Adam Kurkiewicz is the CEO and co-founder of Biomage, an open-source bioinformatics software company whose mission is to improve human health through science and technology. Biomage makes it easier for researchers to understand their own data without having to hire a bioinformatician, which often comes with problems around collaboration, communication, and price.
After completing his Bachelors in Computer Science and Mathematics, Adam joined Skyscanner. And though he loved the company culture, he did not agree with the mission and instead wanted to address people’s health problems, so he decided to pursue a PhD in bioinformatics. However, he decided to not continue with his PhD, and along with his friends Iva and Marcell, they founded Biomage. After overcoming hurdles in the hiring process, Adam and his co-founders have now successfully and proudly expanded their team to 15 people.
In terms of their software, while a typical biology experiment will produce one data point, with Biomage’s single-cell RNA-seq technology, a single experiment results in 200 million data points. Naturally, that would require powerful software capable of processing all that data. So, in simple terms, Biomage’s software does both the processing as well as the analytics of data. Inspired by MongoDB and Elasticsearch, Adam likens Biomage to being the MongoDB of bioinformatics.
Here’s proof that Biomage works. Dr. Angela Bradshaw’s research on the heart bypass in the University of Glasgow uncovered the mechanism occurring after receiving a bypass that leads to its failure. Through this research, they’re able to prevent this from happening in the future, and this discovery would not have been possible without Biomage. Aside from the University of Glasgow, another one of Biomage’s partners is Harvard Medical School, and they hope to partner with more and more universities and institutions.
Adam shares his advice for founders on how to raise funding, and he highly encourages joining accelerator programs. In 10 years’ time, Adam envisions Biomage contributing to major advancements in precision medicine, because ultimately, their goal is to help as many scientists as possible for the betterment of everyone.
Maiko Schaffrath 00:02
You are listening to Impact Hustlers, and I am your host, Maiko Schaffrath. I have made it my mission to inspire the next generation of entrepreneurs to solve some of the world's biggest social and environmental problems. And for this reason, I am speaking to some of the best entrepreneurs out there who are solving problems such as food waste, climate change, poverty, and homelessness. My goal is that Impact Hustlers will inspire you, either by starting an impact business yourself, by joining the team of one, or by taking a small step, whatever that may be, towards being part of the solution to the world's biggest problems.
Adam, it's really good to have you on Impact Hustlers. I'm really excited to talk about your journey and Biomage. Thanks for joining.
Adam Kurkiewicz 01:00
Thank you so much for having me. It's a pleasure to be here.
Maiko Schaffrath 01:03
Thank you very much. You are actually a software engineer by training. You've worked for a while with Skyscanner and then also did a PhD in bioinformatics. I'd love to learn from you a bit more about your journey of doing these two things and how you first started Biomage. Why did you decide to start Biomage initially?
Adam Kurkiewicz 01:28
I just have to say, before we get into it, I never finished my PhD, so I suppose I'll be better classified as a PhD dropout, bioinformatics PhD dropout. But yeah, that's true. I did my BSc in Computer Science and Mathematics.
And after graduation, I joined Skyscanner, where I've worked first full-time and part-time on a problem of making it easier for people to fly to wherever they want to go for their holidays or for work. I loved the company. It was one of the best places to work that I could ever imagine. Actually, Biomage's culture is very much inspired by Skyscanner's culture.
The one thing that didn't work for me was the mission. I did not want to make it easier for people to fly airplanes and pollute the planet even further. What I wanted to do is I wanted people to be healthier. I wanted to fix some fundamental problems with human health, such as cancer, cardiovascular disease, diseases like Alzheimer's, and I decided to reduce myself to part-time at Skyscanner. I worked just one day per week, and I decided to pursue a PhD in bioinformatics.
The PhD really taught me about biology, and I really learned about what it means to do biology, what it means to be a scientist doing biological research, and I was able to combine the things I learned in Skyscanner and I learned through my degree as a computer scientist, software engineer to biology, and that's really how Biomage was eventually born.
Maiko Schaffrath 03:32
Got it. Amazing. Why did you decide to go from the world of academia, doing the PhD and dropping out to start Biomage? Did you see you could actually make stuff happen in a better way through a company? Why did you do it in this way?
