Pod Insights a16z Podcast a16z Podcast | All About Synthetic Biology About this episode with James J. Collins, Vijay Pande (@vijaypande), and Hanne Tidnam (@omnivorousread)
The idea of 'designing biology' -- once science fiction -- has over the last 20 years become just... science. In this episode, a16z's General Partner on the Bio Fund Vijay Pande and Hanne Tidnam talk all about the field of synthetic biology with James J. Collins, Termeer Professor of Bioengineering at MIT. Collins, whose work in synthetic biology and systems biology pioneered the field, has also launched a number of companies and received numerous awards and honors, including a MacArthur "Genius" Award, an NIH Director's Pioneer Award, and Sanofi-Institut Pasteur Award.
This wide-ranging conversation about the birth of synthetic biology covers every thing from the founding story of the discipline to what “engineering and designing biology” really looks like in action -- when instead of engineering electrons, you are engineering toggle switches for genes -- to the disciplinary differences (and synergies) between how biologists and engineers see the world. What are the engineering and design principles, techniques, or approaches that work best when applied to science? How does building a company in this new space look different, in terms of platforms and products? And how is this new field changing education in science, all the way down to kits that allow you to play with the machinery of a cell... at home... in middle school?
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 Hi and welcome to the A16Z Podcast. I'm Hannah and in this episode, general partner on the bio fund Vijay Pandey and I talk all about the field of synthetic biology with Jim Collins, professor of bioengineering at MIT and one of the pioneers of the discipline of synthetic biology. We talk about what engineering and designing biology really looks like when instead of engineering electrons, you're engineering toggle switches for genes, including the scientific story behind the creation of that very first gene toggle on and off switch. We also talk about the disciplinary differences and synergies between how biologists and engineers see the world, what the engineering and design principles, techniques, or approaches that work best when applied to science, how that looks different when building a company
0:43 Speaker 00 in the space and thinking about, for example, platforms versus products, and even how it's changing education in the field all the way down to middle school. So let's start at the very beginning. How did the field first begin to emerge? What's the sort of founding story behind synbio?
0:57 Speaker 01 Yeah, you know, the very beginning really was the island of misfit toys. So if you go back to the 1990s, the dominant story was the genome effort. And what's interesting is the biomedical engineers did not play a major role in the genome effort. In biomedical engineering departments, the curriculum, the research interest more or less stopped at the tissue level and was only beginning to get interested in the cellular level. In the late 90s, however, as the genome effort was really picking up speed and beginning to produce these parts lists for different organisms, the leaders of the genome effort began turning to the engineers and increasingly to physicists to figure out, help them figure out how these parts put together.
1:41 Speaker 00 Can you explain what the missing information specifically was?
1:44 Speaker 01 So the sequencing would lead you to the ability to annotate the genome, to identify coding regions, identify the genes, identify promoters, but couldn't really give you information about underlying networks that were making up these living cells. So in what way were the genes, the proteins, the RNA elements interconnected and leading to the interesting biology and disease biology? The notion even of network was not dominant. Or even prominent within molecular biology. You said one thing that really struck me was you said it was not necessarily biologists, computer science and physicists. You then had this effort to really try to pull the physicists and the engineers into molecular biology. And why them? I think because we deal with complexity well. To get after what became systems biology was the recognition that you needed to embrace
2:29 Speaker 01 complexity.
2:30 Hanne Tidnam? Before the network, what was the predominant kind of idea?
2:34 Speaker 01 You had really the fixation on the actions of single protein, single genes. And at the level of kind of integrating those, you had pathways. And you began to see efforts, primarily out of Stanford, beginning to use mathematical modeling that opened up the doors for folks like me to do basically systems biology. Could you reverse engineer these networks?
2:55 Hanne Tidnam? And let's actually explain what you mean by that, reverse engineering a large-scale network, because that's not a concept that's native to traditional.
3:03 Speaker 01 It's an engineering concept. So it's a strong electrical engineering concept. So the notion is that you have a system that's interconnected or wired together. And so the example that could relate to many of the listeners would be the wiring system or the wiring setup of your house. Say you buy a new house and you've got a circuit breaker box down in the basement. And let's say it's not labeled by the previous owners. Just like my house. Just like my house. So what do you do? You go down and you flip the different switches on or off and you discover what they connect. So you're beginning to reverse engineer the circuitry of your house. You then, through additional experiments, you're running the microwave same time with your toaster that in fact, oh, those two circuits are actually interconnected even further that's not represented by that. And you begin to infer the underlying structure of the wiring diagram of your house.
