In my opinion, YC has no idea what they're doing in biotech. Here's a much more credible neuro disease startup that was recently launched: http://www.forbes.com/sites/matthewherper/2015/05/14/former-...

I'm sure Alice and Jason are very intelligent and motivated, but the smart money's on Marc Tessier-Lavigne any day of the week. The industry dynamics of biotech are totally different - the small and nimble upstart with unconventional thinking rarely wins.

Most of the biotech companies coming out of YC are what I'd characterize as total fliers -- unlike the company highlighted by the Forbes article, these startups aren't being founded by a collection of gray-haired biotech executives with a collective 200 years of research experience in a single disease. And that's fine. One of the biggest problems in biotech right now is that everything is too stodgy and conservative. When I got out of grad school, there was no way of starting a biotech company without putting in 20+ years in academia or at drug company. You wouldn't get funding.

If funding a few grad students for oddball biotech startups leads to a single success, it has the possibility of really opening up the field. And in the worst case, very little money will be lost. I think it's a good thing. If nothing else, maybe it'll turn the attention of smart, ambitious people to something more important and meaningful than photo-sharing apps and YuppieFoodDelivery.com v87.0.

Let a million flowers bloom...as long as they all don't try to crowd into San Francisco, that is.

...most startup companies aren't doing anything new. How many different food-delivery services has YC funded? Photo-sharing services? Video startups? AirBnB wasn't anything new in 2007, either. Dropbox was criticized because everyone knew that file sharing had been done before.

This goes back to what I was saying about biotech being stodgy: the one thing the software industry has going for it is that it's willing to fling seed capital at re-tread ideas. If success is mostly about being in the right place for luck to occur, then the secret is putting enough companies in a place to experience luck. I think that's basically what YC does.

So-called "biotech investors" tend to want to wait around for commercially ready tech to just fall out of academic labs, so that they can avoid putting any investment at all into R&D. There are probably a lot of inexperienced teams that could make a business in biotech if only they had access to cash.

There's a fundamental difference between trying something again where the customers weren't receptive and trying something again where the product couldn't be created. Customer reception can change over time, often quite rapidly and drastically, making previously failed business models successful (ex: Webvan -> Instacart). But nature doesn't just change (at least not over short timescales).

Moreover, as cge explains elsewhere in this thread, you cannot really iterate in biotech:

> Good ideas in software might not catch on, but good ideas in science more often than not turn out to be entirely wrong. Do a closed beta of your software, too, and while your users might not end up liking the software much, it's very unlikely that it's just going to fundamentally fail to work at all. Clinical trials can often end up that way.

Contrast this to business model-based innovation in tech, where a slight tweak can be the difference between a unicorn and a dud.

Another thing I think is different about the industries is that while there are tons of food delivery and photo-sharing startups, there's no really effective/disease modifying treatments for common diseases such as Alzheimer's disease, frontotemporal dementia, ALS, etc, so I think there's plenty of space for any number of new approaches to be tested in the field. I feel it's more worthwhile than pumping billions more into failed antibodies against beta amyloid, as seems to be the trend nowadays.

This is a flawed analogy, because Google's success is largely attributable to business model innovation (AdSense), rather than any core technology, such as PageRank. After all, Robin Li published Rankdex two years before Brin/Page published PageRank, and we all know how trivial it is to engineer around software patents.

> Another thing I think is different about the industries is that while there are tons of food delivery and photo-sharing startups, there's no really effective/disease modifying treatments for common diseases such as Alzheimer's disease, frontotemporal dementia, ALS, etc, so I think there's plenty of space for any number of new approaches to be tested in the field.

That's because the bottleneck in tech is (a) identifying problems actually worth solving and (b) a business model that will lead to a sustainable company. Achieving success means creating a business and iterating to a position of dominance. There was never any question regarding the technical feasibility of Airbnb's site or Uber's app. It was (and still is) all about things like whether Airbnb could create trust between hosts and guests, and whether Uber could navigate the regulatory landscape.

The biomedical space could not be more different. Everyone with a half a clue knows that neurodegenerative diseases are a problem. Nobody who knows the industry is questioning that tons of customers for your (potential) product exist, or that they'll pay boatloads of money for a treatment or cure. The gigantic elephant in the room is whether your product works. Nothing else really matters.

That's something that is strongly influenced by luck. I can point to famous researchers who have made entire careers out of this sort of momentary serendipity, actually.

That's not iteration, that's banging your head against a wall and praying for a miracle. Iteration implies methodically and continuously moving towards an optimal solution, not heading back to the RNG after each failed attempt.

It doesn't help that here, I think, we're seeing a community and media largely centered around software development trying to deal with a field that is far, far riskier, and has far less certainty at every step. Good ideas in software might not catch on, but good ideas in science more often than not turn out to be entirely wrong. Do a closed beta of your software, too, and while your users might not end up liking the software much, it's very unlikely that it's just going to fundamentally fail to work at all. Clinical trials can often end up that way.

> A smart software developer can build and launch a web or mobile app and get paying customers for under $2,000. The same will soon be true for biotechnology.

Moore's law is a unique phenomenon, and it most certainly does not apply in biotech.

2. Hyping up cheaper sequencing the way you are is nothing more than what Sydney Brenner referred to as "low-input, high-throughput, no-output biology" (video source: http://thesciencenetwork.org/programs/reading-the-human-geno...). Also see: http://wavefunction.fieldofscience.com/2015/03/cancer-genomi...