Adam Kurkiewicz 03:51
Yeah, so I always wanted to build software. I've always built software. I've built software before I had a company, before I worked in Biomage, before I worked in Skyscanner. The thing about building software in academia is that it's really difficult, because academic research is about creating new knowledge.
And if you are creating a tool which makes it easier to create new knowledge, it's actually very difficult to get that funded, because the people who decide where money should flow, the people who decide who gets grants and what sort of scientific initiatives get funded, they're very often struggling with the concept of second-order research, which is, in this case, creation of software that enables data analytics, biological data analytics.
I think that industry is very often a really amazing place to do that. So, without thinking too much, 10X Genomics, who are a public company that have built an analytics kit for single-cell RNA-seq data creation or for data analysis on the wet lab side. They've actually enabled hundreds of papers and top-tier journals, including, for example, discovery of new cell type in the human airways called pulmonary ionocyte, which was immediately linked to cystic fibrosis.
And we now know that in order to treat this condition, what is a fairly rare disease, we actually have to treat this particular cell type that we didn't even know existed before 10X Genomics made their kit available. So, this state of second-order science, so that it can really only happen in industry with the current setup of how science works.
Maiko Schaffrath 06:17
Got it. So, in simple words, I'm going to try to introduce a little bit on what you're actually working on, but I'd love to you double down on it afterwards and give us a bit of an overview. But in simple words, you basically switched from the world of science into the world of building the tools for scientists to be more effective and efficient in how they work.
So far, from what I've understood, I think I've been really excited to talk to you, because not everyday I get kind of deep tech founders on the podcast like yourself. Obviously, you're basically helping biologists that are looking at analyzing, understanding human cells and basically speed up the process where they previously would need bioinformaticians to come in and do a lot of manual work.
You basically help them speed that process up and extract insights from human cells. Is that a good summary? How is that type of research? What are the use cases? What are actually the scientists that you're helping working on and how do you help them do a better job?
Adam Kurkiewicz 07:37
Yeah, this is a great summary. I'd like to take a bit of a step back here and just sort of walk you through the high picture overview of biology that we've really seen happening over the past couple of decades. So, traditionally, biology is a low throughput science. What that means is that a single experiment will very often result in just one data point, which is usually like a smudge in some sort of gel, and that indicates the presence or the absence of a protein or some other biological entity that the experiment is trying to detect.
If you're going to be doing discovery in that way, if you're going to be trying to discover new biology, protein by protein, or this low level, low throughput experiments, then learning something new can actually take decades.
There are famous biology labs that have had PhD programs for 20, 30 years. A PhD program would be a couple of years, three to seven years, depending on where it happens. And by the end of those 20 or 30 years, they are able to show that a certain disease works in a certain way according to a hypothesis that was formulated over 20, 30 years ago.
It's a painstaking process of just generating this very manual data, and one experiment corresponds to just one data point. Since the sequencing of human genome in early noughties and especially since this high throughput, like a big shift, in how biology is done that followed, nowadays, certain types of biological experiments can actually create 200 million data points.
This is the type of experiments that we work with. So, single-cell RNA-seq, which is the type of technology that we help biologists work with, a single experiment in this type of technology can actually create 200 million data points. The shift that's required in the skills that a modern-day biologist needs to possess is tremendous, and all of this happened very, very recently.
These are changes that are less than 10-15 years in the field. The specific difficulty is just making sense of the data, and you've got 200 million data points. What do you actually do? You need computing power that's going to help you process that data and present it in some sort of understandable way, but you also need an interactive interface that will allow you to look at the data set and to ask different questions multiple times to change your perspective on the data set.
It's no longer "a data set leads to a result leads to the next experiment and the next data set." A data set leads to this heap of results, and the process of doing science and the process of actually searching for the answers is actually at this very stage of data analytics.
That's exactly what Biomage enables, and that's exactly what we've built. We've built a software that allows both for the computational bit, which is the data processing, but also for the analytics bit, which is understanding the science behind the data.
Maiko Schaffrath 11:54
Got it. And then, talk us through the actual research that scientists that are using Biomage are doing. What's the type of research they're doing? I know some of them are working in cancer, for example, but what are the problems that they're trying to solve, the research that they're doing? What impact does this have on the world?
