And so I have to collaborate with people. So I like finding connections and my current hobby horse is connecting electronics, like custom CMOS based electronics to biology. So that's a rambling answer. If I just try to connect people, write grants, get funding. And I do like to have technical conversations. Like I like to sit down and I like a whiteboard and I like going over things with my students. Yeah, it sounds like fun. Speaking of big collaboration. So you've been leading the living computing project, which actually my PhD advisor, Ron Weiss was a member of. And so we had people working on that with you guys. We were wondering if you could tell us more about this project and what you guys are up to. Yeah, that's great. So the living computing project technically is sunset. It started in 2015 was a six year grant. So it kind of ended towards the end of 2021, but the living computing project, which you could find out more at programming biology.org was something that came out of the NSF expeditions in computing program. That's the largest computer science award that NSF gives. It's typically about $10 million. So it's a big grant for NSF. And the idea of it was to just see which computational paradigms we could get to develop in living systems. And those paradigms were digital paradigms, on or off, analog, continuous memory, a signal on shuts off and it remembers it. And then communication long and short distance. What we wanted to do was to see which of these paradigms were applicable, which of them we could measure quantitatively. And then once we could do that, which of them we could effectively disseminate. And again, it was a joint grant that had people at MIT, people at BU, people at Raytheon, BBN technologies. It really, its legacy might actually be a large outreach program called STEM Pathways that spun out. So I think the grant was great at bringing people together. I think the grant was good at getting kind of individual pieces of research together. I don't think the grant did as good a job as I would have liked at really saying what the limits of this work was. And I should also mention this had people like Pete Carr at Lincoln Labs on it. I'm sure, I feel like I'm at some Grammy or some acceptance speech. I'm going to forget people when I mention it. You guys can see this at programmingbiology.org. The genesis of the grant, I won't go on too much, but the genesis at the core for me was there's something called the International Technology Roadmap for Semiconductors, the ITRS. And it says that in these years we'll be at this node in terms of the size of a transistor, 65 nanometer, blah, blah, blah. It talks about how big transistors we'll get. It talks about the cost of silicon. It talks about 5G, et cetera. It talks about where we'll be. I wanted to make, and still am interested in, right now I can make a genetic transcriptional translational logic circuit that has 10 promoters. What will we be able to do in 2030? What's the roadmap for that? I can record this many genetic states. What will we be at in 2030? The biggest piece of DNA I can synthesize is this. Where will we be in 2030? Get that roadmap kind of developed and really hit the metrics. I would love to have someone say, I have the biggest genetic circuit. And biggest would have to be defined. It's not necessarily base pairs. It might be the most number of interactions. I would like to have quantitative metrics and roadmapping to push the field forward, set up some grand challenges. That was kind of what we had hoped. And I mean, I still can do that. I think it did a good job of research, but there's still a lot left on the table in terms of roadmapping and metrics. That's fascinating. So leading a collaborative project like this across different universities is certainly not easy. So could you tell us more about your philosophy for leading such a diverse team? I think that's an interesting question. And I would say that learning about myself as a leader is probably been one of the biggest things that have happened to me since I've become a professor. And frankly, learning about my shortcomings as a leader. I would be a bad leader in the military because I would be like, well, let's go take that hill if you want to. I'm going to go do it. And if you guys want to follow, follow me. I kind of lead by example, but I'm not somebody who really, I tell people things a couple of times, but I'm not someone who drives their agenda, maybe as much as I should. Now, given the kind of people you mentioned, Ron and Chris Voight was on that and Domitil Devette, like, I'm not going to be able to herd those cats. Like those are really established, high level, really successful researchers who I don't really want to tell them how to do things. It's kind of like if you have LeBron James and Michael Jordan, like you don't want to really tell them to play like, hey, you guys do your thing. But you can see that sometimes, back to my basketball analogy, that the US lost some basketball games in the Olympics. Even with the best people, you need someone to be a coach. And sometimes I can do that and sometimes I can't. So to answer the question, what I typically, my leadership style is to typically hear what everyone wants to do, because that's what they're going to do at the end of the day anyways, what they want to do. And then weave that into a coherent vision that maybe I can stitch together the parts. Oh, you want to do this genetic circuit