3:48 Speaker 01 But our first response was to run away from that problem as fast as we could because there weren't sufficient data to do it. In the late 90s, we just had microarrays appear, technology that is no longer in vogue, but would allow you to survey thousands of genes and their expression states simultaneously within a given cell. These are incredibly expensive. And when I got pulled in, there were only seven publicly available data sets. So the idea you could reverse engineer a large-scale network was ludicrous.
4:13 Hanne Tidnam? And what were those types of data that were lacking?
4:15 Speaker 01 So there was large expression data set. And in those days, it was really microarrays. So we, even though the technology just appeared, we didn't have those data sets. We didn't have a wet lab. We didn't have the capability. And so we began to think about, could you put together? Molecular parts in a network. Could we instead take a tinkerer's approach to molecular biology?
4:33 Vijay Pande? So the expression is the output. What's the input? Like, how were you perturbating it then?
4:37 Speaker 01 Well, so on that side, we had no perturbation, right? Perturbation would be that you're going to kick the system or you're going to stimulate the system in some way.
4:45 Vijay Pande? Or flip the light switch.
4:46 Speaker 01 Flip the light switch on. Let's say you have your radio on in your kitchen upstairs with the volume on. You're going to run downstairs. You're going to flip the switches one by one until you hear the volume go off. That's a perturbation to your circuit that allows you to map out the circuit. We introduced techniques to do that. But even before we get there, we stumbled into what became synthetic biology. We said, OK, could we, instead of figuring out how these large-scale networks are wired together, could we put them together ourselves? Could we do it with intent? So could we design circuits with a desired architecture and a desired function?
5:20 Hanne Tidnam? And that's the key difference, right, in this entire approach? That you're designing.
5:25 Speaker 01 Exactly. We are really doing biology by design. In some cases, we're taking natural parts and putting them in new and different ways. In other cases, we're building new parts and then putting those together in new or even established architectures. We spent about nine months thinking about what we wanted to build. And we arrived at building a genetic toggle switch. And this was motivated by electrical engineering, where toggle switches, which are also called flip-flops or R-S latch, are very simple memory elements, memory switches that can be flipped between 0, 1 in the binary state or on, off by just a transient electrical signal. Toggle switches are at the heart of digital memory. And we cycled through many different schematics and circuit diagrams and arrived at one that was basically a mutually inhibitory network,
6:10 Speaker 01 where we have two genes that are set up so they always want to be on, but they're arranged so they're trying to shut each other off. And we showed mathematically and computationally that you should be able to have it as a bi-stable system. It either wants to exist in state A, gene 1 is on and gene 2 is off. Or state B, gene two is on and gene one is off. And in principle, you should be able to flip it between those states by translating, delivering a chemical or environmental stimulus that would shut off your currently active gene, allowing the gene that had been kept off by that active gene allowed to come on that now would produce enough protein that would shut off the gene that had been on. And you could remove your stimulus and you flip from state A to state B or state B to state A.
6:47 Vijay Pande? Like electrical engineering, except not electrons.
6:50 Speaker 01 It's now a wet circuit, but in this case, we actually did mathematical modeling computation. And now, the next segue was I reached out to Eric Eisenstadt, who was a program officer at the Office of Neighborhood Research, who was running a gene circuit modeling program. And I told him what we were doing and I more or less called him once a month for every for over about six months, said, could you give us money to try to build this in a lab? And he kept putting me off, putting me off. And I basically wore him down. I said, you know, I think this could be really big, but we need to be able to build this and, you know, I'd like this to be part of your program.
7:20 Hanne Tidnam? And was the reason why it wasn't clearly, I mean, clearly having an on/off switch like that seems incredibly powerful. Why was it not immediately, just because it seemed so hard to do?
7:29 Speaker 01 We had talked to about six or seven very serious molecular biologists who told us this couldn't happen, this couldn't work. They said, for it to work, you're going to have to have a very tight off state, two very tight off states. That's going to be impossible. You're going to have to introduce a very large plasmid. It's going to put a burden on the cell. The cell's not going to like this foreign element. The joke was they usually would pat us on the head and say, boy, stick to engineering because biology is really complicated.
7:52 Hanne Tidnam? I mean, those do sound like reasonable concerns.
7:54 Speaker 01 I had an incredibly talented graduate student, Tim Gardner, who was a mechanical engineering trained student out of Princeton. Tim presented our modeling, this was a famous ONR meeting, where he was more or less shouted off the stage by some very serious molecular biologists, including a future Nobel Prize winner, who said it was impossible. This could not work. And in part, Tim, who was green in molecular biology, had been a mechanical engineer who had designed autonomous helicopters to fly around Princeton, didn't know any molecular biology, presented this engineering concept. Nonetheless, in nine months, he had a functioning by-stable toggle switch. At the same time we were doing this, Michael Illawitz and Stan Liebler, who were two physicists sitting in both the physics department at Princeton and the molecular biology department at Princeton.