Adam Kurkiewicz 12:17
Sure. So, one of the researchers that we work with at the University of Glasgow, Dr. Angela Bradshaw, her research focused on heart bypass. What she was able to show using the analysis that we carried out for her, she was able to show that there's a certain biological mechanism that occurs after somebody receives a heart bypass, which can, actually, about 50% of cases lead to the failure of the bypass.
And using our technology and using the cooperation with the company, she was able to show why that happens. We're able to devise a plan for how to stop this from happening. So, this is essentially very early stage drug discovery effort here.
We see that again and again in other researchers that we collaborate with. They will work on some of those fundamental problems in cardiovascular research, in cancer, in rare diseases research, in neurodegenerative research. So, these are the sort of lines of investigations, and that's how the company pushes the boundaries of understanding of biology.
Maiko Schaffrath 13:58
Got it. Those researchers, how would they work without Biomage? You already gave a bit of an introduction on how biology essentially works and how experimentation is being done in one-by-one insights almost and not generating massive data sets. But what are some of the other solutions out there for those researchers? With your approach, are you fundamentally changing how they even do the research? Or are you speeding them up in terms of just getting them to the goal quicker? How does that work?
Adam Kurkiewicz 14:33
So, a lot of researchers, and that's true both in academia and industry, a lot of biological researchers just entirely give up on the side of data analytics. They do the wet lab stuff, and they collaborate with a bioinformatician, with a trained professional, a bit like me, who has some biological knowledge but primarily computational data analytics knowledge, who helps them to make sense of their data.
It's just really a collaboration between these two experts in their respective fields. The only issue here is that there is a lot of biologists and there's very few bioinformaticians. So, all the biologists do not actually have access to that type of expertise.
Another problem is that even if they do manage to find a bioinformatician to work with who's going to help them understand their data, very often, the collaboration can be really quite difficult, because the biologist doesn't really understand what's possible on the analytical side, and the bioinformatician very often doesn't understand the biological question. It's a bit of a constant back and forth between the two, and all of the communication overhead, that can really slow things down.
The third thing is it's really expensive, because if you are going to hire a bioinformatician to help with your data analytics, you have to pay them a salary. You have to spend three to six months working with them on the project. So, using Biomage software gives you about 80% of what they'd be able to get from a bioinformatician, and as a biologist, you can just use it yourself.
Maiko Schaffrath 16:31
Got it. In building Biomage, what's been the biggest challenge for you? Has it been on a technological side? What's been some of the biggest hurdles for you to make this happen and bring the software to life?
Adam Kurkiewicz 16:48
I'd say, definitely, hiring is really, really difficult. Hiring software engineers is something that we really have struggled with. We've grown the team from the founding team, it was me, Marcell, and Iva, it was the three of us working on this very early on. Very early on, Vicky Morrison, our Chief Science Officer, she left academia.
She left the University of Glasgow. She joined Biomage, and she was our first employee. But at the moment, the size of the team is 15. We've got 15 team members, both bioinformaticians and software engineers. Actually, scaling the company and growing that team and finding the right sort of talent has absorbed a lot of my and other founders' energy.
The way we've done it is, initially, we tried to look in our networks. We initially tried to use LinkedIn as a way to find satellite software engineers, but these approaches haven't given us many very good candidates to really be able to scale the team.
So, eventually, we actually used Stack Overflow as a place to- we've put an ad on Stack Overflow, which was there for a week which generated 500 leads, and we just painstakingly went through each and every CV that we've received and organized calls and different interviews at different stages with the candidates.
Eventually, we were able to grow a team I'm really proud working with and I think is an incredibly strong bioinformatics software team, but this has been a major challenge. In Q4 last year, that was essentially most of my time.
Maiko Schaffrath 18:59
Got it. Was it because the software engineers also needed to have really good knowledge of bioinformatics, or was it just generally hard to hire software engineers?
Adam Kurkiewicz 19:14
So, we've split the two functions where you would hire a software engineer based on the strength of their software engineering and potentially general interest in biology or bioinformatics, and we'd hire bioinformaticians based on their strength in bioinformatics.
So, people who have both are really incredibly rare. We don't have many of those in the company, but I'd say the difficulty has really been just the way the job market functions for software engineers. It is very different than it functions for other types of employees.
