The Bioinformatics CRO Podcast

Episode 15 with Trevor Martin

We talk with Trevor Martin, co-founder and CEO of Mammoth Biosciences, about using CRISPR technology to detect SARS-CoV-2, his experience as a new CEO, and science education.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen onSpotify, Apple Podcasts, Google Podcasts, Amazon, and Pandora.

Trevor is co-founder and CEO of Mammoth Biosciences, discovering novel CRISPR systems that enable new possibilities for expanding biology. For example, using CRISPR as a COVID-19 diagnostic test. He has previously been honored as Forbes 40-under-40. 

Transcript of Episode 15: Trevor Martin

Grant: Welcome to The Bioinformatics CRO Podcast. I’m your host Grant Belgard and joining me today is Trevor Martin. Trevor, can you introduce yourself, please?

Trevor: Yeah, thanks for having me. My name is Trevor. I’m one of the co-founders of a company called Mammoth Biosciences and the CEO. 

Grant: Great. Tell us about Mammoth.

Trevor: Yeah, it was founded about three years ago. We were spun out from Jennifer Doudna’s lab at the University of California, Berkeley. And the other co-founders of the company include Jennifer Doudna herself and two former graduate students from her lab, Janice Chen and Lucas Harrington.

And the catalyst for the formation of the company was a couple of things. So first and foremost, the invention in Jennifer’s lab of this new field of CRISPR based diagnostics, that’s very exciting. It’s really come into its own over the course of the pandemic. And also this recognition that the way we were able to invent that field and the way that we’re driving forward a lot of things across CRISPR is through the development and commercialization of new proteins that go beyond the kind of standard CRISPR systems that most people think about, like Cas9, for example, which is maybe the most famous. 

So we actually work with CRISPR proteins that are coming from just totally different families, like Cas14 and things like CasΦ. And these have really exciting properties fundamentally, that enable new types of products that wouldn’t be possible working with the original Cas systems.

Grant: What kinds of properties?

Trevor: Yeah. So taking CRISPR based diagnostics. As an example, when you’re doing CRISPR editing, the main thing you’re concerned about is what you could call cis cleavage. So that’s the cutting of whatever target sequence you’ve programmed the CRISPR protein to bind to. And for those that aren’t familiar with CRISPR proteins, they’re kind of these programmable molecular machines, where by giving it what’s called a guide RNA, you can tell it to go to a certain region of a genome and to bind there. And then things like Cas9 come with a pair of scissors that can then cut that DNA. And through the process of repairing that cut, it’s a classic way of introducing some sort of edit that you would like to have.  

When you’re thinking about CRISPR based diagnostics, it’s not terribly useful to edit the sequence that you’ve targeted. So what you’d rather do is program the guide RNA. To be specific to some sort of disease, like for example, COVID-19, that you’re trying to detect. And then instead of editing that disease, you’d rather somehow have the CRISPR protein read out a signal or amplify signal that it successfully found its target. 

A property that many of our new proteins have that enables that is what’s called trans collateral cleavage or you can kind of think of it as just a molecular shredder. Where if, and only if it successfully binds its target, not only does it cut the target cis, so cutting the thing it’s bound to, but it also will then cut all sorts of other molecules in the solution. So you go from binding a single molecule to cutting many more molecules, kind of in this molecular shredder functionality. And that means that you can read out an amplified signal. So for example, if you’re doing some sort of RNA diagnostics, then it binds to an RNA that’s maybe specific to an infectious disease, and then you have other RNA marker signal molecules in the solution that are then cut and release some sort of color floressence. And that can be a way of amplifying a molecular diagnostic signal in a way that’s not possible with Cas9.

Grant: Can you tell us about things like the length of the guide RNA, how much specificity you might have to something like SARS-CoV2, and what concerns you might have about new variants arising and how easy is it to adapt to those?

Trevor: Yeah. One of the advantages of the CRISPR diagnostic system is that it’s very adaptable. So at the start of the pandemic, one of our really great scientific advisors, Dr. Charles Chiu saw this going on and we were able to put together some really exciting work very quickly within a matter of weeks, showing that CRISPR based diagnostics can actually detect COVID-19.

