Synthetic Biology 4.0 – Not so live blog, part 1

What a difference a few years makes.  SB 1.0 was mostly a bunch of professors and grad students in a relatively small, stuffy lecture hall at MIT.  SB 2.0 in Berkeley expanded a bit to include a few lawyers, sociologists, and venture capitalists.  (I skipped 3.0 in Zurich.)

At just over 600 attendees, SB 4.0 is more than twice as big as even 3.0, with just under half the roster from Asia.  The venue, at the Hong Kong University of Science and Technology, is absurdly nice, with a view over the ocean that beats even UCSB and UCSD.  Kudos also to the organizers here.  They worked very hard to make sure the meeting came off well, and it is clear they are interested in synthetic biology, and biotech in general, as a long term proposition.  The Finance Minister of Hong Kong, John Tsang, spoke one evening, and he was very clear that HK is planning to put quite a lot of money and effort into biology.

Which brings me to a general observation that Hong Kong really cares about the future, and is investing to bring it along that much sooner.  I arrived a day early in order to acclimate a bit and wander around the city, as my previous visit was somewhat hectic.  Even amid the financial crisis, the city feels more optimistic and energetic than most American cities I visit.

I will have to write up the rest of the meeting when I get back to the States later this week.  But here are a few thoughts:

As of the last few days, I have now seen all the pieces necessary to build a desktop gene printer.  I don’t have prediction when such a thing will arrive on the market, but there is no doubt in my mind that it is technically feasible.  With appropriate resources, I think it would take about 8 weeks to build a prototype.  It is that close.

Ralph Baric continues to do work on SARS that completely scares the shit out of me.  And I am really glad it is getting done, and also that he is the one doing it.  His work clearly demonstrates how real the threat from natural pathogens is, and how poorly prepared we are to deal with it.

Jian Xu, who is better known for his efforts to understand the human gut microbiome, spoke on the soup-to-nuts plant engineering and biofuels effort at the Qingdao Institute of Bioenergy and Bioprocess Technology, run by the Chinese Academy of Sciences (QIBEBT).   The Chinese are serious about putting GM plants into the field and deriving massive amounts of energy from biomass.

Daphne Prauss from Chromatin gave a great talk about artificial chromosomes in plants and how they speed up genetic modification.  I’ll have to understand this a bit better before I write about it.

Zach Serber from Amyris spoke about on their biofuels efforts, and Amyris is on schedule to get aviation fuel, diesel, and biogasoline into the market within the next couple of years.  All three fuels have equivalent or better characteristic as petro-fuels when it comes to vapor pressure, cloud point, cetane number, octane, energy density, etc.

More soon.

Company Profile: Blue Marble Energy

A couple of months ago I met the founders of Blue Marble Energy at a party for the Apollo Alliance.  Following up, I sat down with the CEO, Kelly Ogilvie, to learn about Blue Marble, which is the only "algal biofuel" company I have come across that really makes sense to me.  (While at the party, I also chatted with Congressman Jay Inslee for quite a while.  Smart fellow.  Anyone interested in energy policy should have a look at his book, Apollo's Fire: Igniting America's Clean Energy Economy.)

Full disclosure: Blue Marble and Biodesic may begin collaborating soon, so I am not an entirely disinterested observer.

Blue Marble Energy is built around the idea of "recombining" existing biological processes to turn biomass into valuable products.  From the website: "[Blue Marble Energy] uses anaerobic digestion to generate natural gas and other valuable bio-chemical streams."  The company is distinguished from its competitors by its focus on using micro- and macro-algae harvested from natural blooms, including those caused or enhanced by human activity, as feedstock for artificial digestion systems modeled on those of ruminants.  Blue Marble combines different sets of microbes in a series of bioreactors to produce particular products. 

In other words, Blue Marble is using industrialized, artificial cow stomachs to produce fuel and industrial products.

The company's general strategy is to first digest cellulose into synthesis gas (carbon dioxide and hydrogen) using one set of organisms, and then feed the synthesis gas to organisms that generate methane or higher margin chemicals and solvents.  The company expects to produce 200-300 cubic meters of methane per wet ton of algal feedstock.  While biofuels are an obvious target for technology like this, the company also recognizes that fuels are a low margin commodity business.  Thus Blue Marble also plans to produce higher margin industrial products, including solvents such as various esters that sell for $400-800 per gallon.

While other companies are attempting to directly produce fuels from cultured algae, Blue Marble believes these efforts will be hampered by growth limitations in most circumstances.  Biofuel production from algal lipids synthesized during photosynthetic growth requires conditions that cause metabolic stress, resulting in lipid production, but that also limit total biomass yield to ~2-5 grams per liter.  In contrast, Blue Marble "respects the complex ecology", in the words of Mr. Ogilvie, and relies on photoheterotrophic growth of whatever happens to grow in open water.