Actually, I would often discuss with our Chief of Staff, she would have worked in hospitality before she got involved, before she did her degree in bioinformatics, before she got involved in startups. The way she thought about employees was just really, really different than we think about our employees. We just think that they're incredibly valuable.
They are the heart of the company. They are the company. In traditional businesses, the term human resources or even just the approach to the employees is as sort of just a commodity resource, but we really do not think about our employees that way.
They are the company, and that's it. So, it's really hiring people who are not only going to be very good at their job, but also are going to become part of this movement that we're building, part of this, I would sometimes say "cult" that we're building, a human health cult that we're building. That's very difficult.
Maiko Schaffrath 21:37
Got it. So, my next question would be a little bit on the technology side. Obviously, being a pioneer in such a specialized space where, actually, bioinformatics is not necessarily something that's very mainstream and a lot of people know how to do.
And at the same time, you already said how difficult it is to find software engineers. How did you actually go about developing the technology in such an environment? And what's been some of the hurdles of developing the technology that you have now?
Adam Kurkiewicz 22:13
Yeah. So, a lot of this is just really great partnerships that we've got. We've got a really great partnership with Harvard Medical School, who are our development partner on the technology. Actually, a lot of understanding and knowledge of what the product needs to have in order to be successful, that really came from their side.
We've been just incredibly lucky to find this incredible partner that we can work with. Right now, we're actually looking to expand our community into partners, both in academia and in industry, other partners than Harvard Medical School.
I suppose, the way to think about Biomage is the way you'd think about open-source companies, such as MongoDB or Elasticsearch. These are companies that have done something a couple of years ago. They've done something that very few people actually understood.
Search engines, databases, these are really low level things, but they solved a real problem, and they were able to capitalize on this untapped potential of the technology that they've built. That's really what we are doing for bioinformatics. We are a MongoDB of Bioinformatics, if you like.
Maiko Schaffrath 23:49
Got it. So, are you taking a similar approach to them in terms of open-sourcing? How do you see the technology being spread or this approach being spread? Is it going to be Biomage being the monopoly in that space? Is that the goal or is it different?
Adam Kurkiewicz 24:09
Yeah, absolutely. We are open-source. We're building a community. So, Harvard Medical School is one piece here. We've got a lot of really great, as I said, internal developers who are part of this movement and who are passionate about what we're enabling and what we're building, but it doesn't really stop here.
Biologists are constantly inventing new high throughput technologies that have this property that one experiment creates potentially millions of data points, and it's really just working with these new technologies, creating new products that allow us to better understand the human body and create new drugs for diseases.
That's really what's driving us, and we envision becoming a multi-product, open-source bioinformatics company that really helps, that really allows any bioinformatician, any biologist who has bioinformatics needs to get their job done in a better way than right now.
Maiko Schaffrath 25:41
Got it. You already spoke about some of your existing customers and partners like Harvard and Glasgow as well. I believe that, I think if you're not already working with some of them in the future, one of the big markets waiting for you as well are obviously pharmaceuticals and drug discovery.
How do you see the impact of this on drug discovery, especially now we're in this interesting time where there's a lot of awareness on this, at least for COVID in terms of discovering drugs or vaccines? Is that relevant to that? How do you think this can revolutionize the way drugs are being developed?
Adam Kurkiewicz 26:29
So, single-cell technology, specifically, and by extension, the software we're building has got a really bright future in the pharmaceutical industry. There are two components here. One component is drug target discovery, so working out maybe not how to create a drug, but what should the drug do. That's an important aspect. That's pretty much most of what we're doing just now.
Most of what we're enabling just now, really, is just discovering new drug targets. But looking, for example, at what happens further down the drug discovery pipeline, in cases such as high throughput screening, where you screen millions of different molecules and you try to see whether they have this sort of effect on the cell or some other biological system that you'd like them to have, these are very completely, very often, just really random molecules that you'd be working with.
An interesting approach is in confirming that these molecules that you think might be potential drug candidates or potential lead candidates, that they actually do work. So, for example, doing something like a confirmatory screen of your lead candidates with single-cell RNA-seq, that's something that could really help further down the drug discovery pipeline.
And then, single-cell RNA-seq can come back actually and validate leads in animal models, so looking at how different tissues actually respond to the lead candidate. So, the implications in the drug discovery process are multifold and potentially could happen at multiple different stages of the of the drug discovery process.
