Michael Sheets (00:03):
Hi, Mary. It’s so great to have you here today. So this is a BioBuilder career conversation, so it would be great if you could introduce yourself, and just where you work and a little bit about what your lab does.
Mary Dunlop (00:14):
Great. Thank you, Michael. I’m, I’m really excited to be here. So I am a professor at Boston University in the biomedical engineering department. And, Michael is actually a PhD student in my research group. So it’s, ery fun to have him, leading this conversation today. And my lab works on synthetic biology and systems biology, and we focus on work in bacteria. And we use engineering tools to study, some medical problems, things like antibiotic resistance, but then also to do some metabolic engineering work. And we’ve done things related to biofuel production and other chemical synthesis techniques.
Michael Sheets (00:57):
Yeah. That’s awesome. That’s really cool work. And I mean, obviously I think is being in the lab, but it’s really great to always hear the broader picture about the lab directors. Do you have any recent developments or successes from the lab that you’d like to share?
Mary Dunlop (01:11):
Sure. So I, you know, some of this is related to, to work that you’ve been doing, so you’re familiar with it. But just for the broader audience here, I can talk a little bit about some of the work that we’re doing on optogenetic control of gene expression and cells. And so optogenetics is this really cool technology that I started working on about sort five or six years ago around when you joined the lab. And I, we hadn’t really worked on it in my lab before that at all, but I became very excited about it. So the opto part of it means light, and then genetics of course is controlling gene expression. And so the idea here is that, using light you can, can control when genes turn on or off.
Mary Dunlop (01:54):
And so, in a lot of synthetic biology applications you work with things like chemical signals and you can add a chemical to cells and then make a change. But, I was really attracted by this idea that we could use light to control when genes turned on and off because light has a couple of key features. It’s highly programmable. Like, you can use LEDs or things that you might program with something like an arduino to turn on and off light really easily so that there’s this good temporal control. It’s also something you can control spatially. So you can shine light on particular regions, which means you can illuminate individual bacteria or patches of cells in a particular region. And so, it’s also pretty inexpensive to work with. So, people have used it for bioproduction applications as well, because rather than relying on, you know, chemicals you can just work with standard light sources as well. And so we’ve been very excited about being able to interface with cells and, kind of communicate with them, using, using optogenetic, light based approaches for controlling when we turn on and off gene expression. And so, we have a handful of recent projects in the group related to controlling the timing of when genes turn on and off, but also controlling genes involved in antibiotic resistance that I find really exciting. Yeah.
Michael Sheets (03:18):
Yeah. That’s so great. And it’s really cool to hear about the idea of communicating with cells and being able to talk to cells sort of directly using light and using computers with this, and I think this touches really nicely on another area of sort of research and education that you’ve been working on, which is machine learning and synthetic biology and how you have this, really great course that you just developed. And these online resources with the Engineering Biology Research Consortium, that that go over machine learning and synthetic biology Would you mind just briefly talking about sort of what got your interest in that, and what the process of making that resource was like?
Mary Dunlop (03:53):
Yeah, sure. So I, was on sabbatical last year. So sabbaticals is just like amazing experience that really only exists in a few select areas. And academic research is one of them where, I was able to take,a full year off from teaching and I actually went to a different place. I went to London for the year and worked at Imperial College. As part of that, I, you know, kept running my research lab and meeting with students, but I also in exchange for the time I spent teaching and doing some other service based obligations for the, local department, what I did when I was there was I really took a lot of time to dive into learning about kind of machine learning and AI and how it relates to synthetic biology.
Mary Dunlop (04:38):
And, this is an area that I did not know a lot about before because I, you know, first of all, it’s a relatively new field. So when, when I went to college and then later graduate school, like, these courses didn’t exist. And in the past, say sort of five years, there’s been a huge change in terms of what people can do, and the types of things you could do with image processing or recognizing, you know, converting language and speech, translating it to different languages. You know, there, there has just been this like amazing kind of, revolution in what has happened in artificial intelligence and machine learning and people have started to apply that to synthetic biology applications. So I was curious to learn more about that. We have a few people in our research group who were starting to kind of dabble in this space too, and I wanted to take some time to learn about how we could interface this with synthetic biology.