exploration? Well, who needs data from that? Let me then get them the data. You want to explore this mechanism for biological computation. Could we apply that to one of the paradigms? So I try to step back, see what people are doing, and then weave that into a story. And then the people I can motivate typically are the students, because they are motivated differently. And I can say, why don't you help me be successful at that? And then I can help you be successful where you go next. So then try to motivate through the students. I think it's hard though. I mean, some people, yeah, I'm also not, I think something that I could do a better job of is this came across, this is a classic example, and I'll do this for the podcast. When I got married, I said we did not need a rehearsal. I said, how do you need a rehearsal? It was like a hundred person wedding. We'll just do it. When the music plays, we'll walk down. My dad will walk down the aisle with my sister-in-law. It's simple. When the person gets to their seat, the next people walk. And then when that person happens and I was like, got it, everyone? Then I got it. And of course, the day of the wedding, people walked at the wrong time. They didn't wait for the music. So something that I know that I need to do is not make assumptions about what people understand in a project and be more explicit about my expectations. Sometimes I'm like, I tell people things and I expect them to know what to do when they might be looking for more guidance. I see that often with students and that's probably something I could do a better job of. Because again, I often, I'm like, they know how to do that. I mean, I do it all the time. If my nephews came from Arizona, I'm like, shovel the driveway and then for snow. We have a bunch of snow now. I'd say shovel it and I'd leave and then they'd come and they would shovel it in the next door neighbor's driveway. And I'd be like, no, you need to shovel it, but don't do it in the driveway. Put it here, do it. So I often need to give more granular instructions. That would be my. Yeah. This kind of flows into our next question and builds upon it a little bit. You recently set up a COVID-19 testing lab at BU and we're wondering about this program and how you leverage your expertise to set it up. But now I'm also curious about how you've sort of managed this in relation to how you've managed your lab and these collaborative projects. Okay. This is definitely one of those Grammy speech moments. I am one of a lot of people responsible for that facility. This goes all the way from the president of the university down to 35 technicians that run that place. So it spans a whole bunch of these places, not the least of which is Kathy Clapperich, who was a BME professor here, who also helped spearhead it. And also her last name is going to escape me, but I just always say Lynn. Lynn is the woman who actually runs the lab day to day. And we can add that probably in the notes of the podcast or something. I'll get you the name. But the general point was in the summer of 2020, BU said we are going to have to keep moving. And how can we move forward in a responsible way? One of the ways is to test our students. And they ran the analysis and they said the cost of external testing is not going to outweigh the costs and convenience in a lot of research aspects of doing testing in-house. So they said, where can we do testing in-house? They said, where do we have the lab space and where do we have the expertise for automation? And that naturally led them to the damp lab, which is design, automation, manufacturing, and prototyping. It's a cloud lab that I started at BU. And we had a whole open space and robots, or at least one robot, two robots that they could use. And they said, well, we're going to need to get our own robots. So we got eight robots, a whole bunch of PCR machines. And my job was to coordinate the programming of those robots and the connection of the LIM software to the student health records and medical records part. So I was not involved in the assay development. I was not involved in getting emergency youth authorization for these protocols from the government. I wasn't involved in the logistics of getting the equipment, ordering the equipment, storing the equipment. How do we get students tested? How do we do that? I was responsible to make sure their automation and software worked. And to that end, my student, David McIntyre, was huge in getting the robot set up. A former student of mine, Danny Fu, really helped get the LIM system integrated. Students Rita Chen and Luis Ortiz did a lot of getting that protocol to be converted to the robot. So my lab set that up, and then everyone else ran it. But I think back to the impact and the things about those companies, we do and did, we've slowed down now. We did at a height about 6,000 RTQ PCR tests a day. Not only did that, but tracked all the data and descended the results, ended the contact tracing. And that was done relatively quickly in the span of months to get set up. It's probably been, without any hyperbole, the most impressive engineering thing that I've been involved with. And I think it's because COVID was real, meaning it wasn't like, oh, we'll figure this out later. It had a real sense of urgency, and it had a real economic impact on the university. If they can't get their act together, students can't go back. If students can't go back, then there's a lot at stake. And also there