Microsoft Research Podcast - 067 - Programming biology with Dr. Andrew Phillips

Episode Date: March 13, 2019

When we think of information processing systems, we often think of computers, but we ourselves are made up of information processing systems – trillions of them – also known as the cells in our bodies. While these cells are robust, they’re also extraordinarily complex and not altogether predictable. Wouldn’t it be great, asks Dr. Andrew Phillips, head of the Biological Computation Group at Microsoft Research in Cambridge, if we could figure out exactly how these building blocks of life work and harness their power with the rigor and predictability of computer science? To answer that, he’s spent a good portion of his career working to develop a system of intelligence that can, literally, program biology. Today, Dr. Phillips talks about the challenges and rewards inherent in reverse engineering biological systems to see how they perform information processing. He also explains what we can learn from stressed out bacteria, and tells us about Station B, a new end-to-end platform his team is working on that aims to reduce the trial and error nature of lab experiments and help scientists turn biological cells into super-factories that could solve some of the most challenging problems in medicine, agriculture, the environment and more.

So what Station B is aiming to do is to develop a platform, a system, that will transform programming biology from what is currently a process of trial and error to something that's systematic and predictable. And that requires bringing together many different pieces of the puzzle. In programming biology, there's this sort of standard design, build, test, learn cycle. So we're trying to combine these different stages of programming into an integrated platform. You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting edge of technology research and the scientists behind it. I'm your host,

Gretchen Huizenga. When we think of information processing systems, we often think of computers. But we ourselves are made up of information processing systems, trillions of them, also known as the cells in our bodies. While these cells are robust, they're also extraordinarily complex and not altogether predictable. Wouldn't it be great, asks Dr. Andrew Phillips, head of the Biological Computation Group at Microsoft Research in Cambridge, if we could figure out exactly how these building blocks of life work and harness their power with the rigor and predictability of computer science? To answer that, he's spent a good portion of his career working to develop a system of intelligence that can literally program biology. Today, Dr. Phillips talks about the challenges and rewards inherent in reverse

engineering biological systems to see how they perform information processing. He also explains what we can learn from stressed out bacteria and tells us about Station B, a new end-to-end platform his team is working on that aims to reduce the trial and error nature of lab experiments and help scientists turn biological cells into superfactories that could solve some of the most challenging problems in medicine, agriculture, the environment, and more. That and much more on this episode of the Microsoft Research Podcast. Andrew Phillips, welcome to the podcast. Thanks for having me. It's a pleasure to be here.

So you're the head of the Biological Computation Group at MSR in Cambridge. What's going on in your group? Broad strokes, what big problems are you trying to solve? What gets you up in the morning? Well, one of the things we're really working on is trying to understand how biological systems compute. So biological systems like living cells, they actually perform information processing, but they compute by means that we don't quite fully understand. So my team is working on trying to reverse engineer how these living systems, biological systems perform information processing. I want to unpack the big suitcase of biological computation a bit more. As you've noted, programming biology, it's not new, but there are some new things that are going on. So give us a short primer on biological computation. Where did this all get started and why, and where are we today? So one of the things we're focusing on is trying to understand how to program biological systems. But to do that, you need to understand how these

systems function. If you wanted to try to fix a car, but you didn't understand how the components worked, you couldn't just randomly change those components and expect the car to work. So in order to program a system, we need to understand how it's working. And in our case, we need to understand how these cells are computing. Now, as a species, we've been using cells to do things for us for thousands of years. We've used yeast to make bread or to brew beer. And then several decades ago, we were able to reprogram microorganisms to produce medicines, things like insulin. And now we're taking this to the next level as a field, as a discipline, and programming organisms to do much more sophisticated things, make much more complex medicines, fuels, and materials. So understanding how systems compute is an important step to being

able to program them more effectively. When did somebody discover, hey, we could actually make this do something for us or make it do something different than it does? Was that an aha moment or an accident or did people try to start manipulating biology? Well, I think the first example of programming a microorganism to make a medicine, in this case insulin, was in the 70s. And this is as we started to understand more about DNA, about how cells work. And then more recently, we've been able to sequence the human genome. And now we've been able to write DNA, we've been able to write genes. So there's been this steady progress in technology that sort of underpinned our ability to program biology.