And we published that as a white paper and then eventually into a pretty seminal paper, CRISPR-based Diagnostics in Nature Biotechnology, where we showed it in real patient samples. And we were one of the first groups to do that. And I think in terms of sensitivity and specificity, starting with the specificity, that’s really quite exquisite for these CRISPR based diagnostics systems. Even a single base pair, you can design it to very easily distinguish between different alleles at that spot. 

And in terms of the sensitivity, you can get really high sensitivity, both from the CRISPR amplification itself, and then combining that even with other isothermal techniques and the same or different reactions to get sensitivity beyond what even PCR can achieve.

And in terms of additional variants, one really exciting thing about CRISPR based diagnostics is due to the simplicity of how you can target different things, you can actually also multiplexer very easily. So you can either choose to target regions that maybe are less prone to variants and are very constant, or you could actually target varying areas specifically.

So you could differentiate between them and actually identify what variant is being detected. Or you can do a combination of the two by actually having multiple guides for different variants in the same reaction so that any of the variants could actually activate the detection. It really gets you a lot of optionality basically, on which of those approaches you want to take.

Grant: How do you see this fitting into the menagerie of other molecular diagnostics? 

Trevor: So right now there’s kind of this choice in diagnostics about, do you want like a super accurate result? Something like PCR, like molecular style, or do you want something that is very accessible and very easy to use? Simpler, but maybe it would lower sensitivity and specificity? 

And it’s kind of this trade-off between the two. I think one of the promises of CRISPR based diagnostics is removing that trade-off to a large extent. So being able to have something that is a molecular-style result, but it in a much simpler reaction, a very multiplexable reaction, something that’s very accessible and easy to use and kind of getting rid of that dichotomy that diagnostics has existed in for many decades.

Grant: And what do you think is the biggest challenge to getting this out in use? 

Trevor: With the pandemic it’s been really exciting to see that already Mammoth has gotten emergency use authorizations for the technology for detecting COVID-19. So before maybe I would have said that one of the big hurdles is that it’s a brand new molecular technique and those take time to come to market.

Helping out with the pandemic has been a great thing, both for CRISPR diagnostics itself, but also obviously for just helping play a role in combating the pandemic. I think in general, the main things we think about right now are scaling all the different formats that it can go into. There’s just so many different opportunities. So which ones to prioritize. 

In general, I think it’s kind of cool just looking back.  Obviously the first things people thought about with CRISPR were things like CRISPR based therapeutics and editing, and there’s a lot of exciting things going on there including at Mammoth, but it’s pretty exciting to see that actually the first commercial uses where people are actually interacting with CRISPR were on the CRISPR diagnostic side. Even though that’s a much more recent invention. 

Grant: What do you see as the most exciting opportunities?

Trevor: There’s a couple. So I think what’s interesting about CRISPR based diagnostics is that it is a new way of doing molecular detection and one of the first new ways in many decades. Essentially, there haven’t been many of these new techniques that have come out over time. So I think that ‘s cool.

There are a ton of different formats that it can go into, and it can have a role all the way from like central labs. So increasing the throughput of central labs and the testing that can be done in there. For example, like we have our boost product launching, and I think that there can play a big role in like reducing the wait times and like really increasing the accessibility of testing by having higher throughput and really enabling all the labs around the country, not just the large labs to really do serious amounts of molecular COVID testing.

And then on the other end of the spectrum, you have the radical decentralization of testing. And I think that’s a trend that existed before, but COVID has now accelerated it by many years. Cause it’s really shown how critical that can be. So thinking long-term I think it’s really exciting to imagine that the next time there’s COVID-2023 or something–hopefully not, but unfortunately it’s probably a matter of when not if– instead of having that become a global pandemic, what if you had millions of tests in warehouse that could be very quickly reprogrammed with a new CRISPR guide RNA to then go after this disease and actually test and trace and contain at an earlier point. So thinking about things like that is pretty exciting. 

Grant: I think the importance of that is underlined by actually the last podcast we recorded just before yours was with a few COVID experts. It emphasizes the importance of rapid and scalable and precise diagnostics. 

Trevor: Yeah, definitely reliability is a key as well because if you’re testing a lot of people, but you’re not confident in the result, then it can be tricky to understand what to do with that information.

Grant: Right. So can you tell us a bit about the details of the origin? How did the discussions around Mammoth begin and you were, I think finishing up your PhD, right? 

Trevor: Yeah. 

Grant: Yeah. That’s really incredible. Can you tell us about how that came about? 