Blue Marble has already obtained contracts to clean up algal growth caused by human activity around Puget Sound.  The company typically harvests ~100 grams per liter from these "natural" algal blooms.  Future plans include expanding these clean up operations around the U.S. and overseas, and growing algae in wastewater, which would provide a high-energy resource base for both closed and open system growth.  In principle, because the technology is modeled on ruminant digestion, many different sources of biomass should be usable as feedstock.  Experience thus far indicates that feedstocks with higher cellulose content result in higher yield production of fuels and solvents.

Compared with other algal biofuel companies, Blue Marble does not presently require high capital physical infrastructure for growing algae.  However, the company will rely on marine harvesting operations, which bring along a different set of complexities and costs.  I wonder if the company might be best served if it outsourced harvesting activities and focused on the core technology of turning biomass into higher value products.

While the Blue Marble is not now genetically modifying their production organisms, this will likely prove a beneficial move in the long term.  Tailoring both the production ecosystem and the metabolisms of component organisms will certainly be a goal of competitors, as is already the case with companies spanning a wide range of developmental stages, including DuPont, Amyris, and Synthetic Genomics.  Yet whereas modified production organisms grown in closed vats are likely to face little opposition on any front, genetically modified feedstocks grown in open waters are another matter.  For the time being, Blue Marble has an advantage over plant genomics companies because in the company's plans to use unmodified biomass as feedstock, whether algae or grasses, it will avoid many regulatory and market risks facing companies that hope to grow genetically modified feedstocks in large volumes. 

They have a long way to go, but in my judgement Blue Marble appears to have a better grasp than most on the economic and technical challenges of using algae as feedstock for fuels and materials.

Further reading:

"It came from the West Seattle swamp - to fill your tank", Eric Engleman, Puget Sound Business Journal, August 8, 2008

"Swamp fever", Peter Huck, The Guardian, January 9 2008
http://www.guardian.co.uk/environment/2008/jan/09/biofuels.alternativeenergy

"New wave in energy: Turning algae into oil", Erica Gies, International Herald Tribune, June 29, 2008

"Coskata Due Diligence"

Oliver Morton at Nature pointed me to a bunch of excellent posts on Coskata by Robert Rapier at R-Squared.  Recall that Coskata wants to gasify cellulose and feed the resulting synthesis gas to bugs that make ethanol.  Here are Rapier's "Coskata"-tagged posts.

Among other points, Rapier makes some nice back of the envelope estimates of the technical and economic feasibility of Coskata's process.  In short, Coskata's claims appear to be consistent with the laws of thermodynamics, but perhaps not so much with the law of supply and demand, and their logistics challenges might border on being inconsistent with the consevation of matter.

Basically, it all, err, "boils down" to the fact that Coskata is probably going to get tripped up by their focus on ethanol and the consequent energy cost of separating ethanol from water.  Even if you have a nifty process for turning cellulose into ethanol, it takes a large fraction of the energy in the cellulose to purify the ethanol.  And it really doesn't matter whether you distill or use a membrane -- the entropy of mixing still hoses you even if you somehow escape the specific heat of water and its enthalpy of vaporization.

Now if you hacked the metabolic pathway that consumes synthesis gas so that the bug made something more interesting like butanol, or a gasoline analog, that either had lower miscibility or even phase separated, that would really be something because it would minimize the energy cost of purification.

Great work, Mr. Rapier.  And many thanks, Oliver.

DNA Synthesis "Learning Curve": Thoughts on the Future of Building Genes and Organisms

With experience comes skill and efficiency.  That is the theory behind "learning" or "experience curves", which I played around with last week for DNA sequencing.  As promised, here are a few thoughts on the future of DNA synthesis.  Playing around with the synthesis curves a bit seems to kick out a couple of quantitative metrics for technological change.

For everything below, clicking on a Figure launches a pop-up with a full sized .jpg.  The data come from my papers, the Bio-era "Genome Synthesis and Design Futures" report, and a couple of my blog posts over the last year.

Figure 1.

The simplest application of a learning curve to DNA synthesis is to compare productivity with cost.  Figure 1 shows those curves for both oligo synthesis and gene synthesis (click on the figure for a larger pop-up).  These lines are generated by taking the ratios of fits to data (shown in the inset).  This is necessary due to the methodological annoyance that productivity and cost data do not overlap -- the fits allow comparison of trends even when data is missing from one set or another.  As before, 1) I am not really thrilled to rely on power law fits to a small number of points, and 2) the projections (dashed lines) are really just for the sake of asking "what if?".
 