And in fact, there's been an exponential growth in our ability to read DNA and also to write DNA. And then more recently, we've had some transformations in our ability to edit DNA through things like CRISPR. So we have this underlying technology that's allowing us to manipulate DNA, read, write, and edit it. And that's also underpinned this technological growth in our ability to program biology. Well, let's talk about those underpinnings for a minute. There are some pieces that need to be in place before we can make significant progress. You've alluded to DNA. Are there other pieces that we really need to understand before we can move forward and make biology work for us more specifically and even more predictably and less expensively?

Well, yeah, I mean, there's still a lot we have to learn in terms of understanding how biological systems function. So biological systems are highly complex. They're massively parallel. They're probabilistic. In many ways, they're closer to analog computing systems than the digital ones that we're familiar with. So we still have a lot of work to do to reverse engineer these systems.

So that's the challenge, understanding how these systems work. Another challenge is that we still lack a way of doing biological experiments systematically and reliably. A lot of experiments are done manually, they're time consuming, they're error prone. And in fact, recent studies have shown that most biological experiments are not even reproducible. And then the final challenge is that we actually lack the technology stack for programming biology. There isn't really a systematic way. In many ways, programming biology is sort of similar to the early days of trying to program silicon

before the advent of high-level languages and the fundamental theory of computing that we sort of take for granted today. So we're sort of still in the days of almost punch cards and very basic programming technology. That's funny. So you alluded just now to our much more advanced ability to read and write DNA. How has that impacted the growth in programming biology? And what limitations do we still face, aside from the things that you've just mentioned in terms of what we don't understand? Yeah, so this technology has been hugely important and has enabled the progress that we've seen to date in programming biological organisms. So by that I mean reading, writing and editing DNA. But on its own, it's not enough. So we can

read an entire genome, but we still don't understand what most of it means. And we can write an entire gene, but we're still unable to predict how that gene will behave inside a living organism. And we can now edit DNA with really high precision with technologies like CRISPR, but we're still unable to predict the consequences of those edits. So really, we're still in a situation where programming biology is done by trial and error. So what is it about biological systems that confounds our ability to program them? We're coming at this from a computer science angle. So we're basically talking about using programming languages to compile biological algorithms

to DNA code instead of binary. Talk about the differences between how biological cells operate and how computer programs operate. What are the unique challenges that scientists face in programming biology? So yeah, essentially biological programs operate in a fundamentally different manner to traditional silicon-based programs that we're used to writing. So you can think of a traditional computer program more like a recipe where you have a list of actions that happen in a particular order, you know, do step one, then step two, and maybe you repeat this n times. Whereas biological systems, they actually compute via fundamentally different means. So it's more like a chemical soup where you have thousands of proteins interacting in parallel in a noisy fashion.

And many of these interactions can go wrong with some probability. But yet out of all that noise emerges a fairly robust algorithm that is used to compute things like when should a cell divide, or how should an immune system respond to a foreign invader, or even things like the internal body clock, which is essentially a combination of genes and protein interactions that computes a 24-hour period fairly reliably. So these algorithms are actually very complicated for us to understand because we're not used to that. We're still trying to reverse engineer them. Let's talk about noise for a second. You've just mentioned it and you've recently published a paper about how bacteria use noise to survive stress. So tell us about this. What insights did you gain

from this research about noise and bacteria, and what are the implications for the work that you're doing? So this is one of many examples, actually, of how we as a team at Microsoft Research are collaborating with leading scientists in many different fields in universities. So this particular collaboration with the University of Cambridge was with James Locke, the Department of Biochemistry, Sainsbury Laboratory, and a joint PhD student, Microsoft-funded, on potange. And we were looking together at trying to understand the role of noise in how bacteria survive stress. Now, stress in this case is not an emotional response. It's more about, you know, if the bacteria are in adverse conditions, so you give them hydrogen peroxide or some kind of dangerous compound that could potentially kill

them, how do they survive? And this work, you know, Ohm did most of the experiments for this, and we looked together at the computational modeling side, is trying to understand how bacteria can actually anticipate stress and actually survive. And it turns out that bacteria are growing in a noisy fashion, and they're also turning on a stress response sort of randomly. And this noisy growth and noisy stress response are coupling so that bacteria that are growing slowly are actually more able to survive the stress. And also some fraction of the bacteria randomly decide to get into the state so that if a stress happens to be applied in the future, they actually survive.