Trevor: Sure. My PhD is a bit more on the computational biology side, actually rewinding the clock a little bit more. I originally got interested in computational biology at Princeton when I took a program that had been started by David Botstein, basically trick mathematicians and physicists and computer scientists to become hardcore biologists. 

And I started the program on a whim, but I really fell in love with the concept and biology in general, and definitely a different way of thinking about biology than I’d been exposed to previously where it can often be thought of as more memorization or a little bit squishier. That’s how I got introduced to computational biology.

And in general, I think what attracted me to that is thinking about life as something that’s kind of programmable in some sense, and has rules. And it’s something that it’s like very tractable and very difficult, obviously, to understand fully, but it’s not magic. And there are fundamental principles and algorithms that can be applied.

Towards the end of my PhD. I think one thing that’s really cool is that computational biology has infiltrated all areas of biology. And I think now it’s almost silly to call something computational biology differentially because everything is computational, which is great. I think that’s a sign of the success of the field.

That it’s just a fundamental tool in your toolbox now for doing any sort of biology. A consequence of that as well, though, as I started thinking about what are other fields that are about to undergo that type of transformation in biology, where taking something that’s like kind of a new concept, but it’s going to become fundamental to everything that’s going on.

And I immediately started thinking about synthetic biology and like all these new tools, like CRISPR that are coming out in terms of really pushing the envelope of how we think about–in this case a little bit more literally–programming biology, synthetically.

Had some initial ideas around different cool tools for this and in general, I’m a big fan also of this idea of there being power in the intersection of fields and especially like fields that maybe have seen less innovation: diagnostics, for example. I was thinking a lot about the intersection of synthetic biology and diagnostics and had some initial ideas for stuff in that space, but not exactly a trained synthetic biologist through my PhD directly.

And around the time these thoughts were swirling around in my head, Jennifer’s lab was pioneering this kind of new field of CRISPR based diagnostics and Janice and Lucas were at the forefront of this. And I saw the papers being published and pretty much thought, okay, wow, this is way better than any of the things I’ve been thinking about and is exactly a fit for this thesis around just really the transformative power of synthetic biology and these fields that are maybe a bit underappreciated.

So I reached out to Jennifer and the team. And I’d like to say that we just immediately started the next day, but of course spent some time getting to know each other and found that we really shared this thesis around where the field was headed and how transformational this could really be in diagnostics, but also in thinking about next generation therapeutics and just next generation biology overall. And then decided that we had a really exciting opportunity to start Mammoth together and build this platform for what’s next and CRISPR and synthetic biology broadly.

Grant: And what were the early months like? 

Trevor: One thing that I really appreciated about the Bay area ecosystem–and I don’t think I understood fully going into it, coming just from a pure academic career–is that there are a lot of people and institutions that are extremely helpful. And there’s just like an ecosystem that is really well-designed for thinking about big ideas like this and getting them off the ground. So on the one hand, there’s lots of great advisors that we connected with early on, who just shared advice for free and were really helpful in terms of thinking through how we’re setting things up, thinking about the future of the company.

Then also just more practically being able to start doing experiments really quickly by getting like a single lab bench in a building that has single-lab benches for tons and tons of companies. I think those are really exciting times for sure at the beginning of the company because there’s just all these things to figure out, but it’s all something that you can tackle and there’s no one telling you to do it one way or the other, which of course can be scary as well, but I think it’s also very liberating and exciting. 

On top of that, I think the excitement more broadly for these new approaches to biology is something that truly is at the start of an inflection point. And that’s always an exciting place to be. There’s been a ton of progress. And now we have emergency use authorizations for CRISPR based diagnostics and things like that. But I still think we’re just at the very first part of the infection, even with all that progress that’s been made. So that’s just something that makes it a really interesting area to work in.

It’s always exciting to be somewhere with so much potential in terms of just the field. I think the other thing that happens in the first few months is that there are some things that your PhD training really does prepare you for. And I think one of those that’s maybe under-appreciated is dealing with uncertainty.

Like when you’re doing a PhD, there’s no right answer. Obviously there’s no textbook. That’s the whole reason you’re getting the diploma. You’ve hopefully expanded the knowledge of the field. And startups actually have a lot of parallels to that in terms of there’s no right answer, right.