8:36 Speaker 01 We're taking a remarkably similar approach to synthetic gene circuits, completely independent of us and unbeknownst to us. In that we worked in E. coli with three different repressor proteins using dynamical modeling. They worked in E. coli with the same three repressor proteins using dynamic modeling. And instead of creating a toggle switch that consisted of two genes trying to shut each other off, they created what they called a repressolator, a ring oscillator, similarly motivated by electronics, electrical engineering. They consisted of three genes in series. A tries to shut off B, B tries to shut off C, C tries to shut off A.
9:09 Vijay Pande? Well, it's the same parts, just different circuits.
9:10 Speaker 01 Exactly same parts, different circuit. They submitted to Nature, we submit ours to Nature. They end up being published at the launch, the turn of the century in January, late January, in Nature back to back. The next week, the head editorial said, "Oh, physicists are beginning to move into molecular biology." And they said, "You should look back at those two papers because there's something interesting going on."
9:31 Vijay Pande? You know, you raise an interesting point about how everyone on the biology side thought this wasn't possible. Almost like the biologists, some of them have an immune response against engineering.
9:39 Hanne Tidnam? That's a great way to put it.
9:40 Vijay Pande? And that seems to continue even to today.
9:42 Speaker 01 It is. I mean, I have many, many close friends and colleagues who are biologists. But I think within different academic disciplines, there's often too much tribalism. So the very common critique that we get is, "Oh, they're not biologists," or "they're not chemists," or "they don't know basic biology, they don't know basic chemistry." Yes, yes, yes, yes, and yes. I don't think it's fair, though. And I think that these problems really can benefit from a disciplinary approach and different perspectives. We don't know the detail at the level that the biologists do. And one of the more common critiques we'll get, and any time we submit papers to molecular biology journals, is that, "Oh, the columns, they don't have sufficient controls that were done." But I maintain that in almost every biology experiment paper I've read, they've never had the controls that I'd like to see, which is the idea that you can knock out a gene
10:25 Speaker 01 and that what's lacking now can be attributed to the gene is absurd from a systems.
10:28 Vijay Pande? It's interesting because if you think about an extra engineer to a physicist. The electric engineer might not understand like a PNP junction and a transistor the way a physicist might.
10:36 Speaker 01 Right.
10:37 Vijay Pande? But can still use them and make parts out of them and still make things. And so I think there is just a role for each one of them. But there's a fundamental difference between sort of doing science and doing engineering.
10:46 Speaker 01 Yes.
10:46 Vijay Pande? And that culture clash is pretty distinct.
10:48 Hanne Tidnam? Yeah, I was just gonna say it's not entirely just that academic tribalism, right? It's a discomfort with a different kind of mode of knowledge. I would love to hear if you can describe a little bit more what beyond the tribalism is valid, what you try to think about, or what you reject entirely.
11:05 Speaker 01 Yeah, no, it's fair. I think the differences in science and engineering are healthy and valuable. I think in many cases, engineering benefits from science in that most of the work that we do from an applied standpoint is really looking to see in what way can we utilize or exploit principles, discoveries that have been made in science and move toward an application. In the same way, science benefits tremendously from engineering in that as a new technology gets developed, it can just open up the ability to address and answer new and different questions. And so there really is a great synergy and it becomes interesting on the contrast, you know.
11:41 Vijay Pande? Well, yeah, there's a synergy, but there is still a sort of fundamental difference in how they view the world and how they...
11:45 Speaker 01 It is very different, right, in that the engineer often is very purposeful, whereas the scientist is really driven more by curiosity. Can we discover? Can we learn more? And it's not that the engineer doesn't want to learn more. An engineer can do remarkably well in many instances with little if any understanding of mechanism. And that we'll get after a black box. You can represent a model that gives you some sense of relationship between input and output of say going back to our perturbation. And yet that model may have little if anything to do with the underlying biology, but it nonetheless has predictive value that's useful to get after a given application of purpose. That drives my biologist friends nutty. But that doesn't mean that the engineer is uninterested in mechanism, that the more we know about mechanism, the better we can do, the more facile we are.
12:28 Vijay Pande? And the more predictive the model.
12:29 Speaker 01 The more predictive the model, absolutely.
12:30 Hanne Tidnam? Let's talk quickly about the bacteria. So why E. coli?