Mary Dunlop (05:36):
And so I took actually just a handful of courses online myself to learn about some of these new techniques. I did a lot of reading of research papers and I talked with collaborators at Imperial College in London. And then, as you mentioned, I developed this, education module on machine learning and synthetic biology. And part of the inspiration for that was just like, if I’m gonna be learning this, I may as well like, interact with other people and then provide a resource where like other people can, can benefit from, from like, the way that I’ve been learning it. And so, this is through available through the EBRC, the Engineering Biology Research Consortium website. But I, I talked to six different researchers about specific projects in their groups that they’d worked on, and we kind of went through like what they did in those studies and some of the scientific results and the different machine learning methods they had tried and why they chose those over other approaches. And then I also did conversations with them about like how they got started in the field, because I think a lot of people from synthetic biology like aren’t necessarily thinking about machine learning and how they would even, you know, begin that process. And so I had these conversations with, with a bunch of researchers in the field where we talk about what they did and also advice they might have for somebody who was just getting started today for thinking about how to interface synthetic biology and machine learning.
Michael Sheets (07:07):
That sounds like such a, such a great opportunity and such a great resource. I’d highly recommend checking out to anyone watching this who might be interested in the intersection of machine learning or AI and synthetic biology, and what a great opportunity to be able to take a sabbatical and take a year to do that. Yeah. Sort of touching on that sort of, this is a somewhat unique opportunity and, and really, is part of the academic life cycle, growing up, did you always know you wanted to be a faculty member? Or how, what sort of led you to that career path?
Mary Dunlop (07:38):
Yeah, that’s a great question. No, no, I didn’t. Definitely not. And I think I, I’ve actually had like, you know, I’ve, come to this sort of like later than I think maybe many people did. In high school, I definitely didn’t know what I wanted to do. I mean, I was kind of good at math and science, but I liked other things too. And I did sports and then I, kind of by chance ended up deciding to do mechanical engineering in college. And I picked that for a few different reasons. I mean, I figured like I liked, you know, as I was getting to senior year of high school, I was really enjoying my like, kind of physics classes and math. And it seemed like engineering would be like a thing that might use some of those ideas.
Mary Dunlop (08:21):
This is really like the level of sophistication that I had at the time. And then I signed up for the engineering program and I had to pick between like, you know, electrical and chemical and mechanical and I, I think I went to like a poster session and I met some sophomores that there were a couple like women there in the program and they seemed cool. So I was like, Yeah, maybe mechanical engineering is the right one to do. You know, I sort of picked it based on like, you know, I can see people ahead of me who seem like they’re doing interesting things and I can, I could imagine myself in that role. And so I did actually mechanical, It was a combined degree in mechanical and aerospace engineering, and I did that all the way through.
Mary Dunlop (09:02):
And I also minored in computer science along the way, which is something I hadn’t done any programming until I got to college. But I ended up really enjoying it when I was there. And so you’ll, you’ll notice that conspicuously absent from any of this discussion is with like biology or professor. And so I didn’t really do anything biological at all, all all through college because I was doing this mechanical and aerospace engineering degree. And then I went to graduate school for also mechanical engineering. And I had done some undergrad research in my junior and senior year on like kind of fluid dynamics and was thinking I would do that. And so I went to graduate school. I don’t really know kind of what, what I thought I was going to, you know, do in terms of a job, but it, you know, it seemed like there would be engineering jobs out there.
Mary Dunlop (09:54):
You know, it was maybe considering also national labs in addition to like places where you could do, do some research or, you know, some practical things. But then in graduate school I started working on a project. First I was doing fluid dynamics related to biological problems, and then I kind of switched to doing more synthetic biology and kind of control system stuff in, in graduate school. So pretty, pretty late on. Which I think maybe just means that like sometimes people get really scared that they don’t have everything kind of perfectly mapped out. And my experience has been there’s actually quite a lot of flexibility and that you’re not really kind of set in stone in what you need to do for quite a while and you can make pretty dramatic changes, which I think is kind of reassuring.