was a lot at stake, frankly, and I give a lot of credit to the BU leadership. There was a chance that a lot of, this could have been a disaster. It could have been people came back, the test didn't work, COVID spread around campus. We could have been one of those headline news stories about a real failed COVID rollout, but the opposite really happened. I think it's been fantastic. It's been a real, and when you tried to get tested, I don't know if you guys have tried to get tested, at least in the Boston area, at one point it was a real pain. You had to wait in lines and you couldn't get in the test. So the fact that faculty had that on campus, like, and you didn't have to go to an external place and you could get tested whenever you wanted pretty much was really great. So I think we really got spoiled. That was a cool effort. Yeah, I believe many of us, including the audience, are amazed how powerful your expertise and synthetic biology to fit into this big collaborative project. Well, I'll say something, and this is a shot. This is a jab. This is now I'm a full professor, right? I can start taking jabs at people. I'm jabbing the funding agencies. Any funding agency listen to me, I'm jabbing you because I wrote grants. I've written them and I'll continue to write them about the need for replicable automated biological engineering, including robotic platforms, integrated software. And when I write these grants, and perhaps it's my fault, perhaps my grants aren't good enough. I'm willing to take that criticism, but the grant feedback always is, why do we need this? There's this notion that it's an optional thing, that automation and engineering and replicability is somehow an optional additional thing that can happen after the really cool science happens. And I'm making the case that is the really cool science, like getting a test that you've even done once to happen at 6,000 times a day, seven days a week, 365 days a year with diverse staff. That is an effort. And then I couldn't get a robot to save my life often before the COVID. And then eight showed up in a week, a week quote, it was longer than that. But you know what I mean? It was like, suddenly they couldn't throw enough robots at us. And I don't want to wait till the next pandemic knock on what are the next thing for us to figure out? Hey, we need a distributed biological infrastructure in this country that we can't like try to retrofit when we need it at the last minute. And then it's a first-class research citizen. It's like this whole idea of like, oh, the algorithms in the supplemental. That's another thing. I need a t-shirt that like says that's BS more of my, like, I understand there's venues that you might emphasize things differently, but I would make the case in synthetic biology that the algorithm is a first-class citizen. That's why it's synthetic biology. That's why it's not supposed to be some other biology discipline. I can get off my soapbox a little bit, but I will make the case that I'm hopeful that if anything came out of COVID for synthetic biology sake, at least in my side, is that automation is vital and that we need to make that a real focused effort in 2022 and beyond. Yeah. Before we continue on, I was just thinking of another question related to this, which is this COVID testing thing that you're talking about. Yeah. Like there's so many places where it's impossible to get a test or you have to book a test a week or more in advance to be able to get like a PCR test or something. It's important for travel, but also just if you're trying to get certain kinds of tests here and there. And it just seemed to me that so much of biology and like medicine is not like filtered down to people's lives directly. We don't have the ability to easily tell if we have the flu or COVID or cold or what kind or generally what kind of disease we do have. And I was just wondering if you have thoughts about where this is going, sort of the democratization of medicine, I guess, and what we can sort of do about it as synthetic biologists. Yeah. I think part of it to what I started about is what is our biological manufacturing, testing, experimental infrastructure in this country. Right now you have companies and you have like academic labs. Companies like Moderna, Pfizer are great, but they're making a product. They're not really saying we have this facility open for use for other people. Then academic labs are like, like they're trying to publish a paper. They're not really set up. But if you look at computing, we have that, we have a third element in computing and that's the cloud. Like there are people who make products. There are people who make, you know, have academic labs like in biology, but there's a whole service infrastructure in computing, like compute as a service. I think what we like to think about is what is biology as a service? And I'm thinking about your genesis of your first question, what the question was, but that biology as a service, I think is something that's key. And the question people say is, well, right now everyone needs testing, but what happens when we don't need testing? I make the case that we can then use that biology as a service infrastructure to start answering lots of these other big data questions. Like you can have something running and it doesn't need to be running a test for a person. It can be running a test that feeds a machine learning algorithm, because there's always this question about how are