12:33 Speaker 01 You know, on E. coli, the E. coli was easy to pick. It was and remains really the workhorse, broadly molecular biology. It's been very well studied. It's still not fully understood, but we had a number of well-characterized parts, established protocols, and so a couple engineers could relatively quickly get up to speed to do experiments on it. Having said all that, I'll give two points. One is that when I first moved into the space, I was told not to work on bacteria because everything's been worked out, we understand. Oh, really? And it turned out to be far from true. And second, linked to that is that it's fascinating to me that there still is a high percentage of the genes are not functionally mutated in E. coli, 30 to 40 percent. Nonetheless, it was enough there that we could begin to design and build with some
13:15 Speaker 01 level of predictability and some level of comfort that you could insert these circuits and get them to function the way you like.
13:20 Hanne Tidnam? Can you tell us about the latest in bacteria that you guys are now working on?
13:24 Speaker 01 There was then a very nice piece by Louis Serrano and Attila Bexki that came out also in E. coli about four months later in Nature. And then it was about ten and a half years of nothing coming out because the field didn't exist. You had a number of modeling papers, but nobody really had experiments going. We and others then got excited about engineering bacteria, in particular E. coli, as living technologies, as programmable cells that could sense something in the environment, make a decision, and act on it. And around 2004, began creating these programmable cells to be wholesale biosensors. But the notion of putting it in somebody's body was crazy. The idea of the microbiome was not prominent. Nobody was talking about it. And so we talked about how you could engineer these, place them on chips, computer chips, electronic chips, and place them in a quad at a university to see was there an anthrax
14:09 Speaker 01 release or was there
14:10 Hanne Tidnam? Detectors.
14:11 Speaker 01 Detectors of lead paint in a home or on imports. And showed that you could engineer these quite readily. And then by the late 2000s, early this decade, a lot of excitement arose. Could you engineer bacteria to serve as living diagnostics and living therapeutics? Could we engineer bacteria to detect and treat cholera? And we decided to engineer Lactococcus lactis, which is found in Dairy products, fermented milks, and showed you could engineer to eavesdrop on cholera by taking the quorum sensing system out of cholera and repurposing it.
14:49 Vijay Pande? Oh, that's cute.
14:50 Speaker 01 Into L. lactis. And that you could then harness the natural lactic acid producing capability of L. lactis to treat or inhibit cholera infections.
14:57 Hanne Tidnam? It's incredible that you were tackling from the very beginning both the diagnostic and the therapeutic aspects. Did that not seem like biting off a lot?
15:05 Speaker 01 No, no, I mean engineers, we probably take on too much. We, engineers are famous for being able to focus but do it with lack of focus, in the sense that they can, can take on big problems, and thus lack of focus, but take on those big problems and try to accomplish it. We thought for sure it'd be fine, even though it's really viewed as two distinct fields. And we launched what became SynLogic. And in a matter of a small number of years, they now have multiple clinical trials. They're a public company and they're using engineer E. coli to treat different rare genetic metabolic disorders, to treat inflammatory bowel disease, and now to treat cancer.
15:37 Hanne Tidnam? What are the engineering principles that you tend to think are the most useful in this crossover space, that are kind of your north stars?
15:46 Vijay Pande? I mean, part of it is that like, you know, when one is trained as an engineer, you learn certain principles. Do you think bioengineers can take some of those existing engineering principles and bring them over? Or are bioengineers inventing new engineering principles?
15:59 Speaker 01 You know, it's an interesting question. I think that in many cases, it's more techniques and approaches than it would be principles. I tend to think of principles really being technically grounded notions that have established a relationship between certain variables or parameters that you come in. So what's interesting is that a major challenge in engineering biology, or more broadly, specifically synthetic biology, is that we don't really know the design principles.
16:25 Vijay Pande? That's right.
16:26 Speaker 01 And that we'll put things together, and they'll be motivated by electrical engineering, but recognizing that obviously we're not making an electrical or electronic circuit. But in many cases, the circuits don't work at the level as well as we like or as we predicted. And I think it reveals that we don't have those design principles, in that establishing the relationships, for example, that really guide electrical engineers, we're still, I wouldn't say hapdazzledly putting them together, but it's still very much trial and error. And it leaves great opportunities for getting after the design principles. At present, I think for really the first two decades of synthetic biology, it's really been more engineering approach. And that is this idea that you can design a schematic. For your circuit, find the right parts, put them together and begin now
17:08 Speaker 01 characterizing both the parts and their functioning in a circuit and iteratively improve upon the behavior. The engineer will go after it in a highly parallel fashion. So as he was doing the toggle switch, he would explore two dozen possibilities simultaneously and do it relatively quickly and identify the one or the couple that gave him the closest to what he was going after and then he would expand around that after a couple of weeks and do this iteratively and get through what could be basically many dozens and hundreds of possibilities.
17:40 Hanne Tidnam? Was that because of just a different approach or was the technology actually enabling him to do that for the first time?