3. Here's a more useful Moore's law derivative that's seen in the life sciences: http://www.nature.com/nrd/journal/v11/n3/fig_tab/nrd3681_F1....

You bring up another good point about "Eroom's Law" in which pharma productivity is actually decreasing, and that article was actually one of the things that got us interested in trying to do something new. What we are trying to do is to help reverse that trend by doing something different than what large pharma companies are already working on - if we keep doing the same thing despite ever-decreasing productivity, then I don't see how we can expect the trend to change.

You can sequence as fast and as cheaply as you like, but the big-O on biological research is not our ability to generate DNA sequences.

Can you point us to a link that has a more substantive discussion of the approach that you are taking?

http://www.nature.com/nature/journal/v461/n7266/full/nature0... http://www.nature.com/nrg/journal/v16/n8/pdf/nrg3934.pdf

The Institute for Systems Biology has been around since 2000. I remember interviewing people in an internal systems biology group at one of the largest pharma companies in the world in 2004 and writing about their work. Admittedly, they were a small unit intended to drive innovation within discovery at that pharma. By 2007 it had been at least tried in most major pharma companies and John Russell was writing about the Awkward Adolescence of Systems Biology.

http://www.bio-itworld.com/issues/2007/sept/cover-story/

Even if your characterization that large pharma prefer a different approach is mostly accurate it is a substantial disservice to the intelligence and hard work of hundreds of scientists who have worked on applying systems biology to the study of disease and treatments.

Maybe I am over-reacting to this article and your video ...but from 2000 to 2004 I heard so many biotech and related software executives tell me that their company was going to solve these same discovery and development problems. Most faded into oblivion, others found a useful niche and a few got targets identified and development started and were acquired by a large pharma to bring them to market.

I sincerely hope that you have discovered a new approach to system biology that will in fact provide breakthroughs. If you have something great let it stand on its own merits; there is no need to misrepresent the hard work and investments of other scientists or the companies they work for.

Also regarding articles, please email me at [email protected] and I will send you PDF versions to read.

I have been doing other things since 2004 and not following pharma and biotech closely. It appears that what has happened is large pharma invested in systems biology in the early 2000's and by the end of the decade had let it fade. It may also be true that it never made it into specific disease groups, like neurological.

Systems Biology feels a little like AI in the software field. It feels so intuitive that it will one day make a big difference, but there have been many big investments and disappointments over the years, but each time it makes a resurgence we get a lot of advancement in narrow domains. And, it still feels like one day, maybe soon we will get breakthrough's in more general AI and maybe you will lead the way with SB.

A certain confidence, even brashness of youth is ok, but please understand how "tinny" it sounds to people who have been around for one or two boom/bust cycles. Forgive us our cynicism.

Journalists often don't create the simplified strawmen, the PR staff writing the press releases for the firm that they are covering do that for them.

When will people understand the problem with genomics isn't complexity, it is a lack of raw data.

We simply have not collected enough annotation on what every gene does and how it interacts. Without that information, you have diddly-squat.

On the other hand, if you do collect enough data in a particular domain to be able to call some interesting targets, it's straight-forward enough to validate the method. To presume generality (as the scientists probably do not, though as the article and every writeup I've seen here do) is surely foolhardy.

As a side note, if the company wishes to pretty much sell to 'big pharma', why then do they publish these touchy-feely tech-crunch articles at all? Their website is designed for all those web-2.0 ideals of retaining customers and click-through and responsiveness, etc. But I doubt Roche cares about any of those things...

Guffaw. Tech journalism at its finest. The company sounds great, but I'm not sure Techcrunch is really doing them a lot of favors by just making stuff up.

It is highly misleading to use the term cure in this case. You simply have no data to support that your costs are any lower than pharma until you actually show people being cured, and I think Phase II or III is the actual point where you can claim initial cure results, and it's not until the drug has been widely deployed for years without side effects that you can claim it's a treatment or a cure.

I do have to wonder, however, when having a focus on neurodegenerative diseases that are largely seen as proteopathies, how useful a method that is presumably targeting only gene expression can be, which is what I assume you're targeting. If the view of diseases like PD as prion-like diseases with perhaps randomly misfolded proteins that recruit others and propagate through the brain is correct, for example, will targeting gene expression be able to do that much beyond treating symptoms? And if targeting genes that are not necessarily

I'm divided on the usual search-of-preapproved-drug approach too. On the one hand, it vastly lowers costs, speeds up trials, and can actually make development somewhat feasible for startups, but on the other, it does really feel like searching through a bunch of drug candidates that have no reason why they'd be effective for your targets. It's unfortunate we don't have a better approach here. And of course, as I'm sure you know, success in mouse models often translates to nothing at all in humans.

I'd be curious to know what sort of genomic data you're actually using for your analysis.

Again, though, it's great to see a startup working in this area, and I'll look forward to seeing what you come up with.

In terms of the expression data for diseases, we are doing a systemic review and re-analysis of publically available raw micoarray data from GEO, as well as RNA-sequencing and microarray data that we will publish soon and make publically available. That part is what I mentioned in the video; rather than just looking for differentially expressed genes (which is what most of the associated studies have done), we use network methods we developed to identify what we think are the key drivers of disease. Also, by combining data we get more power to find signal. To match these with drugs, we use a combination of proprietary gene expression data as well as data from large public projects.

Thanks for your comments and wishes!

Unfortunately, the operative word here appears to be "should."