we going to do machine learning in biology? And the 800 pound grill in the room is we don't have enough data. Could we use these facilities in their off time to be generating lots of interesting biological data for a lot of interesting biological computation questions? And again, I lost track of the thread of your first question. I don't know if I answered it correctly, but I'm getting the thumbs up. I just would like to make the point, if anyone listening to the podcast, maybe you believe some of the things I said, maybe you don't, but the point that I would like to generally make that I hope people do agree with is that we do need to give a thought to how do we serviceize, make services biological experimentation? We have it as a lab. We have cutting edge services in the lab. We have scaled up commercial processes in companies. How do we take those pieces and make that available to people in the same way that an Amazon or someone has said, I have made compute resources available. Could I, as an individual say, well, I'm not feeling so good. I want to use the cloud, the bio cloud resources to run a test on me. I'm thinking of making something. I want to use the bio cloud to do that the same way I wanted to make something computationally. And I didn't have my own server farm, but now I could do this in the cloud. So how do we do that? Now there's a whole ethical and security layer on top of that. But again, that's coming anyways. We're not going to get the 2060 and not have to address biosecurity and bioethics. Like these are things we're going to have to address. Those aren't going to go away. So like we can't just say, well, this is too crazy. Let's just ignore it. We're going to have to deal with these issues. So I think earlier is better than later. Thank you for sharing these wonderful insights. Let's say we have some audience are convinced by your insight. So they want to dive into the computational or engineering world. So what do you think for people coming from a biology background? So what modes of thinking or concept building would be most helpful to dive into the computational or engineering world? That's a good question. I teach a graduate course called computational synthetic biology for engineers. And I try to get some of these concepts across. I had a similar conversation years ago with another EBRC member, Howard Salas, who was a professor at Penn State. Because when I first met him, he was a postdoc in Chris Voight's lab at UCSF. And I said to him, how do you learn this bio? Because when I learned about computers, I don't have to memorize computers. A computer fetches an instruction. It decodes it. It goes through this pipeline. The text sees hazards. That's just a machine executing. And I remember this very clearly. He was like, once you understand biology, you're not memorizing things. You understand that same machinery. So my comment to the bio folks is first try to understand the machinery of computing. In my case, you need to have some background that what happens is there's little transistors that turn on and off. And those transistors turn on and off in a way that does logic. So understand a little bit about logic and then start to realize, well, how do we get a piece of hardware? How do we make that? How does that actually work? How do we describe a billion transistors? Then maybe just understand a little bit about operating systems and programming. I feel like there's no really short, like I couldn't get into biology and say, well, I don't want to learn any of that. Just tell me how to do stuff. They'd be like, no, you have to understand some of the central dogma. You have to understand some things about bacteria, some things about, again, how DNA is synthesized or something. There was no shortcut to actually sitting down and learning that. And I feel like there's not really going to be a shortcut to trying to understand how computers work. If you really want to do what I'm doing and bring these things, you need to understand how an operating system schedules. And that operating system scheduler is a little bit how I could schedule a cloud lab and a little bit about what are some of the challenges in physical design of electronics, like design rules. How do we make sure that a piece of silicon lays out correctly? I think we could apply some of those to how a piece of DNA is fabricated. What I would say to people who want to get into computing from biology is you have to put your 10,000 hours in on some level of programming. MATLAB is not going to cut it. You need to learn a programming language. And again, that's, and it might even be if you learn that program language, you leave biology and go make six figures and some software. But you got to learn, you got to put your 10,000 hours into programming. You got to really program. It's not scripting. You need to learn how to program, in my opinion. I think the other thing is figure out an aspect of computing. That's a huge space, but you can say I'm interested in how we translate programs to execution. That could be a compiler. I'm interested in scheduling kind of computations. That's operating systems. I may be interested in analog circuits or pick one area that you are most kind of think your analogy most applies to and meet an electrical engineer and befriend them the same way I befriended a Chris Voight and really try to engage with that person and understand it. It's not, the thing is it seems a little risky, but it's