What can we learn from the figure?  First, the two lines cover different periods of time.  Thus it isn't completely kosher to compare them directly.  But with that in mind, we come to the second point: even the simple cost data in the inset makes clear that the commercial cost of synthetic genes is rapidly approaching the cost of the constituent single-stranded oligos. This is the result of competition, and is almost certainly due to new technologies introduced by those competitors.

Assuming that commercial gene foundries are making money, the "Assembly Cost" is probably falling because of increased automation and other gains in efficiency.  But it can't fall to zero, and there will (probably?) always be some profit margin for genes over oligos.  I am not going to guess at how low the Assembly Cost can fall, and the projections are drawn in by hand just for illustration.

Figure 2.

It isn't clear that a couple of straight lines in Figure 1 teach us much about the future, except in pondering the shrinking margins of gene foundries.  But combining the productivity information with my "Longest Synthetic DNA" plot gives a little more to chew on.  Figure 2 is a ratio of a curve fitted to the longest published synthetic DNA (sDNA) to the productivity curve.

In what follows, remember that the green line is based on data.

First, the caveat: the fit to the longest sDNA is basically a hand hack.  On a semilog plot I fit a curve consisting of a logarithm and a power law (not shown).  That means the actual functional form (on the original data) is a linear term plus a super power law in which the exponent increases with time.  There isn't any rationale for this function other than it fits the crazy data (in the inset), and I would be oh-so-wary of inferring anything deep from it.  Perhaps one could make the somewhat trivial observation that for a long time synthesizing DNA was hard (the linear regime), and then we entered a period when it has become progressively easier (the super power law).  I should probably win a prize for that.  No?  A lollipop?

There are a couple of interesting things about this curve, along which distance represents "progress".  First, so far as I am aware, commercial oligo synthesis started in 1992 and commercial gene foundries starting showing up in 1999.  The distance along the curve in those seven years is quite short, while the distance over the next nine years to the Venter Institute's recent synthetic chromosome is substantially larger.

This change in distance/speed represents some sort of quantitative measure of accelerating progress in synthesizing genomes, though frankly I am not yet settled on what the proper metric should be.  That is, how exactly should one measure distance or speed along this curve?  And then, given proper caution about the utility of the underlying fits to data, how seriously should one trust the metric?  Maybe it is just fine as is.  I am still pondering this.

Next, while the "learning curve" is presently "concave up", it really ought to turn over and level off sometime soon.  As I argued in the post on the Venter Institute's fine technical achievement, they are already well beyond what will be economically interesting for the foreseeable future, which is probably only 10-50 kilobases (kB).  It isn't at all clear that assembling sDNA larger than 100 kB will be anything more than an academic demonstration.  The red octagon (hint!) is positioned at about 100 MB, which is in the range of a human chromosome.  Even assembling something that large, and then using it to fabricate an artificial human chromosome, is probably not technologically that useful.  I reserve a bit of judgement here in the event it turns out that actually building functioning human chromosomes from smaller pieces is problematic.  But really, why bother otherwise?

Figure 3.

Next, with the other curves in hand I couldn't help but compare the longest sDNA to gene assembly cost (beware the products of actual free time!).  (Update: Can't recall what I meant by this next sentence, so I struck it out.) Figure 3 may only be interesting because of what it doesn't show.  Note the reversed axis -- cost decreases to the right.

The assembly cost (inset) was generated simply by subtracting the oligo cost curve from the gene cost curve (see Figure 1 above) -- yes, I ignored the fact that those data are over different time periods.  There is no cost information available for any of the longest sDNA data, which all come from academic papers.  But the fact that gene assembly cost has been consistently halving every 18 months or so just serves to emphasize that the "acceleration" in the ratio of sDNA to assembly cost results from real improvements in processes and automation used to fabricate long sDNA.  I don't know that this is that deep an observation, but it does go some way towards providing additional quantitative estimates of progress in developing biological technologies.

"Learning Curves" and Genomics: Thoughts on the Future of Sequencing

(Update: 23 March 2009, I fixed various broken links.)

I have been wondering what additional information about future technology and markets can be discerned from trends in genome synthesis and sequencing ("Carlson Curves").  To see if there is anything there, I have been playing around with applying the idea of "learning curves" (also called "experience curves") to data on cost and productivity.

Learning curves generally are used to estimate decreases in costs that result from efficiencies that come from increases in production.  The more you make of something, the more efficient you become.  T.P. Wright famously used this idea in the 1930s to project decreases in cost as a function of increased airplane production.  The effect also shows up in a reduction of the cost of photovoltaic power as a function of cumulative production (see this figure, for example).