Otherwise someone else would have just done it or there wouldn’t be any reason to form a startup around it. So I think dealing with that uncertainty over long timescales is a super great asset and is under-appreciated. On the other hand, I think somewhere that maybe you’re a bit woefully under-prepared both for academia and startups is hiring people, managing people. That’s definitely somewhere that you have to have a lot of growth very quickly especially in the early days, but also every single day at a startup, even after you have thousands of people. 

Every single hire is going to define what your startup is and where it’s going. So it’s one of the most critical decisions that you make day to day. So I think that’s somewhere that has a super steep learning curve in those first few months

Grant: Interesting. So what areas of biotech other than CRISPR based diagnostics are you most excited about?

Trevor: Yeah, there’s a lot. Some of the ones that are talked about more that I think are interesting are things like the data storage side. That’s a little bit directly related to synthetic biology because being able to read and write is greatly influenced by those tools. 

I think more generally something that’s a little bit, maybe less visible that I’ve seen a lot of and is pretty exciting is non-drug or molecular approaches to therapeutics. Whether that’s sound-based or diet based or other things, I think that there’s a lot of really interesting work going on there. And I think that it is going to require even more exquisite on some level understanding of what’s going on to really understand mechanisms and have better hypotheses about what works and doesn’t work there. But I think as we develop that really great foundational molecular understanding, it does allow a lot of opportunity to take it to the next level. 

The simple answer is I think even fields like CRISPR that obviously have been in the public eye for several years now are just at the start of the inflection points. It seems like something that has already made so much progress because it has, but I think it’s still not even at the 20th percentile of its potential in terms of where it’s headed over the next 10 years. 

Grant: So, what would you say are the most important things you’ve learned since you started the Mammoth journey?

Trevor: I think the first one would definitely be going back to managing people. You do things like collaborations in academia, and obviously you work closely with other people, but I think something that’s underappreciated is how much a startup can align people around a common goal.

And that can be a really exciting thing. I think there’s a lot of stuff to be said around having that shared vision and actually bringing something into the world as well. It’s something that I appreciate a lot. So, whereas in academia you might publish a paper and hopefully it gets cited many times and drives the field forward. That’s definitely rewarding.

But for me personally, there’s something an order of magnitude more rewarding about having a product that actually then goes and helps someone directly rather than always being a few steps away from that. And I think that that’s something pretty enriching about startups in particular that maybe I didn’t fully appreciate at the beginning.

But yeah, I think in general all the learning about working with people and recruiting people and managing people, that’s been some of the most rewarding stuff and interesting stuff that has been a part of the Mammoth journey so far. You’re exposed to a lot of different ways of thinking. And it’s also just critical to the business, so you’re kind of forced to learn a lot about it.

Grant: What advice would you have for first time CEOs? 

Trevor: The main advice I would have is to not be too wrapped up in knowing everything. And it goes back to the people as well, but like trust is the critical element. Because, in my opinion, the only way you’ll scale into a successful company or as an individual is by relying on others that you trust. Because yeah, you can read every book you want and might try and become an expert in all these different areas. But you’ll never be able to do that fast enough to cover all the things you need to learn as quickly as you need to learn them, even if you’re just a perfect genius.

So the only way to scale yourself is to scale yourself with additional hires and people and the team. And the only way you’ll leverage them effectively so that they can actually help you scale is if you hire people that you trust and really empower them to do the things that you hired them to do, right? 

And worse is to hire someone that’s really awesome and then make all the decisions for them anyway. So I think that would be the number one piece of advice, because it can be a little bit counterintuitive because on the other hand, you also don’t want to seem like you know nothing and you want to convey some aura of authority. More fundamentally, a lot of that trust from the team in the other direction comes from the trust towards them. And I think that’s something that can get lost, especially for early stage founders. And that’s one of the most important transitions that happens as the team grows. 

Grant: What is something on which you disagree with most of your peers, and why are you right? 

Trevor: Yeah, that’s a classic Peter Thiel interview question. One thing that I’m a big believer in  right now is definitely a more controversial topic in the Bay area: whether Silicon Valley will remain the place that the next generation of startups are going to be born in and grow, or if now maybe accelerated by COVID-19 everything’s going to be decentralized and sort of all becomes more of a mindset than necessarily a place. In biology, it’s kind of funny because in many ways you can replace Silicon Valley with Boston actually for that comparison. But I think there’s some trivial reasons why that’s not true for biology, just in terms of like you need lab space and people to come into the lab. 