actually, it'll end up making you a more unique researcher. If you're someone who can really legitimately talk about both areas, you'll be part of a relatively small group of folks and that time will be well spent. Yeah, one thing I love about synthetic biology is that it brings together people from so many diverse expertises. I'm like a kid in a candy shop when I get to talk to people that know things about computation or whatever that I don't know. EBRC in general is dedicated to creating an inclusive synthetic biology community. We're wondering if you had insights for trainees in the field about embracing and enhancing diversity, equity, and inclusion DEI initiatives to really help the field continue to grow. Well, that's great. That's a great question. I've talked more about this as I've gotten more established and older. I had the ability to talk about my experiences as a black student at the University of Michigan and I've talked more about this kind of stuff in recent years now that I have a daughter and some things I think more about her life in this world and things. I mean, and I think it's also interesting coming into biology where people are talking about this, which is great. And I'm always like, there are a lot more women in biology than there ever were in electrical engineering. So that's not to say there isn't work to be done. There is, but I think it's always an interesting perspective. My first point to everyone is just do good science and do good engineering. That makes your projects good. That makes the impact of synthetic biology important. If you're an underrepresented person, that helps legitimize what you're doing. That helps end up making, you know, you're becoming a valuable member. And then once you are doing well, I think that's ways that you can then look at how can you reach out to communities you might be a part of. Like for example, I might say I'm going to go out to the National Society of Black Engineers and think about what kind of people I could recruit to the field or go to the Society of Women Engineers and talk about where we're going to bring people in. Find messaging and mechanisms that resonate with those communities. Like help understand, you know, people often feel like there's not a place for people. So often just by being visible and saying there is a place for all kinds of diversity and different perspectives and different people. I think that's important making places welcome. I think also I have some communities, like if I talk to some students, they might say, I'm not interested in this part of electrical engineering because there's too much XYZ, too much math in this. I don't like that or too much of this. Whereas I think it's really hard for someone to say, I don't like synthetic biology because there's too much of X. Because if they tell me that, I'll say, well then don't work on that part, work on the other part. You should be able to, I think for most students that are interested, you can figure out a story in synthetic biology that resonates with them and try to find ways to recruit them into the field. And you can do that in ways through things like iGEM. That's a way to get students, kind of undergraduate students involved in synthetic biology at the high school level, undergraduate level. I think you can do this through, I mean, EBRC has initiatives, I'm sure for education and outreach tapping into those. I mean, it's kind of a ramble and I don't have all the answers. I'm not going to fix all this myself, but I think being a good role model is part of it. And just giving people opportunities and letting them know they have a place in the field. Thank you for sharing your insight. I believe that's something that we all can work towards too. Switching to a completely different topic. Previously you mentioned you have several arcade games in your office. So, you know, sometimes grad students and postdoc struggle with world-life balance. So since you are a professor and co-founder of several companies, so we would like to know how do you balance your work with other interests? I'm actually a poster child for a bad work-life balance. To be honest with you, I don't think I historically did a good job of that. And it's because I liked work. I think work-life balance is easier when you don't like work. Because then you're like, well, I will take my weekends off. That's not hard. But when you liked work and you like work, my struggle was, particularly as a postdoc and a pre-tenured professor, I was working seven days a week. Like I would be like, I'm going to go in the lab because I wanted to work on this. I wanted to program when I was a postdoc. I wanted to do that. And I'm sure my relationship with my wife suffered from that. She was busy at the time too. But as she transitioned away from academia into a workplace and she wasn't doing that seven days a week thing, how do we find time to connect? So part of it is just carving out time. I think the most important thing in that is figuring out what needs you and what doesn't. And if you get rid of the things that don't need you. So let's say you had 10 hours a day and you were working five hours a day on what you really wanted and five hours on stuff you didn't. If you cut out the stuff you didn't, you still get your five hours to work on what you really wanted. And then the trick is then don't let that flood the other five. The other five now you've cut out. That was sending this email or reviewing this