And so this is a really kind of interesting example of how noise can perform a useful function for bacterial systems. But more generally, it's one of the examples of how we're trying to understand the mechanisms that bacteria and other living cells use in order to survive and process information more generally. So you're talking about bacteria writ large, and we know that some bacteria is actually really, really bad, and we don't want that to survive. Is there a way to parse out, hey, I'm going to provide some noise and stress to the bacteria that I want to survive? Well, yeah, it's essentially trying to understand how a system works.

Then we can try and direct it, reprogram it, depending on what we want it to achieve. So if it's a dangerous infection that we're trying to eliminate, then we can understand where we it, reprogram it, depending on what we want it to achieve. So if it's a dangerous infection that we're trying to eliminate, then we can understand where we want to perturb that system. For example, trying to overcome things like antibiotic resistance. And if it's a beneficial bacteria, for example, the bacteria that lives inside our gut, we want those bacteria to survive because they provide tremendous benefits to us. And so understanding the mechanisms that bacteria use in general can help us determine what strategies to use

in the beneficial case and in the harmful case. What are the most promising applications of the research you're doing? What is the field hoping for? Before we start talking about some specific things that you are doing at Cambridge. Yeah, it's a really exciting field. It's often referred to as synthetic biology, where the goal is to program biological systems more systematically using engineering-based principles. And so this field as a whole is moving forward rapidly, and there are many applications that are actually currently making excellent progress, and there are many potential future applications. The ones that excite me most are actually in the medical field. Biologics, these are drugs made by reprogrammed organisms, and they are

essentially the fastest growing sector in the pharmaceutical industry. And they account for over half of industry revenues and annual drug approvals. And they're actually some of the most powerful treatments we have for diseases like cancers that many traditional drugs, chemical based drugs, are not able to treat. And so these biologics, they're too complex to be made by ordinary chemical means. And so instead, they're made by genetically programmed organisms that act as living factories. And biologics also include sort of more advanced treatments. One example is cell therapy, where you can actually reprogram a patient's immune cells to target specific cancers.

And there's an example of a company, Oxford Biomedica, with whom we're working, that in partnership with Novartis, they've developed the first living cancer drug, which essentially reprograms a patient's immune cells to fight cancer with 80% patients in complete remission in the first trials. So that's one of the most exciting areas, but also many other areas. Agriculture is another one. So nitrogen fertilizer is responsible for 5% of global greenhouse emissions and half of the fertilizer is washed away, causing toxic pollution. And this company called Pivot Bio, they've essentially reprogrammed soil microbes to transfer nitrogen directly to the plant roots

without emitting these greenhouse gases and with almost no pollution. So very little is washed away. These programmed microbes are actually performing extremely well in recent trials in the field. And then there's a lot of potential for other industries as well, like construction. So the cement industry accounts for about 5% of global carbon dioxide emissions. And there's a company called Biomason that's reprogrammed microbes to produce cement at ambient temperature so they can get rid of most of these emissions. And then textiles. So the textile industry generates about a fifth of the world's industrial water pollution, mainly in developing countries.

And there's a company called Colorifix. It's an early startup, but they've actually programmed microbes to produce and fix dyes to fabric using 10 times less water than traditional dyeing methods. And then there are a whole lot of other examples, for instance, in the chemical industry. So the company called Genomaticum, they've actually programmed microbes to produce fully biodegradable plastics. And so now they can produce biodegradable plastics at scale to replace things like plastic bags. And then you look at the textile industry as a whole, we can program yeast to produce

leather or even spider silk. So there's a whole. We can program yeast to produce leather or even spider silk. So there's a whole range of technologies that are really exciting. So this research is incredibly ambitious. It takes a lot of brains, a lot of expertise. We'll talk about partners in a minute, but I want to talk right now about the main project that you're working on. It's called Station B. So we've identified some of the problems inherent in programming biology, as well as some of the sort of individual trial and error attempts to solve them. But this is a much more comprehensive run at this hill. Tell us all about Station B. What is it? How's it different? What's it going to do? So Station B is really motivated by all

the applications that I just talked about, right? And so there's this tremendous potential, but yet there are these tremendous barriers to achieving that potential. And the one I mentioned is just the fact that programming biology is primarily done by trial and error. And so there are many aspects to to something that's systematic and predictable. And that requires bringing together many different pieces of the puzzle. In programming biology, there's this sort of standard design, build, test, learn cycle. So we're trying to combine these different stages of programming into an integrated platform. And in the design phase, we're developing biological programming languages and compilers that can take programs written in a language that people can understand and compile them down into DNA code that living systems can execute. In the test phase, we're partnering with a company called Synthase.