17:44 Speaker 01 It was really, I think, more the approach. And the distinction is that the young biologists in my lab will instead pick one design, one embodiment of that design, and they're going to drill down and grate the tail over, instead of two weeks, over six weeks, realize that it didn't work and then they're going to go and create the next design on the basis of that, drill down one by one.
18:06 Vijay Pande? More serial.
18:07 Speaker 01 Serial and they will have a much greater in-depth understanding of that one case. But it's going to take them 20 times longer to get to a functioning prototype. And so there is where there still is really a cultural divide on the approaches.
18:22 Hanne Tidnam? Would they argue there's something lost when you do it simultaneously, the engineering-led approach?
18:27 Speaker 01 I haven't heard them say that it's lost.
18:30 Hanne Tidnam? Because otherwise, like, why not?
18:32 Speaker 01 You know, there would be interesting is that I do think they feel that they'd like to get an in-depth understanding. And again, it's now coming up against the culture clashes. The scientist is curious, wants to understand, is looking for discovery. The engineer wants to get it to work and doesn't necessarily care do they understand how it works, but that it worked.
18:49 Vijay Pande? You know, the scientist really wants to understand and making it work is proof of understanding, but understanding is the real ends and making it work is the means. Whereas for the engineer, making it work is the ends.
19:00 Speaker 01 Yes.
19:01 Vijay Pande? And understanding is the means.
19:02 Speaker 01 Yes.
19:03 Vijay Pande? And because both care about the ends, the means actually they care less about.
19:07 Speaker 01 Yeah.
19:07 Vijay Pande? And so they're optimizing for different things, even though from the surface they may look like they're doing the same thing.
19:12 Speaker 01 And I'll draw an interesting further distinction on level of comfort is that I've been in the synthetic bio world now for about two decades and I have yet to get significant buy-in from the engineers in my lab for doing directed evolution. So you would think after you get your prototype that's working okay, but not at the level that you like, that you would be open to using one of the great techniques.
19:36 Vijay Pande? Nobel Prize winning technique.
19:38 Speaker 01 Nobel Prize winner Frances Arnold, who's considered part of our community and she's marvelous and a great pioneer. That you would use directed evolution techniques to optimize, you're just taking advantage of this beautiful feature biology systems. It is completely foreign and anathema to the engineers in that they view it as cheating, that they want to be able to themselves re-tweak this system by design to get it to function the way you like. The chemists and the biologists in my lab much more readily are embracing directed evolution.
20:07 Vijay Pande? That's kind of funny and I think there's now going to be three prongs because, you know, the success of directed evolution over most rational methods, let's say protein design, enzyme design, something like that, it's just really hard to do that rationally. There's a third arm, you have rational design where maybe trying to use physics and quantum mechanics, directed evolution where you just sort of optimize for some function. Machine learning is an interesting way to sort of have it both ways. In that, you know, from all the data you get from directed evolution, could you use that to actually come up with a predictor or readout?
20:36 Speaker 01 I do think machine learning within synthetic biology is one of the next frontiers.
20:40 Vijay Pande? Yes.
20:41 Speaker 01 And the challenge there is a couple of levels. One is can we have enough data to feed the models? And by there, the data is can we generate enough diversity and can we functionally characterize that diversity to feed the algorithm?
20:54 Vijay Pande? Absolutely.
20:55 Speaker 01 I think the marriage of directed evolution with synthetic biology really is also the future coupled to machine learning. Synthetic biology allows you to cheat in that it can move you in that genotype phenotype space into an area that you can't get to readily through evolution.
21:10 Hanne Tidnam? Can you break that down?
21:11 Speaker 01 If I'm going to tweak a system, whether it's naturally evolving or I'm going to use directed evolution to evolve it, you're really going to move incrementally. Now, at Fairness, maybe you get a five-fold or even a hundred-fold or two-hundred-fold after many, many rounds, as Frances showed, for enzymatic activity. But in cases still that you're going to be kind of starting in some place, say if you've got on your x-axis genotype and your y-axis phenotype, you're stuck over in this corner. Direct evolution will allow you to move from that corner, but you're going to stay in the general space. You're going to kind of randomly move around. Synthetic biology allows you to take your starting point far away from that corner. Or to hop. Or to hop over into another spot and then fluctuate around using direct evolution. And so it's the marriage of the two
21:56 Speaker 01 that allows you to explore spaces that would take hundreds of years, if not millions or billions, or what really has happened on the planet.
22:03 Vijay Pande? Well, the interesting thing is you've seen-
22:04 Hanne Tidnam? Sounds like wormholes to another spot.
22:05 Speaker 01 Yeah, yeah, yeah.