paper you didn't need to review. That was you were the 20th reviewer on or sitting on this committee or like you need to do some of that. But if you can carve out the stuff you don't like, and that's still a struggle because people often want to do that, particularly untenured folks because they feel like it's going to help them move forward. But the first step one is trying to identify what needs you and what doesn't. Do the things that need you and the things that don't need you try to be judicious with. The other thing is setting whatever your boundaries are. I didn't have good boundaries with my wife, for example, because I was like, she'll be fine without me. Like if I'm busy, I'll get home when I get home. We'll just start the movie at nine instead of seven. I need to fix that. But it really became clear when I had my daughter and I had my daughter, I didn't wait to have a child until I was 40. But then my daughter can't wait. Like there was no waiting. I couldn't, she'll pick herself up from daycare or, you know, she'll fix herself dinner. She'll be fine when I get home. So that really forced me to slow down. And not only that, it also makes you realize how fast time goes by. Now my daughter's almost five. You'd be like with your like significant others or your family, you're like, well, they'll be okay. Like my mom hasn't changed that much. She'll be, you know, fine. But your kids change really quickly. And you realize quickly, if you're not careful, you'll miss out on that. And that's a motivator of nothing else to figure out your time. And then like I said, figure out where you have the most impact. Like again, I just jumped from things to things. Like I realized I can't be a depth person anymore. Some professors can. Some professors really like to be like, I'm deep in this paper and I'm tweaking this line of the algorithm and this figure and this data point. Some professors can do that. I'm not one of them. I'm like, if I do that, then I have now dropped the seven other people waiting on me for something that I could also push on and have impact. So you have to figure out where your biggest value is and spend the time. I don't know. The other thing is I think a lot of this is easier said than done when you're more established. I'd be lying if I said I could do this pre tenure and pre some of these things. You feel like a lot is at stake. And now I feel like less is at stake. Oh, I don't have an answer for that. Some of that's perspective. Some of that would be your family. But also I think work life balance. Last thing I will say, I remember years ago, I think this is a good podcast. I get to talk about everything. I remember years ago, I woke up one morning and said to my girlfriend, I said, ah, this day is going to suck. I hate this day. I was an undergrad and I was like, this day is going to be terrible. And it blew her mind. She was like, you woke up hating the day already because I knew I had to do this thing in a lab. I had to do this. I had to do that. And so I remember that to this day. And that was a long time ago. That was 20 years ago, more than 20 years ago, 23. That's kind of been my mantra going forward is I don't want to wake up, not wanting to do the day. And if that's what makes work life balance hard is if you turn to that point where you are waking up and you want to work every day, like you want to do that, then it's you got to figure out the balance because now you're like the whole day I want to do. It's easier to have work life balance when you're like, I don't want to do these things. Because then it's easy to go back to your life. So it's like this kind of ironic situation where you're spending your whole life to get to the point where you really love what you're doing. But then when you love what you're doing, you have to stop doing what you love for the other things that are really equally important in your life. And that's the trick. If I knew the answer to this, you'd be interviewing me for some book I wrote. I don't know. Good luck to all the listeners. I don't know. Maybe you picked up something in that ramble. Maybe we'll read your book in 10 years or so. I think there's some great advice in there. I think that really thinking about priorities is important. And we see this a lot of times in students and so on that I've worked with as well is we all always need to think about, okay, what do I really want to do and not get cut up interviewing professors or whatever, instead of doing our projects? The other thing I will say is don't apologize for liking to work. That's another thing. I mean, that's a strange little thing. But let's say you're in the lab and you're loving it and you're doing all this great stuff and your friend's like, hey, stop doing that. Come out to the bar. Come on, do this. Go to do this thing. Why are you working all the time? It's up to you how you want to manage your social relationships. But that's not a work life balancing if you like that and your relationships aren't suffering with your friends. If your relationships are suffering, then you have a problem. But if they're not, I don't think it's bad to be engaged in what you're doing and saying and being passionate. I wouldn't confuse passion doesn't mean that you have a bad work life balance. If you're working seven days a week and you're single and you're loving it and you're enjoying this and you're still having good mental health, that's not necessarily a bad thing, in my opinion. As