To start with here are some musings about the future of sequencing and the thousand dollar genome:

Figure 1 was generated using data on sequencing cost and productivity using commercially available instruments (click on the image for a larger pop-up).  I am not yet sure how seriously to take the plot, but it is interesting to think about the implications.

A few words on methodology: the data is sparse (see inset) in that there are not many points and data is not readily available in each category for each year.  This makes generating the plot of cost vs. productivity subject to estimation and some guesswork.  In particular, fitting a power law to the five productivity points, which are spread over only three logs, makes me uneasy.  The cost data isn't much better.  In the past I have cautioned both the private sector and governments from attempting to use this data to forecast trends.  But, really, everyone else is doing it, so why should I let good sense stop me?

Before going on, I should note that sequencing cost and productivity are related but not strictly correlated.  They are mostly independent variables at this point in time.  Reagents account for a substantial fraction of current sequencing costs, and increasing throughput and automation do not necessarily affect anything other than the number of bases one person can sequence in a day.  It is also important to point out that I am plotting productivity rather than cumulative production, and that both productivity and cost improvements include changes to new technology.  So the learning curve here is sort of an average over different technologies.  It is not a standard way to look at things, but it allows for a few interesting insights.

The blue line was generated by taking a ratio of fits to both the cost and productivity lines.  In other words, the blue line is basically data, and it suggests that for every order of magnitude improvement in productivity you get roughly a one and a half order of magnitude reduction in cost.  Here is the next point that makes me uneasy: I really have no reason to expect the current trends to maintain their present rates.  New sequencing technologies may well cause both productivity and cost changes to accelerate (though I would not expect them to slow -- see, for example, my previous post "The Thousand Dollar Genome").

Forging ahead, extending the trend out to the day when technology provides for the still-mythical Thousand Dollar Genome (TGD) provides an interesting insight.  At present rates, the TGD comes when an instrument allows for a productivity of one human genome per person-day.  It didn't have to be that way; slightly different doubling times (slopes) in the fits to cost and productivity would have produced a different result.  Frankly, I don't know if it means anything at all, but it did make me sit up and look more closely at the plot.  You could even call it a weak prediction about technological change -- weak because any deviation from the present average doubling rates would break the prediction.

But even if the present rates remain steady, that doesn't mean the actual cost of sequencing to the end user falls as quickly as it has.  Let's say somebody commercially produces an instrument that can actually provide a productivity of one genome per person-day.  How many of those instruments might make it onto the market?

Let's estimate that one percent of the US population wants to sign up for sequencing.  Those three million people would then require three million person-days worth of effort to sequence.  Operating 24/7 for one year, that would require just over 2700 instruments.  It will take some time before that many sequencers are available, which means that even if the technological capability exists there will be some -- probably substantial -- scarcity (the green circle on Figure1 ) keeping prices higher for some period.  Given that demand will certainly extend into Europe and Asia, further elevating prices, there is no reason to think the TGD will be a practical reality until there exists competition among providers.  This competition, in turn, will probably only emerge with the development of a diverse set of technologies capable of hitting the appropriate productivity threshold.

What does this imply for the sequencing market, and in particular for health care based on full genome sequencing?  First, costs will stay high until there are a large number of instruments in operation, and probably until there are many different technologies available.  Thus, if prices are determined solely by the market, the idea of sequencing newborns to give them a head start on maximizing their state of health will probably be out of reach for many years after the initial instrument is developed.  Market pricing probably means that sequencing will remain a tool of the wealthy for many, many years to come.

So, what other foolish, over-extended observations can I make based on fitting power laws to sparse data?  Just one more for the moment, and it actually doesn't depend so much on the actual data.  At a productivity of one genome per person-day, you really have to start thinking about the cost of that person.  Somebody will be running the machine, and that person draws a salary.  Let's say that this person earns a technician's wage, which amounts with benefits to $300/day.  All of a sudden (the trends are power laws, after all) that is 30% of the $1000 spent on sequencing the genome.  If the margin is 10-20% of the cost, then the actual sequencing, including financial loads such as depreciation of the instrument and interest, can cost only $500.  We are definitely a long time from seeing that price point.

I'll post on the learning curve for genome synthesis after I make more sense of it.

"The Big Squeeze: New Fundmantals for Food and Fuel Markets"

Bio-era recently released a new report describing our latest thinking about the future of food and fuel markets.  In the short term, we could be in for an even bumpier ride than we have seen so far.  Over the longer term, new technologies (biological and otherwise) will profoundly alter our ability to produce non-fossil fuels and will thus alter the structure of the economy.  But the sheer size of the petroleum and gasoline markets will continue dominate energy markets, and our economy, for many years to come.