22:06 Vijay Pande? You've seen evolutionary approaches applied to electronic circuits, right? Where you make a radio and it's just like some bizarre circuit that works, but it's hard for human beings to understand. It's funny, with biologists and engineers are two different cultures. The machine learning is like a third one still, possibly.
22:20 Hanne Tidnam? Do you think that science as a whole will be moving in this direction going forward? More with this blend of engineering approaches?
22:28 Vijay Pande? There's been a blending between science and engineering, especially in any of these interdisciplinary activities. I have Stanford colleagues that were trained as engineers but are writing science papers and doing science. And I'm trained as a physicist, but I've moved into engineering. And I think when you're at the interdisciplinary sort of interface, a lot of people do whatever it takes.
22:46 Speaker 01 Yeah.
22:47 Vijay Pande? And so that spirit is there. Now, there will be people who stay in their domains that just love biology and are just doing biology and that's all they want to do. And that's also fine. But I think the rise of the interface has really sort of led to this new era.
23:01 Speaker 01 I do see more and more of the interface between quantitative approaches in science, not per se engineering, but quantitative advances of really getting after data analytics and embracing this notion that machine learning and more broadly, computer-based modeling and data analysis. Is becoming dominant as people are trying to figure out how to embrace our increased ability to collect a lot of data and analyze data.
23:25 Vijay Pande? Yes, one thing that I was talking about internally and almost as an investing theme is, you know, where is synthetic biology on the hype cycle? You know, my read on is it's gone past peak hype and is sort of in the now getting into production, getting to useful.
23:36 Speaker 01 Synthetic biology has been hampered by basically too much hype from its very beginning. And I think there's multiple reasons for that. One is that it's a very romantic, exciting field. This idea that you can engineer life to address some of the world's biggest problems is exciting. And second is that I think we've had an unchecked field that is not yet an established discipline. And so you have fringe elements that will grab their 15 minutes of fame to put out stuff that is frankly not realistic. Sounds right, likely will work, but could take centuries to accomplish. And we've been hurt by that. Now, if you go back to the mid 2000s, our field took a strong left turn toward bioenergy.
24:18 Speaker 01 And there are many factors driving it starting around 2004, where you had gas prices going north of $4 for the first time. You had then-President Bush calling for alternatives to petroleum. You had large oil companies looking for alternatives. You had venture capitalists seeing, boy, there was a real opportunity here. And they heard about this exciting new space in synthetic biology where people were engineering bacteria and fungi to invert different foodstuffs and sunlight into fuel.
24:44 Vijay Pande? Sounds like a panacea.
24:45 Speaker 01 Sounds brilliant. And they poured hundreds of millions of dollars into it. You had Department of Energy and other national fundings similarly pouring huge monies in. And it was too early. Too early in that you had brilliant academics showing that you could do this in small bioreactors in their lab at a university. But to think you could scale this up to the million-liter bioreactor was absurd. And that you had loss of efficiencies, you just couldn't compete economically. And so many of these early companies got to be pretty good at producing. $1 worth of gasoline, but it cost them $8 to do it.
25:18 Vijay Pande? Right.
25:19 Speaker 01 Which was a horrible business model.
25:20 Vijay Pande? Yeah.
25:21 Speaker 01 And so that.
25:21 Vijay Pande? Unless gas went up 10x.
25:23 Speaker 01 Unless gas went up 10x, yeah. And so, you know, that part of the field collapsed on itself.
25:26 Vijay Pande? That's right.
25:27 Speaker 01 On the hype. What happened then in the late 2000s, when the bioenergy focus hit earth, came back down, is that the field got grounded and really began building out more broadly tools, constructs, platforms, and turned toward biomedicine. I think the field got really re-grounded and saw that there was great potential. And alongside that, you had other developments in biotech that were complementary to synthetic biology, broadly engineered cell therapy outside of synba, like CAR-T therapy. And you then had the biotech discovery, or development, I should say, of the century, which is on CRISPR, enabling very rapid, easy, inexpensive genome editing that together
26:10 Speaker 01 now opened up possibilities that just weren't going to be readily apparent to us 10 years ago.
26:14 Vijay Pande? I mean, no other directions to go for molecules that are just worth more than oil. I mean, a barrel of perfume is a lot more expensive than a barrel of oil.
26:22 Speaker 01 Yeah, and so then the question is, can you gain a lot by engineering a living cell to produce it versus using a chemist?
26:28 Vijay Pande? Yep.
26:29 Speaker 01 In a regular petri dish?
26:31 Vijay Pande? Yeah, yeah, yeah.
26:31 Speaker 01 And that's where I think the jury's still out on many of these companies.