long again, you need to have the mental health aspects. You don't need to be stressed. You need to be developing relationships, interpersonal relationships with people. But provided that you're doing those things, I don't think you need to apologize for liking to do what you're doing in work. And hopefully you can find something that you like. Yeah. So another thing that a lot of people in the SPA community, Students and Postdocs Association, would like to know from somebody that's sort of seen academia and industry up close is what advice do you have for choosing a career in one or the other? This is back to the work life balance. I'm a weird person. I say this, but who knows? I'm weird in that I would like to think, and I'm sure I'm not completely true, that I haven't made decisions for money. Now granted, it's always the poor postdocs like, what? Forget that. But what I'd say about academia and industry and all those kind of decisions is goes back to this passion piece. The Genesis question you're asking really is how do you choose between and how do you make these decisions? I would make decisions first of all back to the Genesis. What am I going to wake up every day and like doing? Because if you do that, you'll be successful. And if you're successful, other things will come. Freedom to do what you'd like, financial rewards. It's really hard. I'd love to know people who are successful in those areas, but hate what they're doing. Try to figure that out. That's what grad school is good at. I worked at Intel for four summers as an undergrad. I worked at a place called Cypress Semiconductor as a grad student. I worked at a place called Xilinx. They make something called a field programmable gate array. I realized I did not like working. I did not like working at a large company in Silicon Valley. I didn't like the hierarchy. I didn't like the relationship between product teams and projects and research. So you can figure that out in grad school. Do you like working on big teams, small teams? I like seeing my PI every day. I don't like seeing them. Figure that out and then figure out what environment is going to make you the most happy. Then hopefully you'll be successful. I know it is tempting to jump into something very established, but I feel for the most part, you guys will all be your own judge of this, that you are in the position to take some risks. I think like I took a big risk switching fields as a postdoc. So this might be the time to try a new field, try a new time. You don't have, maybe you do, but you probably don't have the six kids and the mortgage and the elderly parents that need care. So this is a time where you have some freedom in your personal life to be a little bit more risky, exploratory. Because you can always fall back on, I mean, you can typically, unless you burn a ton of bridges or forget everything you ever learned, you can typically go back to what you were doing, but it is hard to switch as you become older. At least it takes more conviction to switch as you're older and there are more things at stake with your surrounding life. And also your credibility is not as good often. So take some risks, find what you like. I wouldn't take the first offer, have multiple offers. At some point the money will be kind of numb. Just saying that my personal opinion, that's again, at some point I won't be like, I'm making 10k more at this job I hate than the other job. That 10k is not going to make you feel better. That 20k, that 30k, that 50k, that 100k won't make you feel better. I mean, it's easier said, I mean, you might not realize that now, but that won't make you undepressed. Like if that did, then we'd have no depressed rock stars or famous actors or whatever. I mean, at some point after that settles in, that doesn't fix your underlying unhappiness. Try to make those decisions wisely. And again, establish some people you really trust. That's another thing is to have some mentors in your life, people you can run ideas by. And if you don't have those folks, it's kind of like, I never did online dating, so I don't really know. I feel like most of my relationships are relatively organic. So I think ideally these mentorship relationships will happen organically, but if they don't, like if you're having trouble, you might have to switch to the quote unquote online dating method. And I'm sure places like the EBRC and other things can set up mentorship networks that you can start meeting people and doing that. Because I think eventually those relationships are really key. Yeah. And actually EBRC, SPA, we put on a mentorship program exactly for this purpose. So any student in postdoc listening that's interested, check out our website and sign up if you're interested. Well, thank you for the valuable advice. Speaking of taking risks and trying new fields, what areas of biology are you interested in learning more or getting into in the future? So could you share your perspective of them in future synthetic biology? My big push right now is for something I'm calling hardware, software, wetware co-design. And so in embed electronics, you write a specification and that gets turned into hardware and software to the extent that it's heavily biased towards one or the other, depends on the application. I'm interested in doing the same thing that a spec becomes DNA, microfluidics, and software to control it. So hardware, software, and wetware. So what I'm interested in is what makes that