Click on the image to  obtain the report -- as with previous releases you can purchase a copy from a print on demand service or download a PDF after registering.

Here is the Introduction:

In recent years, rising prices for agricultural and energy commodities have heightened interest in the economic fundamentals governing these markets. This report presents bio-era’s latest thinking on some of these fundamentals, and how they may be changing in unanticipated ways. Part of what we explore here concerns the interactions between the principal “long forces” affecting these markets, including the forces of climate change, the limits of conventional crude oil supply expansion, and the impacts of continued underlying growth in global populations and economies. Not surprisingly, we foresee these long forces acting in combination to place additional upward pressure on fuel and food prices, and we present a model for thinking about the dynamics at work in what we hope is a simple, but useful, way.

In addition, we also consider the growing linkages between agricultural and energy commodities, and how these linkages might affect current and future pricing dynamics within and between these markets. Under one, very specific set of conditions, we believe that price signaling between these markets could lead to a self-reinforcing feedback loop — which if left unchecked — could result in steadily escalating clearing prices.  The theoretical effect we describe is akin to an “evolutionary arms race” or a “red queen effect.” Should market circumstances ever give rise to the price dynamic described here, the implications could be far-reaching. Energy and food prices could rise steadily as a result, at great cost to the global economy. Continuing globalization might even be placed at risk. For these reasons, and because these theoretical possibilities have gone largely unnoticed to date, we felt it worth calling special attention to them here.

Here are the "Key Findings":

• Despite seven years of rising real prices for crude oil and a doubling of prices over the past year, global crude oil production has been nearly flat since 2005.
• The production of biofuels--in the form of ethanol fermented from sugars and starches, and biodiesel derived from vegetable oils and animal fats - has increased significantly and is now an important source of supply satisfying year over year increases in global liquid transportation fuel consumption.
• There are two principal connections between the crude oil and petroleum product markets and many of the so-called "soft" agricultural commodities such as grains, sugar, and vegetable oils:

1. an input-cost effect on agricultural commodity prices because oil and energy-intensive fertilizers account for a significant share of total production costs for most major crops;
2. an output-price effect prices of petroleum products such as gasoline or diesel oil set a floor price for agricultural commodities that can be converted into fuel substitutes.

• The first of these connections--the input-cost effect--is "one-way." The cost of petroleum will influence agricultural commodity prices over time, but the reverse is not true--the cost of agricultural commodities will have little or no effect on the costs of producing, transporting, and refining petroleum.
•  The second of these connections--the output-price effect--is increasingly "two-way." As volumes of agriculturally-derived fuels grow, expanding or withholding these volumes from the petroleum product markets directly influences both the price of petroleum products and the price of agricultural commodities.
• The result is competition between food and fuel end-use markets to price at a level sufficient to attract (and/or preserve access to) marginal supplies. Attempting to hold down food prices by restricting or redirecting feedstocks used to produce fuel, may cause fuel prices to rise. Similarly, attempting to hold down or lower fuel prices by increasing conventional biofuels production may increase food prices.

In the absence of a supply response from conventional crude oil, looking ahead, this dynamic is expected to continue until either global economic growth slows substantially, or additional supplies of non-conventional fuel substitutes - such as gas-to-liquids, coal-to-liquids, or biomass-to-liquids -- become available at meaningful scale. The necessary lead time on the latter option is at least 3-5 years.

"The Big Squeeze: New Fundamentals for Food and Fuel Markets",  A Special Bio-era Report, June 2008, By Stephen C. Aldrich, James Newcomb, Dr. Robert Carlson

More Pieces in the Distributed Biofuel Production Puzzle

Here are some additional musings on distributed production of biofuels and economies of scale:

Following on last month's launch of the efuel100 Microfueler, which seems to be a step toward distributed biofuel production, comes word of a couple of high school students who built a "Personal Automated Ethanol Fermenter and Distiller" (via Wired) for the 2008 Intel International Science and Engineering Fair.

In the video, Eric Hodenfield and Devin Bezdicek don't give a great deal of detail about their project, but I think it is fascinating that a couple of high school students decided to build a widget intended to facilitate personal fuel production.  Kudos to those two.  The device, like the Microfueler, is supposed to produce ethanol on a small scale, but both would be useful to produce Butanol instead if the appropriate microbe were handy, as I have written about before.

But why stop there?  What about home production of petroleum?  The TimesOnline this week has a short story about LS9, featuring Greg Pal, who suggests the company has a microbe with the capability to produce petroleum at $50 per barrel using Brazilian sugar as a feedstock.  (See my earlier post LS9 - "The Renewable Petroleum Company" - in the News.)  That number is interesting, because when I met Mr. Pal last fall at a retreat organized by Bio-era, he was more reticent about proposing a target price.  It would seem that the company is making decent progress, with Mr. Pal suggesting to the Times that LS9 hopes to be producing fuel on a commercial scale by 2011.