And maybe those are the less interesting reasons why I think that the San Francisco Bay area will remain the center of innovation. But the less obvious reasons would be around what I mentioned at the beginning around the ecosystem and how supportive it is. Like, man, these first few months, I can only imagine if I was not in the Bay area–or Boston for that matter–just how many orders of magnitude more difficult it would have been to get started. Like it’s just way harder. And so one result of that is maybe the deep tech startups stay in these certain hubs and maybe software truly can just be built anywhere.

I don’t know, like I am not familiar enough, but my gut intuition is that I do hope it does democratize access to building a startup more. Cause I think that’s important. I think the ideal would be that you could anywhere to start a company just as easily. That’s definitely the ideal in my mind, but I do believe that the reality is that places like the Bay area in Boston will continue to be where you kind of have to come to really build startups that are really ambitious and have a lot of capital behind them to really tackle audacious goals. 

Not just because that’s where the capital is–although that’s part of it as well–I think the barriers to that are lessening, but more because of the support networks around. Even from the small stuff, like the lab space, and the bigger stuff like where you’re going to recruit people. I think that that’s something that is just way more sticky than even a year of forced remote working can remove. 

And I think there could be things that disrupt that longterm. But this would be on like a 10, 20 year timescale and have nothing to do with the power of remote tools, but more to do with do other areas start to encourage innovation more than the Bay area? Or things like that. So, yeah, that’s my kind of thoughts on is Miami going to supplant San Francisco? I hope that Miami grows into an awesome tech hub, but I don’t think that’s necessarily at the expense of the Bay Area. 

Grant: So what can cities like Miami do to increase their biotech competitiveness? Why are we starting from pretty far behind? Maybe we can talk about Raleigh-Durham or something.

Trevor: Well, I think that’s a great example actually, because you are starting to see a lot more startup activity there. I think it’s a bit of a chicken and the egg problem. So one way to get around that is to build a pretty massive public investment and incubator space and lab space and increase access to capital and things like that.

I think that’s like table stakes. I think the harder part is the mindset, because I think something that’s really important and you see this even in little microcosms of the startup world is an example of success. And I think this is one thing that I hope Mammoth can contribute back long-term is an example of where scientific founders built a successful company coming out of PhDs and contributed back to the world in a really impactful way.

Because right now, the classic biotech model is that the scientists have some idea that gets passed off to maybe like a venture firm. And then they hire a bunch of people and go off and do it and deliver something of value to the world. But I think there’s this new model that Mammoth and other companies are pioneering, where you don’t have to pass it off to someone else and you can really kind of drive that vision forward yourselves.

And I think that can have huge advantages long-term for the vision of the company and where it’s headed and the innovation that happens after the technology has been transferred, that long-term beats the pants off transferring the technology to people that aren’t the ones that necessarily invented it. 

But I think Silicon Valley and the Bay area and other places have a huge advantage that people have seen others found companies that go on to be super successful. And it opens your imagination. Right? 

Like I grew up in Georgia and knew nothing about startups. I just wasn’t exposed to it all. I just had no understanding of it at all. I couldn’t even think about that as something that would be interesting to me. It wasn’t even on my radar, but then when you come to somewhere like Stanford, everyone is doing it for better or worse. It’s just like an accepted part of the paths you can take.

And that means you have more people trying it. And that means you’re going to see more examples of it and that’s just a self-reinforcing loop. So I think the trickiest part is getting places that are not the Bay area or Boston or the usual suspects. I think they need a few big wins and a few companies to really pioneer it.

So anything that can be done to support those companies and get them across the finish line. Cause then you’ve opened everyone’s imagination. They’re like, Oh yeah, those people from Duke. They founded that company that has a bunch of products that we use all the time. They were in my shoes 10 years ago. Why can’t I do that? And I think that’s the trickiest part and that’s the big advantage that the Bay area has over anywhere else is just that mindset somehow. 


Grant: So you touched on something that I think is pretty important and that we try to explore a bit on this podcast and that is inspiration. And what inspires people to do what they do. At what age did you know you wanted to be a scientist? 


Trevor: It’s an interesting question because I think early on in my life, I wanted to be a historian. I was obsessed with history and read anything I could get my hands on. It wasn’t until maybe late in high school, when I started to really understand and was introduced to the idea that you can build up a model of the world. Actually, in some ways, similar to history. The part of history I was most interested in was what can we learn from our mistakes? And how can we avoid repeating history. And science is like an ultimate form of that. By doing experiments, you build up a corpus of knowledge. You can actually predict the result of the next experiment you’re going to do and use that to build things that are really helpful to people.