26:35 Vijay Pande? It seems like they can make it. Now, the question is, is there a business model there?
26:38 Speaker 01 Yeah.
26:38 Hanne Tidnam? I'd love to know about company building in this space, what that looks like? How is it actually different, building a company in this space versus traditional bio?
26:46 Vijay Pande? Yeah, and then there's like two, two models. There's how to build a traditional bio company and how to build like a engineering-driven tech company. And this seems distinct from both.
26:53 Speaker 01 You have to make a decision. And that is, are you a platform company or are you a product company? And what happens is, is that the companies in this space typically are being launched by the academic, is the academic comes in thinking they can do everything. And so they want it to be a platform company that's feeding a product company. And when you then talk to the real company builders, they'll say, no, we've got to make commitment. And I think they're hybrids. I mean, you can have these multi-product platform companies.
27:20 Hanne Tidnam? Let's break it down what you specifically mean by that.
27:23 Speaker 01 So by that, I mean that on a platform company would be that I'm going to develop internal capabilities that will allow me, for example, to engineer bacteria, to sense different things and produce molecules that would have an impact.
27:34 Vijay Pande? To make many products.
27:35 Speaker 01 To make many, many products. And it might be that I'll even focus on therapeutics. But that I could have the capability to engineer E. coli that would allow me to address 20 different diseases. And that my model then might be I'm going to team up and partner with pharma and large biotech. The product company would be, no, you know what I'm going to do? I'm going to engineer bacteria to go after your recycled disorders. And I'm going to launch a clinical trial. And that's my product. I'm going to move it toward clinic. I could build and maybe have one or two other internal programs. But it's a struggle. In what way are you going to play it in this space? And you see companies that are playing both ways. Synlogic is really a product. It's a therapeutic, synthetic bodies therapeutic company that is focusing on developing therapeutic products that will go into patients and that they themselves, maybe through partnerships
28:20 Speaker 01 to market them, but develop them. Ginkgo Bioworks, for which I'm not involved, is more of a platform company, developing capabilities to engineer organisms, to optimize enzymes, and through a partnership model could have a platform. The platform companies in this place are playing to the technology investors. And the product companies are playing more toward the traditional biotech investors.
28:41 Vijay Pande? Although I think that's starting to change in that I think there's been cycles. So even the biotech investors are curious about platforms.
28:46 Speaker 01 And I'm encouraged to hear that, right? It goes booms and busts. So when the NASDAQ collapsed 17 years ago, in 2001, the local biotechs ran from platforms. Prior to that, from 98 to 2001, it was platform happy.
28:58 Vijay Pande? Yeah.
28:59 Speaker 01 And then they became fixated on products to a fault in that many of the companies would rush a molecule into trials to say they had a product. And most of those molecules were horrible and then they'd fail.
29:09 Vijay Pande? The other thing that happens is that it's very tempting to, once you've built a platform and you actually have an asset that is, let's say, past phase one or phase two, that becomes worth so much, it's a hard financial decision. To take money away from that and put it to other things. And then you become a platform company, becomes a single asset company.
29:26 Speaker 01 And I think that the model is that if you can move your assets, develop assets, move them to trials, you then also have that struggle. Do you now flesh out your platform? And I see that in companies I advise and I'm involved with. And I'm a big believer that you should also have the long view.
29:42 Vijay Pande? Yes.
29:42 Speaker 01 That it's worth building that internal capability, whether it's around design or whether it's around manufacturing. So for example, having the ability to manufacture your product in-house significantly increases your value.
29:55 Hanne Tidnam? What is the go-to-market like in this space when you're building these synthetic bio products or platforms? What is the way you see this kind of entering the system? How is that process happening?
30:06 Speaker 01 I think the challenge is really making a commitment as to what is your go-to-market? Because in many cases, it's these early companies, particularly coming out academically, are aspirational in that you want to take on all these problems. And yet, if you're really then going to get down, it's very hard to go to market and what's your choice? And then it really becomes specific. So in one area I'm involved with is diagnostics. Is it through a partnership model? Or are you really going to develop a product, get it to approval, and now introduce in the market? Well, that poses all fascinating new challenges of who's going to manufacture? How are you going to distribute? What are your distribution? And in many cases, I think synthetic biology, it's still at a stage where either they're following traditional paths.
30:49 Speaker 01 And the traditional paths in therapeutic space and even diagnostics is through partnerships. And so it's at what stage? But then it's a challenge is at what point do you partner? You partner too early, you're not going to get as much value for your investors. And so can you hold it off? Can you maybe partner off maybe less essential? Less central?
31:06 Vijay Pande? Well, and part of it is that synthetic biology has the hope to do so many different things. And almost now, you have the challenge of figuring out of all the things you could do, what should you do?