They actually specialize inynthase. They actually specialize in lab automation. They're one of the leading lab automation companies. And what they're doing is developing device drivers and an infrastructure layer to actually make it much easier to program lab equipment, lab robots to do experiments more systematically and reproducibly by digitally encoding those experiments as programs. And Synthase is actually built on top of Microsoft Azure Internet of Things technology. So we've got design, we've got build, we've got test. And in the learn phase, we're actually combining expertise in machine learning to analyze the data in order to learn models of how biological systems compute. So we're sort of

proposing models using machine learning to actually refine our hypotheses, and then storing that information, that knowledge, inside a knowledge base. So that as we go around this design, build, test, learn cycle, we're actually getting better at understanding how to program biological systems. And so the key point here is to try and bring together these different technologies. And over the past decade almost, we've been working on individual methods, individual pieces, individual programming languages. And now with Station B, we're trying to bring together the individual methods we've been developing and some of the breakthroughs that we've made into this integrated system that will help our partners and collaborators become better at programming biological systems. So where is this now? It's very much still in

the research phase, yeah? That's right. We do have a research prototype of this platform that we've developed. The next phase now is to actually work very closely with a selected number of partners in order to develop and apply this platform to specific challenges. So let's talk about those partners for a minute. You've got them across industry and academia. Who are you working with and what kinds of things might we expect to see? Well, we continue to work with many university collaborators around the world on a range of specific research projects. But really the first university collaboration involving Station B as a platform is with Princeton.

There we're working with Professor Bonnie Bassler, head of the molecular biology department, and also Professor Ned Wingreen, a biophysicist by training, on understanding the mechanisms of biofilm formation. So biofilms are essentially surface-associated colonies of bacteria, and they actually kill as many people as cancer, and they are one of the leading causes of microbial infection worldwide, and also an important cause of antibiotic resistance, which was recently highlighted by the World Health Organization as a growing crisis that

we cannot ignore. So what we're trying to do is use the Station B platform to understand how biofilms form, what are the mechanisms that they use. And the platform, as I say, will combine programming languages and analysis methods to allow us to program microbial systems, perturb these microbial systems, measure the effects of those perturbations, try and reverse engineer how bacteria communicate

and how they interact in order to form these biofilms. And then by understanding the mechanisms of formation, we can seek to disrupt these biofilms and potentially, hopefully in the future, that would give rise to new forms of treatment. And by biofilm, you mean slime. Yeah, that's right. Well, I mean, let's get real. So that's fascinating because one of the things we think about when we think about what kills people and what's bad is disease. But where does the disease come from? So that's what you're addressing, right, is if we can get to the source, we can control more of it. That's right. if we can get to the source, we can control more of it.

That's right. I mean, for many years, you know, the pharmaceutical industry has almost been forced to do things again by trial and error. There is a disease and we have a hunch as to what molecules we want to target. And then, you know, pharmaceutical companies and researchers would just test a whole range of random compounds, see which ones stick, and then maybe put those in mice, and then maybe eventually put them in people, without often knowing how these drugs are working. But now as treatments become more sophisticated, and as we get better at treating disease, it's becoming increasingly important to understand how the treatments work. And that requires an understanding of how the disease or the pathogen works. This is so cool because if you look at science

over the eons, it's been, what happens if I put this with that? And your efforts here are to codify and shrink down that process of trial and error by using computer science. That's right. And I do want to emphasize, you know, there's a whole field and there are many people around the world working on this and we're a part of that field. You know, at Microsoft, we do have expertise and many years of research and breakthroughs in biological programming languages, compilers, machine learning methods. But we're part of this growing field that's really trying to solve some of the most important challenges facing humanity. So who are some other partners that you're

working with in Station B and what are you working on with them? So our main other partner is Oxford Biomedica. And as I mentioned briefly before, they essentially have developed technology to reprogram a patient's own immune cells to target specific cancers. And they are the first company together with Novartis to actually have FDA approval for this type of treatment. And in clinical trials, 80% of patients who actually had no hope of surviving, many of them had had bone marrow transplant or had gone through chemotherapy. 80% of patients who actually had no hope of surviving, many of them had had bone marrow transplant or had gone through chemotherapy. 80% of these patients, when they received this treatment, were in incomplete remission. The treatment has also been approved by the NHS, National Health Service in the UK,