So I think that’s kind of when I first started to get interested in science. But especially in high school, you can fall into the trap of thinking that physics is really the place to do that only because it’s just how these subjects are classically taught. 

Grant: That is a very recurring theme. I was the same way. I wasn’t that into biology until actually later in college. 

Trevor: Yeah, exactly. Because I think once you’re in college and you’re finally exposed to the immense amount of interesting research that’s going on across all fields, you can kind of realize that every field is like this and does experiments and can learn from them and predict what’s going to happen next. And use that to advance our knowledge and build cool things that help people. So then I think it was in college in the program that David set up that really solidified my interest in science and kind of how exciting it can be to push the limits of human knowledge in ways that are reproducible. And also that can be communicated and replicated and yeah that’s a pretty exciting concept in general. 

Grant: Do you have any thoughts on how we should change curriculum? How science is taught?

Trevor: In general, it’s a bit of a double-edged sword these days. Cause I think one of the coolest things you could do would be to introduce earlier that science doesn’t know everything and that there’s so much room for innovation. Instead of thinking about math and science as learning these rules, which obviously you have to do–to break the rules, you have to learn the rules–and I’m a big believer in you’ll be the best at breaking a rule once you fully understand it. And like every assumption that’s in it, et cetera.

So I think introducing that a bit earlier in people’s science education, like middle school. It’s not just like, Oh, there’s hypotheses and learn all this stuff that’s all fixed. Giving a bit more of a story around how we figured out that genetic information was coded in DNA and things like that. And maybe even letting people do that experiment, or if we wanted to take it further, what were the things that we thought we knew that were wrong? Like experiments that prove one thing, but maybe ended up being disproved later and actually doing those experiments as well. I think that would really open people’s minds to the possibility of like Oh, I can contribute to this on the other end.

There’s so much skepticism and conspiracies about science that it’s tricky. The strength of science is that it can withstand that stuff, but then are you just going to lead people down this weird path of like no trust in science. And I think that really comes down to how well that process is taught.

So I think if we want to do that and we want to have a really great science education that emphasizes that it’s a continuous process and it’s valuable and that there’s things that are wrong even today. I think that relies on investing way more in education than we do at the moment, or at least better allocation of resources. So it’s a tricky answer. 

Grant: So speaking of things we thought we knew, but we were wrong. What jumps out to you today as a candidate for that? So if you look at statistical genetics, for example, you can certainly look at a lot of the work that was done on candidate genes studies some years ago that mostly turned out to be BS. 

Trevor: Probably a million things would fall into that bucket 10 years from now, but I think maybe one of the biggest ones is. I think there’s still an under-appreciation for non-novel results. Just like replication of results. Towards that point because right now I definitely think the publishing standards are a little bit skewed towards new results, whether those are confirmed later or not.

So it’s just, all the incentives are aligned towards publishing a new candidate gene rather than replicating a previous candidate gene. And there’s reasons why that is helpful. Right. You want to be driving forward new knowledge, not just resting on the laurels. I mean often in this crisis of replication–that term is thrown around a lot. I don’t necessarily know if I’d use the word crisis–but I think something that would help uncover what we should be building on and what things maybe we have a few false assumptions on that could be fixed and then built on, would be if we change the incentive structures around replication and the rewards for validating results and things like that.

I hope that in 10 years we look back and we say, wow, what we’re even thinking? Like in terms of how we reward replication studies and things like that. Clearly that was the worst possible thing we could do. 

Grant: Do you have any, any final thoughts for our listeners? 

Trevor: Yeah, I think the main thing would be to emphasize to anyone that’s doing a PhD that there’s a lot of different options you can do after your PhD. Like going into academia, I have friends that have really rewarding careers down that path and really enjoy it. I know people have gone down that path and hated it as well. Same for any path. But in addition to industry, I think that there is this new wave and openness towards having people at the forefront of science also be at the forefront of business and is definitely not something that people should be shy about or think that they can’t do. 

Grant: That’s a good positive note to end on. Well, thanks for coming on Trevor. It was great. 

Trevor: Yeah. Thanks for having me.