The Times article goes on to list some rather large sounding figures for the land that might be required to supply the US fuel weekly demand of ~140 million barrels using microbes; "205 square miles, an area roughly the size of Chicago".  Skipping the issue of whether there is enough sugar produced around the world to use as feedstock, the choice of paving Chicago over to crank out a weekly supply of renewable petroleum is a little odd.  Simplifying the calculation makes the whole problem seem quite reasonable.

First, consider that US daily oil consumption is something like 20 million barrels, according to the DOE.  So, if in practice biofuel production is no more efficient than LS9 projects, we will only require a little over 29 square miles of infrastructure or a plot about 5.4 miles on a side.

Spreading that out over all 50 states (ignoring the fact that population is not evenly distributed), we would need only ~.6 square miles per state.  Every city of decent size in this country has industrial parks bigger than that.  No problem there.

Taking the this approximation to the extreme -- say, to the "personal fermenter and distiller" high school science project -- dividing the 29 square milles by the 2008 US population of about 300,000,000 gives a silly figure of 10^-7 square miles per person; that's about a foot and a half on a side.  Switching to more rational units, it is ~40 cm on a side.  A family of four (on average) would therefore require roughly a square meter to produce a daily supply of fuel at present consumption levels.  Coincidentally, photos of the efuel100 Microfueler suggest it has a footprint of about a meter square.

Of course, only about two-thirds of total oil consumption goes to transportation, with much of that used by commercial operations, so that family of four would be overproducing even at a meter square (in the present ridiculous units of [production/day/person/area]).  Realistically, larger facilities would probably be employed to produce fuel or "renewable petroleum" for industrial purpposes.

How much the capital costs would be for the square meter of production capacity is up in the air.  The Microfueler lists at ~$10K.  I'll bet the high school students can beat that.

What's yours is yours...right?

Does information describing the pattern of genetic markers embedded in your genome, and even the sequence of your own DNA, belong to you?  I would say yes, but evidently the California Department of Public Health (DPH) has its doubts.

As reported in the LA Times, the DPH has sent cease and desist letters to 13 companies that offer direct-to-consumer genetic testing.  I am especially confused about this because if you have an extra $1-10 million sitting around, you can FedEx your DNA to any number of sequencing companies and have them send you an electronic copy of your sequence in a few months (see my earlier post, "The Million Dollar Genome").

The LA Times, The San Jose Mercury News, and The San Francisco Chronicle all report that the letters were sent following "consumer complaints" about "the price and accuracy of the results".  According to the Chronicle, "California law requirescompanies that conduct genetic testing to have those tests ordered by a licensed physician and to use laboratories that are both licensed by the state and have federal certification."

There appears to be some tension between the interpretation of tests ordered for diagnostic purposes, which probably should require a prescription, and sequencing or genotyping services that provide information about a consumer's genetic makeup.

From the Mercury News:

A spokeswoman for 23andMe, which has financial backing from Google Inc. and Genentech Inc., described the company as an "informational service."

"What we do is offer people information about their genetic makeup, including ancestry and applicable scientific research," spokeswoman Rachel Cohen said.

If physical or pharmaceutical intervention of some sort will be based the results of the test, you probably want a doctor involved in interpreting the results, particularly since correlations between genome sequence and health are still being elucidated.  But even when such correlations are strong, practicing physicians may not know what to do with the information.  As the LA Times points out, "Public health officials have urged consumers to be skeptical, pointing out that most of the research is in its earliest stages and that doctors have little training in interpreting the results."

This gets to the heart of the matter for people interested in knowing their own sequence.  It may be true that connections between relating sequence information and physiology may be sparse, but should that prevent consumers from having access to the raw information?  A physician may take some time to integrate genetic testing into daily practice: should we all be forced to wait until doctors are up to speed?  And what if you just want to know about the pattern of mutations that gives you insight into your ancestry, or are simply curious about the sequence of your own DNA?

Over at Wired News, Thomas Goetz has a few things to say on these issues to the California DPH:

[The cease and desist letters reflect] as much a cultural disagreement as a legal or regulatory one. That is, there is the assumption in the states' letters that, because genetic information has medical implications, the dissemination of this information must fall under their jurisdiction.

But there are, in fact, all sorts of areas in life that have medical implications that we don't consider the province of government -- a pregnancy test, most obviously. We neither want nor assume that doctors should have a gatekeeper role in establishing whether we are or are not pregnant, nor do we look to the state to protect us from that information. Pregnancy is a part of life, and it has all sorts of implications and ramifications. So too with DNA.