process most possible. And I suspect, I'm not 100% sure, is that cell free systems might make that easier because now hopefully I know they can be, they have their own set of complexities and difficulties, but losing some of the cellular context might make that system more controllable in a microfluidic environment provided we can give some precise control. So I'm interested in cell-free systems. I'm interested in circuits that can interface with electronics, either because they produce a luminescence or a fluorescence or a free electron or something that we can end up measuring. So I'm interested in cell-free systems, interfacing with bioelectronics. I think then we can leverage all the electronics infrastructure. I'm not going to be a therapeutics person. I'm not that, I don't have that kind of background. I'm not going to be a bioenergy person. So I still am really interested in biosensors and bioremediation, I think is interesting. I'm ultimately still interested in what that Living Computing Project is still kind of set out to do is to figure out what paradigms are really interesting. I don't ever worry about my computer dying. I worry about it dying like it breaks, but I don't worry about it dying the same way we were about biology dying, like a bacteria dying, or we don't worry about, I don't worry about my computer replicating. But those are fundamental things that happen in biology that I feel like there's a really powerful computing paradigm around. Imagine if I entered an instruction into a computer and it had a lifetime and then it replicated itself and executed on a parallel piece of hardware. Like that's another entirely different way of thinking about computing. And I think, you know, if you go back to like Boolean algebra comes from a guy named George Boole, who developed a system of reasoning in the 1800s. He didn't know anything about computers, but he developed a series of reasoning. And then what people having people came up computing, they said, we can leverage that. I think now early pioneers in synthetic biology will come up with a way of reasoning about computing in biology that won't be relevant now. But as we learn more about biology, like in 40, 50 years, 60 years, 100 years, we'll go back and say, oh, you know, it's the Ross method of computing. And like he made this in 2020, like 2024. He died, you know, in the insane asylum after too many EBRC podcasts or something. You will have moved on, but people will look back and say those like, we don't need to wait on some of the theoretical frameworks that we could come up with for computing and biology. And that's what excites me. And again, I can do that on the white board. So that's something again, that's something that's probably a weird perspective from someone like me that you won't get from a lot of other speakers is a lot of folks for the reason they do the research, which is fine. We'll rule out things if they can't realize it in the lab. Like if they can't do it in the lab, it's probably something they're not that interested in right now. Whereas I'm someone who has no lab mandate. Like I could do something completely on a white board and I feel like I could make a contribution. And so I'd like to do more of that. Like what is just more thinking about the space? Yeah, that's, that's all really fascinating. I guess we'll wrap it up here. We're just wondering if there's any other things you'd like to promote, for example, any DEI efforts like that outreach program you were talking about before spun out from the living computing project or any research openings, papers, books you'd like to promote. Are there show notes and things that come out of this? Yeah, we can add things to the episode description. Okay. I'll give some links to programming biology effort, which now is linked to something called semi-symbio, which integrates semiconductors and synthetic biology. So there'll be a link to this programming biology. There'll be a link to STEM pathways. I'm hiring in my group for a computational person, like a programmer or a senior postdoc. I will put links to the companies that companies are hiring, Asimov's hiring, Lattice is hiring, BioSensei will be hiring. I'm sure in 2022 we've had some initial funding, big initiatives. I'll put a link to the international workshop on biotech automation. International workshop on biotech automation. I think this was the first official announcement will be happening in Paris in October at the same kind of time iGEM is happening. So if you guys are following iGEM, iGEM is happening in October in Paris. We'll be there as well. That's a workshop I've run since 2009. I think that's probably the thing is I'm pretty self-contained now. I've got my own little set of projects. Yeah. Yeah. Yeah. That sounds fantastic. Thank you. All right. This has been another episode of EBRC in Translation, a production of the Engineering Biology Research Consortium's Student and Postdoc Association. For more information about EBRC, visit our website at ebrc.org. If you're a student or postdoc and want to get involved with the EBRC Student and Postdoc Association, you can find our membership application linked in the episode description. A big thank you to the entire EBRC Spa podcast team, Catherine Brink, Fatima Enam, Andrew Hunt, Kevin Reed, me, Koke C, and David Mai. Thanks also to EBRC for their support and to you, our listeners, for tuning in. We look forward to sharing our next episode with you soon.