00:04:25 · Helmy Eltoukhy, Mark Jacobstein
It's like if you can pattern match between two different areas you can, I think, really make a lot of progress? [00:04:33] S1: I love that in part because I think you're literally giving like the SPC pitch here, which is to take as many bright people from different fields and stick them in a place together and sort of see what comes out of it. But I'm curious to sort of a little bit more about that path because you started with so you got the interest in biology, you did the postdoc work, and then immediately out of the postdoc, started of Avantome. Say a little bit more about that path. And I'm curious how did you end up with Mustafa? So Mustafa Ronaghi, your co founder, ended up as the CTO of Illumina. So how did this all happen?
00:05:03 · Helmy Eltoukhy, Mark Jacobstein
[00:05:03] S0: Yeah, have to give him a lot of credit. He really was the pioneer who was thinking about recruiting students from engineering to solve some biological problems. And so he recruited myself, he recruited my co founder, Amir Ali, at Guardant and a bunch of other sort of electrical engineers. And we were there before bioengineering, before biox, so this was a very early group of sort of weird misfit electrical engineers who were thinking about biology and solving sort of technical problems there. I would say that I didn't know that when I was doing my PhD in semiconductor sequencing that I would start a company. Doing that I actually had this cursive period where I looked at starting like an e commerce company, like a bunch of other things, a search engine in The Middle East, and I found that sometimes that uneasy sort of period of exploring many different opportunities, almost like a simulated annealing kind of where you're looking at a bunch of different things. And then I did that. So during my postdoc I was exploring probably five or six different other opportunities, and then I finally came back to the PhD work and said this actually makes the most sense to commercialize. And there was another thread. So, when you do a PhD, obviously the path that a lot of people think about is becoming a professor somewhere, staying in academia. So that was one path I thought about. My dad was a professor at USC, I said, Oh yeah, maybe that's [00:06:41] S1: Although also an entrepreneur.
00:06:41 · Mark Jacobstein, Helmy Eltoukhy
[00:06:41] S1: Although also an entrepreneur. [00:06:43] S0: Yeah, so he also left that after a year and started a couple of companies in the valley here. So, I said, Oh yeah, that's a good pathway. Especially I looked at all the professors at Stanford, they had one foot in the door and a bunch of startups. It seemed like a really good place and I looked at it for a while and I just said this is tough. It's like all of the challenges of a startup. You have to raise money, you have to get a bunch of students with the bureaucracy of a 20,000 person organization as well. I said, yes, maybe that's not for me. So I said, why don't I just join a large company? I'll learn kind of the ropes and so on. And then, or I maybe just start a company. And then I thought about it for a while and I sort of had this Pascalian kind of wager, which is like, I said, well, I'll start a company, and even if it fails, I can go join a large company later, but at least I have CEO on my resume, and I'll probably get a higher place in that company than I would have a year earlier. So it was in some ways I think we overestimate risk for some decisions. I mean, I think that's the thing that
00:07:49 · Mark Jacobstein
[00:07:49] S1: I actually think that's very true, especially in the Silicon Valley ecosystem. The way I think about it in terms of doing a startup is you work for one company at Silicon Valley. You just switch divisions occasionally, and sometimes the division gets shut down. But if you're good at your work and you build your reputation in the ecosystem, then that was one failed experiment. So you come out of the postdoc, you go start the company. And one of the things that's it's a very interesting transition. So if Avantome was like a core technology that helped enable next gen sequencing, we should talk about that. But it's very different than Guardant, which Guardant is an application of the core technology that was developed by of Avantome and by Illumina. And one of the things that I think is unusual about your journey is I feel like most entrepreneurs play in one layer. Like there's people who make infrastructure. They usually tend to be so closer to the metal, more science y types, and then there's people who make applications, they tend to be more commercially minded or product people. It's very rare for, I actually don't know many other entrepreneurs who sort of played between the different parts of the stack, and Maybe walk us through that and like what motivated that? We just talked about this a little bit in the other room, but I think it would be great to hear.
00:08:59 · Helmy Eltoukhy, Mark Jacobstein
[00:08:59] S0: Yeah, I think part of it is like what problem are you trying to solve? And I think I was fortunate that the PhD I had was sort of one foot in multiple doors, in two doors, so the one was the circuit design side of things. The other side was information theory signal processing is very sort of underrated because it really helps you break down problems, think about like what's core, what are the core variables, what are the core elements of this problem, distill them into sort of those core fundamentals and try to come up with a solution. And that's essentially what I think you can do with almost any field. There's jargon there, there's things you have to learn, you have to understand like the ins and outs. But once you do, you can sort of break those things down and make a lot of progress there and come up with solutions. So some of it was, you know, if you think about I started in like straight electrical engineering doing like device physics and so on and there was one core set of problems there in terms of like how do you make the frequencies faster or this or that and then translating it into photonics and basically detecting very low amounts of light from DNA sequencing reactions and really understanding the physics of that, what is the problem with that, and then taking that at Illumina and scaling that down. [00:10:28] S1: Was that sort of the core, I know of Antone was like CMOS is applied to very early versions of NGS, but I don't know much more than that. Was that sort of the core thing?
00:10:39 · Helmy Eltoukhy
[00:10:39] S0: Yeah, core thing there was a technology called four fifty four that was the first next gen sequencing technology. That technology used the same camera that's in the Hubble telescope. It was a $100,000 camera. So, the system was a $500,000 machine. We realized that essentially something like 90% of the light was being lost in the optics. When you think about a camera, it has a big lens, the actual source is far away. So the amount of light that's actually coming into the pixels there is very, very small. So we said, you know, if you take a CMOS sensor and you put the light emitting reactions directly on each pixel, you could miniaturize everything, you could actually make the CMOS sensor disposable because you could make them for $10 But the problem was CMOS was not even good enough to be used as webcams back then. It was still a very CCD centric sort of ecosystem. And so we had to sort of improve the sensitivity of CMOS, photodiode and photo pixel probably by about a hundredfold in terms of what was there. And so that was part of the problem. So it was really systems engineering, thinking about the whole system, How do you get those 10x improvements in certain areas so that you can make the whole thing work? And it's the same thing with DNA sequencing with liquid biopsy. There was a 10x problem in terms of a lot of molecules were being lost when you take a tube of blood and you look at cell free DNA. At the time, only about 5% of the DNA was actually being used in the sort of sequencing reaction, and we improved that to about 70% of the DNA being used. So that gives you 10x, 14x improvement in terms of sensitivity. And then there was a noise issue in the back end. The sequencers at the time had about a 1% error rate. We were trying to take things down at the level of one in a thousand, maybe even a little bit lower, so we had to improve the error rate. So it's fundamentally, there's biology there, but these are engineering problems.