For Goetz, who reported for Wired last year on direct-to-consumer genetic testing, the DPH is inserting its bureaucratic nose, and a physician, where neither are wanted or needed:

This is not a dark art, province of the select few, as many physicians would have it. This is data. This is who I am. Frankly, it's insulting and a curtailment of my rights to put a gatekeeper between me and my DNA.

This is *my* data, not a doctor's. Please, send in your regulators when a doctor needs to cut me open, or even draw my blood. Regulation should protect me from bodily harm and injury, not from information that's mine to begin with.

So, bringing this back to the motivations for the cease and desist letters, what of the complaints about "price" and "accuracy"?

The 23andMe homepage advertises that the company provides:

A web-based service that helps you read and understand your DNA. After providing a saliva sample using an at-home kit, you can use our interactive tools to shed new light on your distant ancestors, your close family and most of all, yourself.

Nothing about diagnostics there.  But following the "Gene Journal" link leads to an "Odds Calculator" that will:

Help you put it all in perspective, allowing you to combine genetic information, age, and ethnicity to get an idea of which common health concerns are most likely to affect a person with your genetic profile. While the Odds Calculator is neither a medical diagnostic nor a substitute for medical advice, it can help you confront the bewildering array of health news reported in the mass media and help you decide where you may want to focus your attention.

(Note the specific caveat that the service is not "a medical diagnostic".)

Given the early stage of most efforts to link genomes with physiology, it would be very surprising if a small start-up could assemble the resources to "put it all in perspective".  But even if they don't have the ability to pull that off in a manner I would be satisfied with, it isn't clear that the state should be denying them the opportunity to try.

With respect to the "price" complaint, the last time I checked we are living in a society in which goods and services are priced according to what the market can bear.  Since neither private insurers nor the government is paying for these particular services, which are not intended to provide information to be used in healthcare, there does not appear to be a good argument that the state should care what the price is.

With respect to the "accuracy" complaint, it would seem that these companies are already trying to do business in a competitive environment -- if they aren't providing accurate information then presumably they will succumb to companies that provide better information to consumers.  Again, since this isn't a diagnostic service, it is not clear that the state should intrude in the transaction.

There are a great many snake oil peddlers and quacks out there who offer no caveats as to accuracy or effectiveness, and in comparison 23andMe and its competitors appear paragons of virtue.  Direct-to-consumer genetic information services are creating a new market, and there always bumps along the way in that endeavor, particularly when regulators decide they know more about technology than do innovators.  But it is a market. It is not, in priciple, directly related to health.  Caveat emptor.  Since when is this the concern of Department of Public Health?

2008 US Presidential Candidates' Positions on Biological Technologies

Biological technologies constitute a rapidly growing portion of the US economy.  When you add together drugs, plants, and industrial products, genetically modified organisms now contribute about $130 billion, or ~1%, to the US Gross Domestic Product, with sector revenues growing at 15-20% per year.

Given our reliance on new biological technologies to provide innovations in health care, food production, biofuels, materials, and myriad other areas, the policy preferences of the next President will have a profound impact on the future of the bio-economy.  What follows is a non-partisan, though highly biased (in favor of biological technologies), look at the positions that are easily accessible on the web.

Unfortunately, the candidate with the best explicit proposals just dropped out.  Science and technology receive far too little attention from the two supposed nominees, and neither have agreed to participate in a science-only debate, such as the one proposed by ScienceDebate2008.

Where do they stand?

Senator McCain's campaign web site contains very little in the way of specifics about the role of science and technology in driving the economy.  Here is his "Issues" page.  Spread through the sections on Healthcare, Climate Change, and the Space Program, there are brief mentions of the need to provide funding for innovation, and to keep regulation minimal.  But no specific policy proposals.  The AAAS "Candidates Compared" page on McCain has substantially more detail his positions than his actual web site, but it is still pretty minimal if you are looking for a guide to his eventual policy positions.  All in all, quite disheartening.

Grade in Biological Technologies: C, but only with today's rampant grade inflation.

Senator Obama's Technology page has improved a bit since the last time I checked it out.  Previously, based on the text, "technology" was synonymous with communications and the Internet.  Now, in addition to a broadly worded proposals on communications tech and education, the Senator now has a few paragraphs addressing funding for technologies to mitigate climate change and reforming immigration and the patent system.  On the Healthcare page, he expresses his enthusiasm for "Advancing the Biomedical Research Field" and promises to increase funding.  Hurrah.  At the bottom of the Healthcare and Environment pages there are reasonably detailed policy summaries available as PDFs.