but at a cost of £282,000 per patient. And so these treatments are really expensive. And part of our collaboration with Oxford Biomedic is to try and work with them to improve the ways in which these treatments are produced and try by understanding how the cells are functioning, how the cells are producing the treatments to actually bring down the costs, but also to help with the development in the future of new treatments. There's a whole range of diseases, including diseases like Parkinson's disease and others, which could benefit from this type of technology that Oxford Biomedica and others in the field are developing. And so we've just started a collaboration with Oxford Biomedica to help

improve the way that these treatments are produced and to look at ways of producing new treatments as well. What we're doing is working with this company in particular to try and help improve their existing technology and bring down the costs and allow them to develop new technologies, which in turn will be subject to the rules and regulations of the industry. Oxford Biomedica, their treatment is saving lives today. And with our Station B platform, we are looking forward to working closely with them to help save more lives tomorrow. All right, Andrew. With all the promising futures, including winning the war on slime, your research is at its core about altering biology via computer coding.

What could possibly go wrong? Good question. Well, as I said before, we are very careful about who we work with. And the two main partners we're working with, Prestation B, Princeton and Oxford Biomedica, they are subject to very stringent regulations that they abide by. And they've been doing this work for many years. And as I say, as new treatments are developed, then those treatments will go through the same or even more rigorous approval processes. So we're really working with the right partners to try and help them be more productive. So that's one point. What's also very encouraging is that governments are taking this technology very seriously, and they're the ones who are setting the agenda. And there have been councils appointed by various governments to study synthetic biology and the desire to program biology more effectively. And this situation is constantly being monitored.

And as regulations are produced, then our partners will abide by those. So, yeah, we have to many best case scenarios in front of us on how this technology could be really helpful in our lives. But I can think of several, if not numerous, outcomes that might fall in the dystopian bucket of technical advance. So even as you think about how governments and agencies can try to regulate this, Is there anything that keeps you up at night? What keeps me up at night right now is all these challenges we face as a species, you know, sustainability and disease and environmental pollution. That's what currently keeps me up at night.

And I see this technology as a way, as I mentioned in many of the applications I talked about, as a way to solve so many of these challenges. There's also another issue, which is that, you know, what if we do nothing? So nature itself, interestingly enough, is constantly evolving. Natural organisms are constantly mutating. Viruses are mutating. So nature is producing new diseases naturally, constantly. And we're seeing, in some cases, resistance to medicines like antibiotics that have saved hundreds of millions of lives.

These systems are now becoming resistant to antibiotics. And so we need to find new treatments. And so if we do nothing, there is a real danger that a global pandemic breaks out, that nature has produced through random mutation that we are unable to treat because we don't understand how these systems work and we're not able to develop the treatment in time. Or as these existing treatments start to fail because nature again is mutating and smart and out-competing us and going around our treatments, if we don't understand how to develop new treatments quickly enough, then we're in real trouble. So I think there's a real threat from nature itself. But there's another important issue as well, which is, as I mentioned before, about the drug industry

traditionally doing things by trial and error. And now we see this new potential still being done by trial and error. It's going to be increasingly important to do things in a predictable way, to do things systematically, to be able to understand what we're doing. I think with computer models, programming languages, machine learning, being able to close that loop between models and experiments, we'll be able to predict more and more accurately the outcomes of the modifications we're making so that we can be very careful about not making the wrong modifications. And if we get better and better at counteracting the bioterrorist that is

nature, which is constantly throwing things at us, we'll also get better and better at counteracting human endeavors, which are trying to be malicious, because now we understand that if a random mutation happens or a deliberate mutation happens, we'll be able to counteract it. So I think it's going to be really important to stay on top of this. Andrew, tell us about yourself and your academic background. You're originally from Barbados, West Indies. You went to Toulouse, France, and now you're in Cambridge, England. You've had quite a journey. What got you started and how did you end up at Microsoft Research?

