Post of the Day
June 29, 2000

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Celera

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Subject:  Now, Functional Genomics
Author:  SamIAm99

Finish line! Done. Finit. Time to turn off the lights, auction off the 3700s on Ebay, and take a nice long vacation. Now we get to sit back and watch the customers roll in, cash in hand, right?

Sorry.

There seems to be a general misconception that this accomplishment was the supreme goal and raison d'etre of our beloved genomics concern. That somehow a human genome database with some nifty annotations would be enough. And that drug developers would line up in droves, hypnotized by the sheer power and genius of JCV and Co. Hardly. What Celera has developed is a teaser. A sample product that hints at what is to come. It has significant value to be sure, but it is nothing compared to what they plan to offer over the next few years. The human genome database is the raw silicon upon which increasingly detailed functional circuitry will be layered. Functional genomics, folks, is "what Celera WILL sell".

Before I describe the functional genomic products Celera will sell and how they will develop and evolve going forward, I want to point out a series of articles that every Celera investor should read. (Note: apologies to anyone who cited these references already. I tried to look back, but, alas, was swamped by a flood of posts) In the June 15th issue of Nature, there is a terrific series of reviews covering Functional Genomics. I think to understand what Celera is doing right now, we need to understand where genomics is going and this review helped me tremendously. Since I can't link the articles, I thought I'd outline a few of the key points and then try to relate them to what Celera is working on. Hopefully, we can stimulate a little discussion on what to expect from Celera over the next couple of years and forget the human genome and market jitters for a thread or two.

Data drives innovation

In the first article, the authors present one of the biggest problems scientists face integrating genomic data into their research: we have to change the way we do science. Adding to the trouble, tools to approach problems from this new angle are raw, clunky, inefficient, and in some cases non-existent.

Why is there a problem? Because biologists currently work in a "data-poor", reductionist fashion. We design our experiments to eliminate most extraneous data sets in order to make sense out of the one we want to focus on. Genomics will force us to turn this reasoning on its head. Now, we are presented with enormous data sets that need to be synthesized into useful observations and hypotheses, which can then be tested using existing methodology.

Solution? Build it and they will come. Sounds a lot like a leap of faith, but has some merit. There is very little incentive to build the interpretation tools before the data exists. Because scientists recognize the value of genomic data, they are now becoming increasingly motivated to develop and purchase tools to put it to work. Novel information revealed with the tools will then spur another cycle of innovative demand, and so on.

This is nothing new, but crucial to understand. Revolutionary, discontinuous innovation often displays delayed acceptance as those who have committed resources to previous methods are reluctant to abandon their investment until it becomes obvious they must to stay competitive (GGers, does this sound familiar?). Genomics is at this stage now. Cautious acceptance and slow development of the technology. The calm before the storm. But I do think we can feel the wind kicking up a bit...

Arrays: the first toolbox

While many of the critical tools for genomics are still in their infancy, DNA arrays have entered maturity. The article takes you on a tour of the development of the technology, current uses and future. Rather than rehash, I want to point out one potential implication: what began as a simple technology to look at gene expression has exploded into an entire industry. New uses and advances hardly dreamed of when the first nylon array was produced are popping up everywhere. I see this as a model for the explosive growth the various subsets of genomic data and tools will see in the relatively near future. They touch on a couple in the next articles.

Pharmacogenomics and SNPs

In this nice review, the author uses a few examples to demonstrate the uses of genomics and SNPs in drug discovery. Genomics is currently being used to help in target selection. Specifically, genomics accelerates the discovery of disease susceptibility genes. These genes often help pharmaceutical companies design compounds to treat the disease. That's just one example.

They also focus on two uses for SNPs: disease susceptibility and pharmacogenomics. SNPs will allow us to correlate genotype with risk for developing certain diseases. Physicians could then be able to more carefully monitor patients for certain conditions their profile indicated they were at risk for. Similarly, pharmacogenomics is the correlation of SNP map with drug response. By distinguishing between patients, drug companies can design more effective clinical trials and medicines, and physicians can prescribe more appropriate treatments. The market for this sort of "personalized medicine" is vast and the functional work has just begun.

Proteomics

My take: many, many years behind genomics in technology, tools, and applications. For one, mapping the proteome is tough. There is no equivalent machine to the 3700 sequencer. The methods involve 2D gels that still require large amounts of protein for detection. Tough work. Additionally, the bioinformatic tools are even cruder.

But like genomics, the potential value of the data is well understood. I think this field is not unlike genomics was when the HGP got started. It will be quite a few years before the innovation snowball picks up momentum.

So what does this all mean for Celera?

1. Time dependent increase in value for their data. This comes in two flavors: A) The ever increasing complexity of additional genomes and annotation and B) Increased acceptance as the existence of complete genomic data spurs innovation of tools and other "killer apps".

2. Build your own value chain. Ideally, Celera could happily smile from their enabling technology perch as the value chain condensed around them. But I think it needs a kick-start. They need to develop a few of the killer-apps internally to set off an avalanche of demand. This could be within their Discovery System or collaborative projects with chip makers or whatever.

3. Layer function. They need to pursue collaborations for much of this. The more the better.

4. Focus on SNPs. By all accounts, Celera has mapped more than 6 million SNPs. This is twice the expected density, and is probably enough to get to work on the functional end. They should push collaborations with researchers to correlate disease with genotypes and pharmaceutical companies to develop pharmacogenomic profiles for existing drugs and pipeline products. A unique, functional SNP database will be extraordinarily valuable.

5. More of #4. Personalized medicine. Not only develop the database, but also actually create products to put it to use. SNPs on a chip of Illumina fiber optic. Whatever. I really think this should be their biggest push over the next few years.

6. Collaborative drug development. As the generators of huge amounts of data, they will definitely be able to spot some juicy opportunities that they won't so easily give up to their subscribers. They should pursue them vigorously, but probably not in-house.

7. Proteomics, when time permits. I like the field, but I would like them to focus on what can yield dividends now. I've heard PEB has made a new device, so maybe this will change.

Well, that's what I think. Kind of a combination of what they have stated and where I think they should run with it. They key point: forget the human genome. It's modest sized product that forms the foundation for many bigger products. Now that the hoopla is behind us, let's try to look ahead.

What do you think?

-S


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