Grade in Biological Technologies: B, but only because based on the language in the policy summaries I can imagine he is willing to listen.

Alas, the policy positions relevant to biological technologies of the lately departed (from the race) Senator Clinton are much more detailed and coherent than the two putative nominees.  The most specific proposal for biological technologies in the Clinton "Innovation Agenda" is this (even though it substantially underestimates the contribution to the US economy):

Increase investment in the non-health applications of biotechnology in order to fuel 21st century industry.The NIH dominates federal investments in biology and the life sciences, and there are only a few programs exploring non-health applications of biotech. And although biotechnology is a $50 billion industry, it is still in its infancy-and that is particularly true where the non-health applications are concerned. An example of non-health biotech is the creation of bacteria that can remove toxins from the environment, such as heavy metals or radioactive contaminants. Insights from biotechnology can accelerate growth in a large number of other fields-not unlike the way 20th century developments in the chemicals industry drove growth in oil and gas refining, pulp and paper, building materials, and pharmaceuticals. The NIH will have to work with other agencies to explore these non-health applications.

It is true that in this quotation nowhere present are the words "metabolic engineering", "synthetic biology", or "metagenomics", but in my reading of the text those fields are how we get to meaningful results from "non-health applications".

The Agenda also calls for, "Requiring that federal research agencies set aside at least 8% of their research budgets for discretionary funding of high-risk research."   This sounds great, and I am in favor of it, but I wonder if there are enough talented program managers out there to handle the load.

Finally, the Agenda calls for, "Increasing the NIH budget by 50% over 5 years and aim to double it over 10 years."  While I would like to cheer for this, the NIH has not been the paragon of innovation over the last couple of decades, with the vast majority of funding going to established investigators rather than young people.  Even with an increase in funding, I don't see the NIH investing in synthetic biology any time soon.

Grade in Biological Technologies: A, and head of the class, but not "+" because while she addressed many of the relevant I am afraid the Senator didn't use the actual key words on the checklist.  That's how you grade essays, after all.

But, of course, even if she is as much of a policy wonk as her husband, Senator Clinton did not write the essay.  Somebody else did, and we can only hope that Obama or McCain 1) immediately picks up whomever was responsible for Clinton's excellent policy positions, and 2) listens to that person...

Farming and Economies of Scale

Biological technologies constitute a rapidly growing portion of the U.S. GDP, about 1%, or $150 billion, as of early 2008.  If biological processes continue to displace chemical processes in industry, we might expect all of industry to look more like biology.  While most industrial chemistry is carried out in large facilities, throughout the living world big organisms are rare.  Yes, we have a few examples of gigantic trees and charismatic megafauna, but very few creatures are larger than about a meter.  The vast majority of biomass on Earth consists of microbes.

Physics and economics both dictate that some kinds of industrial processes are best implemented at scale.  Anything involving large amounts of heat, particularly when there are large masses of water involved, generally benefits from increased scale because energy can be more easily contained and recycled.  Energy is more easily contained with small surface to volume ratios; big vessels and pipes loose less heat.  Similarly, benefits of scale can be found in big pipes have less fluid resistance and are easier to pump things through.

Biology tends to do things smaller.  Thus when I muse about the possibility of distributed biological manufacturing, particularly the potential of distributed biofuel production, I am inspired by the fact that biological processing tends to be networked or mobile.  Ecosystems are full of material transport and exchange, a large part of which is mediated by animals that wander around eating in one place and crapping in another.

As transportation costs increase with the price of oil, moving both food and manufactured goods around will be ever more expensive.  At some point, we should expect food to be cheaper when grown locally and transported shorter distances.

According to an Op-Ed in The New York Times a couple of weeks ago, we are well past the point where small farms are more economical than large ones.  In "Change We Can Stomach", Dan Barber writes that:

...Small farms are the most productive on earth. A four-acre farm in theUnited States nets, on average, $1,400 per acre; a 1,364-acre farm nets $39 an acre. Big farms have long compensated for the disequilibrium with sheer quantity. But their economies of scale come from mass distribution, and with diesel fuel costing more than $4 per gallon in many locations, it’s no longer efficient to transport food 1,500 miles from where it’s grown.

Mr. Brown doesn't cite any sources for these numbers, but it is something I will be looking into as my book finally gets wrapped up.  It is generally asserted by economists that 1) large farms are a better use of land, and require less labor per unit output, than small farms, and 2) labor has a higher value in cities when employed in manufacturing.

But cows are cheap and mobile, and if biological technology ever gets to the point of using cows to produce industrial products, then the economies of scale could be radically shifted.  I am put in mind of a short story by David Brin in which not only cows are used as biomanufacturing platforms, but people are, too.  Here's to hoping that is some years off.