Okay, so I was always interested in robotics, engineering. I was fascinated by machines that people design. And so I studied engineering in Toulouse, France. And then I got really interested in programming. And so I learned computer science in Cambridge, did a PhD at Imperial College in London, and studied concurrent, parallel computer systems. So programming languages for programming these parallel systems, the theory and also the implementation techniques. And there, while at Imperial, I met Luca Cardelli, a scientist at Microsoft Research at the time. And he was of a similar background,

but a leader in the field of concurrent programming languages. And he was applying these to study biological systems, which are massively concurrent. And I got fascinated by this. And so I did an internship at Microsoft Research.

And then I was hired by Stephen Emmett, who was leading a team at the intersection of computer science and biology, and that's how I got started. Since then, I've been trying to develop methods from computer science, but that are specific to biology, and there's been a lot of cross-fertilization there. So did you actually come up with the programming language to translate from binary code to DNA code, as it were? Well, actually, I had an intern, very, very talented intern back in 2009, Michael Peterson. And we worked together on this very preliminary prototype of a programming language, which he coded up. And then we published the paper together and designed the language together and since then we've sort of been evolving and extending the language and more importantly trying to bridge the gap between what you write on a computer and what gets executed in a cell and making sure that that's more and more predictable so we started a long time ago I think we still have a long way to go in the future

but we're making progress. Is anyone else using your language? So we've developed actually three main languages, one for programming systems at the molecular level, another at the genetic level, and a third at the network level. So far, we've had most success at the molecular level because it's much more predictable. This is sort of programming DNA systems to compute. And so, yes, there are a number of people who've used that language. I've also taught some courses at this International Genetically Engineered Machines competition on using our genetic programming language. So, yeah, we do have people using our software, but we are actually very careful about who

we collaborate with and using the software mostly internally. Do you have names for the languages? Yeah, we have one. It's called Visual DSD, DNA Strand Displacement. Another one is Visual GEC for Genetic Engineering of Cells. And the third is RAIN, Reasoning About Interaction Networks. I love that. So every so often I get a researcher on the show who has such an interesting side quest that we have to go there. I'm not even going to ask you about all the things you've done, like snowboarding, kite surfing, Chinese kickboxing, Thai boxing. You're just like this extreme guy. But you're a qualified ballroom dance instructor. And you were a member of the

Imperial College dance team while you were getting your PhD. So I just, I have to know, how did you get involved with the Strictly Ballroom set? Okay, so how I actually got started was I was sort of looking forward to my wedding and wanted to make sure that I did a good job on the first dance. So I thought I would attend a couple of ballroom dancing classes, But then I got invited to audition and it all took off from there. So I was part of the university team. We used to travel around the country and compete with other universities. It was great fun. We used to have lessons and practice several times a week.

And I really enjoyed it. And then because we had to do all of that, I thought I could as well take the exams that qualify you to be a ballroom dance instructor. And then, you know, because we had to do all of that, I thought I could as well take the exams that qualify you to be a ballroom dance instructor. And so I did those. Sadly, I'm not so much involved anymore. That was a long time ago.

My best dance was the waltz and also the foxtrot. I really enjoyed it. I still do the odd salsa from time to time. Awesome. All right. As we close, I like to ask my guests to leave

our listeners with some parting thoughts. So sometimes it's advice, sometimes it's wisdom, sometimes it's predicting the future. What would you say to aspiring researchers who might be interested in the field of computational biology? What are the big open problems and what kinds of people do we need to help solve them? Well, the first thing to notice is that it's really an interdisciplinary endeavor. So we need mathematicians, computer scientists, people with expertise in machine learning, programming languages, lab automation, and of course, biologists, experimental biologists. So I would say that if you're looking to get into this field, it's really important to at least understand the intersection of these different disciplines or a subset of these disciplines.

Someone who can do biological experiments but understands the principles of, say, machine learning could really help make some of these exciting breakthroughs at the intersection of the two fields. The other thing is that I really think that programming biology is going to transform many of the industries that are in existence today. I think it's a sort of an underpinning technology that will help transform medicine, food, energy, and build the foundations for a future bioeconomy that's based on sustainable technology. So it's really going to be an exciting field, and I would encourage anyone with an interest to join.

Come help us. Exactly. Andrew Phillips, thank you for coming on the show today and sharing all the insights in programmable biology. My pleasure. To learn more about Dr. Andrew Phillips and how researchers are using computer science techniques to program biological systems,

visit microsoft.com slash research.