Mary Dunlop (10:40):
And so, when I got to graduate school, I eventually switched research directions to more of the synthetic biology space, but I would say it wasn’t until like the very end of graduate school that I was even considering faculty positions. And there was a, a postdoc who’s like, so somebody in this sort of very senior role who had finished a PhD and then was doing more research and and he said to me something like, you know, I I you should apply for faculty jobs, like, I think you’d be a good professor. And I was like, like, it never even crossed my mind, and I don’t, I don’t know why it hadn’t crossed my mind. Like, I guess I didn’t really feel necessarily like I was, I don’t know, some combination of like as smart as or as good at research as, or like as yeah, I just didn’t feel like a faculty member and like in some ways I still don’t.
Mary Dunlop (11:32):
But I think, you know, that could be, it could be a gender thing where like there weren’t that many female faculty members in mechanical engineering. And so it didn’t really like even sort of seem like the sort of role I could see myself in, or it could just be like that it wasn’t particularly on my radar screen, but in the end it’s actually been a really good fit in that over time, time I have learned that I like teaching a lot and working with students is really fun and I like the sort of independence and self-directed nature of running a research lab. So I think the fact of the matter is, I, I would be happy in a lot of different jobs but I do think that that being a professor has been, has been fantastic. But that’s a long answer, but no, I, I never thought I would be a professor. Yeah.
Michael Sheets (12:17):
Well, that was a fantastic answer. Thank you so much. And I am very glad that you found your way to a professor role because you have been a fantastic advisor for my PhD, so thank you for that. Yeah, and I think that that’s such a great example for students too, if you don’t need to know in high school and college, even at sort of grad school really what you want to do, and there’s always time to, to change and find new opportunities as you go.And thank you for providing a role model for future students and professors and people in all careers took up to. I think with that, I may wrap up with one last question, which also segues well from that, which is do you have any advice to your past self or current students that might be interested in the field?
Mary Dunlop (12:56):
Yeah, interesting. Past self. So I think one thing that’s been sort of surprising to me over, over time is that with research and faculty careers, but just sort of in general, just being like you don’t necessarily have to be like the absolute sort of smartest person in the room, but I think being, having like a sort of level of persistence and willingness to just kind of keep working at something and not be put off when it’s not going well, but just sort of to come back and take a new angle and, and take some time and sort of slowly and methodically work through things has been really valuable. And I think it’s, you know, it’s not terribly like glamorous, but I think that the one thing that I’ve been perhaps surprised about is just the, the, the, the extent to which like just being persistent and being willing to like kind of put in the time and put in the effort, it actually goes a really long way.
Mary Dunlop (14:00):
You know, I think about it in the context of like maybe applying for a fellowship that you don’t think you will get. Like a lot of people have that, that kind of mindset or, a fellowship or a scholarship or something like that. A lot of people have that mindset. And then if many of the people just sort of eliminate themselves from the running by not just putting in the effort and trying and so that’s kind of one example, but with research as well, like just sort of methodically trying things and then they kind of ruminate in the back of your head for a while and then you suddenly hear a talk or happen upon a paper that does something similar and then that, that leads to some breakthrough. And so I think the sort of slow and steady effort, it doesn’t necessarily translate to slow and steady progress. Like my experience has been that the research progress tends to come and like fits and starts. Like things will go nowhere for quite a while and then all of a sudden, like everything will kind of come together. And then it’ll go nowhere for a while and then it’ll come together. But I think that behind that is a really sort of steady willingness to kind of keep, keep working at things. And I don’t think I appreciated that when I was first starting to work on research.
Michael Sheets (15:13):
Awesome. What a great note to end on and thank you again so much for being here today. This has been a really great conversation and we’re, we’re very happy to have you here.
Mary Dunlop (15:20):
Great. Thank you, Michael.