Maiko Schaffrath 28:44
Got it. You already mentioned in terms of your entrepreneurial journey the challenges of building a company like Biomage. You mentioned the difficulty of hiring. You mentioned how important it is to have strategic partners like Harvard Medical School. So, quite a few nuggets already for maybe founders that are currently doing a PhD and that are considering to drop out and start a company.
On the fundraising side, what would your advice be for founders that are deep tech founders starting a company? I can imagine most investors don't know what you're doing. They have no clue about your space.
There's probably a very small set of investors that truly understand what you're doing, and I can imagine that it can be challenging to find the right investors for a company like yours that are aligned with you and that are valuable beyond wiring some money. Can you give us some advice for founders in the space from your experience on that?
Adam Kurkiewicz 29:53
Yeah, absolutely. There are two approaches you can take here. One approach is you can find the investors who understand what you're doing. That's the beauty of the fundraising process; it only has to work once. You can have 100 conversations with investors. It only has to work once. You only need it to work once.
So, if you find somebody who's passionate about what you're passionate, or if you find an investor who's got science PhDs as part of the team who can help you explain that to the investor, then you're in good hands at that stage. That's one approach, just find somebody, find a partner on the investment side. Find a partner who's going to help you on that journey and who understands what you're trying to do.
Another approach is you can, you'll need really both approaches, you can talk about science, and you can talk about what you're doing in a way which is accessible to different audiences. Whenever I'm talking to an investor, I'm trying to highlight the things that are important for them, things like market size, things like opportunities for collaborations with the industry, such as various metrics that indicate the growth of the company.
I'd say probably learning a little bit about the world of investors yourself is something that can really help somebody and really help a founder to adjust their messaging, so they can explain their company in a way that's understandable to venture capitalists.
I'd say it's a little bit of both, working on VCs meeting you in the middle and you moving a bit more towards the world of VCs and understanding how do they speak and just speaking their language, really. So, I'd say there's work that needs to be done on both sides, for sure, for companies like Biomage to effectively raise money.
Maiko Schaffrath 32:44
That's probably one of the biggest challenges that I have seen with highly technical deep tech founders. A lot of times, the number one skill for any founder is sales, whether you're selling to investors in order to get investment or you're actually selling your product.
Sales is very often about the audience, really understanding how to relate to the audience and speak their language, so really good advice on that. But the good thing, you can learn it, so even if it's not your background, if you're not strong at it traditionally, it can be learned.
Adam Kurkiewicz 33:24
Yeah. I think, actually, programs like Y-Combinator, or IndieBio, or Petri Bio, accelerator programs are really, really helpful in honing those business skills and making technical founders better founders, better CEOs, better salespeople.
Maiko Schaffrath 33:52
Yeah. IndieBio, by the way, for anybody listening who wants to learn more about companies like yours, is an amazing program. IndieBio is under SOSV. That's behind it, actually, as well. So, really, a bunch of amazing companies that are being supported across the world, really, and you've been part of that program as well, right?
Adam Kurkiewicz 34:19
Yeah, absolutely. We graduated from the first New York batch last autumn, so I can very highly recommend the program to any founder who would like to learn about how to build an amazing company.
Maiko Schaffrath 34:41
Amazing. I've got one more question for you. If you think about Biomage, and more importantly, the world in 10 years' time, how does the world look like in 10 years if Biomage succeeds with what you're trying to do?
Adam Kurkiewicz 34:56
So, there is a huge opportunity in precision medicine. I believe that precision medicine will happen, and has been waiting to happen, and will happen sooner or later, and I believe Biomage is just going to be a big part of that story, because especially in cancer, every disease is different.
Every cancer is different, and having an assay that tailors treatments to the cancer, to the disease type that a certain individual has, it's just going to be world-changing. Techniques such as single-cell RNA-seq and new iterations of the techniques that are spatial transcriptomics, I believe, will become a huge part of precision medicine, and I believe that Biomage will become part of the part of that story on the data analytics side.
Maiko Schaffrath 36:04
Thank you, Adam, for joining me today. It's been really inspiring to speak to you, and I wish you all the best for the journey ahead. Thanks for joining.
Adam Kurkiewicz 36:13
Thank you so much, Maiko.