31:14 Speaker 01 It is. And that's a hard choice to make because it's really opportunity cost on the negative that will drive many of these companies batty. And it's really what makes or breaks companies. And separate from the technology, it really comes down to the team that can make those decisions and that has the flexibility to change up depending on. Markets that change or opportunities that come or go.
31:33 Hanne Tidnam? I wanted to think about how this field is now sort of percolating down into education. And I was really surprised to read that it's percolating quite far down into these, you call them biobits, these kits, these classroom kits. Can you tell us a little bit about those and what kinds of kids are using them, what they're learning?
31:49 Speaker 01 Yeah, so I'm 53 years old. The best Christmas present I ever got was when I was in third grade from my dad's mom. And it was one of the old classic chemistry kits. Big yellow tin that had these powdered chemicals. And I did all these different experiments. And they had fantastic combustibles and explosive switches.
32:06 Vijay Pande? I don't think you can buy those anymore.
32:07 Speaker 01 You cannot buy them.
32:08 Hanne Tidnam? Because you can't blow yourself up.
32:09 Speaker 01 You can't blow yourself up or poison things and set things on fire. But you have these marvelous hands-on activities for chemistry. You have marvelous hands-on activities for electronics, robotics, mechanical, I had an old rectifier kit. But you don't have anything for biotech. And this motivated us, could we develop synthetic biology tools? So these biobits kits are new synthetic biology educational kits that my lab have co-developed with Mike Jewitt from Northwestern. So with Ali Huang, Peter Nagun, and Jeff Stark, grad students and postdocs in our respective labs, we developed these kits that are basically motivated by chemistry kits, but for synthetic biology. Mom's not going to be happy if Sally's growing her brother's stem cells in the kitchen. And what we used were cell-free extracts.
32:54 Speaker 01 So you can open up a living cell, remove the machinery of the cell. And typically, you'd play with these in a test tube, a petri dish. But we showed you could freeze dry them in pellet form. And so now you have shelf-stable, room-temperature-stable pellets that you then add to water. And in now your test tube at your home, you have the machinery of a cell that's outside a living cell.
33:14 Hanne Tidnam? Oh, my gosh, it's like those owl pellets that you can bring home from preschools, but for cells.
33:19 Speaker 01 But it's for cells. They now allow you to play with how proteins are made, how circuits are made. And we created these biobit kits. That already go down to the middle school level. Really with the idea to introduce kids at an early age to the ability to engineer biology. Given that I think in many ways, this century is going to be the century of biology. And really to put a point on it, I think it's going to be the century of synthetic biology. And we need to get the youth excited about the potential of this field to really change the world.
33:46 Hanne Tidnam? So what do you think changes when you start introducing that framework and that mindset so early? What do you think in kind of biology, design, thinking fundamentally changes?
33:57 Speaker 01 Well, I think you expose the young people so they begin to see the world differently. So if you're a designer, you're going to see a problem and think about how you can design it.
34:04 Hanne Tidnam? Just the way your chemistry set did.
34:06 Vijay Pande? Well, you think about some of the greatest challenges that we face right now. World hunger, global warming, these types of things are inherently interlaced between chemistry and biology.
34:14 Speaker 01 Yes.
34:14 Vijay Pande? And if you want to have an impact on these things, you will have to be able to engineer biology to some extent.
34:19 Speaker 01 Yes. I think that synthetic biology will provide solutions that are going to contribute to solving broadly environmental challenges. You're increasingly seeing companies turn towards synthetic circuits to alter traits in a programmable fashion of plants and to enable them to be more robust to pathogens. I think climate is really just beginning to excite the community and broadly environmental issues on two counts. One is in what way could we use algae and other organisms to pull more carbon out of the atmosphere? Can we help reverse the trend on using biology? And second, can we address issues such as the breakdown of coral reefs? So could we create synthetic ecosystems, synthetic microbiomes for coral reefs to make them heat tolerant
35:03 Speaker 01 and to prevent the bleaching of schemes? So I think those two areas are ones that I see in the next 10 years, we're going to see a good amount of activity. And we need more talent in these spaces. We've got to reach out and make them comfortable with these technologies and introduce them to concepts, the eventual design principles.
35:22 Vijay Pande? And that's just the next 10 years. I mean, we talked about the biocentury. The finding is I think it's, people tend to overestimate what people can do in a few years. That's what generally feel like, and underestimate what you can do in 10 years. I don't even think it's, we can conceptualize what 50 years is going to look like.
35:36 Hanne Tidnam? Thank you so much for joining us on the A16Z podcast.
35:39 Speaker 01 Oh, thanks for having me.
35:40 Vijay Pande? Thank you.
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