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How Competition Improves DNA Sequencing

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The technology that enables reading DNA is changing very quickly.  I've chronicled how price and productivity are each improving in a previous post; here I want to try to get at how the diversity of companies and technologies is contributing to that improvement.

As I wrote previously, all hell is breaking loose in sequencing, which is great for the user.  Prices are falling and the capabilities of sequencing instruments are skyrocketing.  From an analytical perspective, the diversity of platforms is a blessing and a curse.  There is a great deal more data than just a few years ago, but it has become quite difficult to directly compare instruments that produce different qualities of DNA sequence, produce different read lengths, and have widely different throughputs.

I have worked for many years to come up with intuitive metrics to aid in understanding how technology is changing.  Price and productivity in reading and writing DNA are pretty straightforward.  My original paper on this topic (PDFalso looked at the various components of determining protein structures, which, given the many different quantifiable tasks involved, turned out to be a nice way to encapsulate a higher level look at rates of change.

In 2007, with the publication of bio-era's Genome Synthesis and Design Futures, I tried to get at how improvements in instrumentation were moving us toward sequencing whole genomes. The two axes of the relevant plot were 1) read length -- the length of each contiguous string of bases read by an instrument, critical to accurate assembly of genomes or chromosomes that can be hundreds of millions of bases long -- and 2) the daily throughput per instrument -- how much total DNA each instrument could read.  If you have enough long reads you can use this information as a map to assemble many shorter reads into the contiguous sequence.

Because there weren't very many models of commercially available sequencers in 2007, the original plot didn't have a lot of data on it (the red squares and blue circles below).  But the plot did show something interesting, which was that two general kinds of instruments were emerging at that time: those that produced long reads but had relatively limited throughput, and those that produced short reads but could process enormous amounts of sequence per day.  The blue dots below were data from my original paper, and the red squares were derived from a Science news article in 2006 that looked at instruments said to be emerging over the next year or so.

I have now pulled performance estimates out of several papers assessing instruments currently on the market and added them to the plot (purple triangles).  The two groupings present in 2007 are still roughly extant, though the edges are blurring a bit. (As with the price and productivity figures, I will publish a full bibliography in a paper later this year.  For now, this blog post serves as the primary citation for the figure below.)

I am still trying to sort out the best way to represent the data (I am open to suggestions about how do it better).  At this point, it is pretty clear that the two major axes are insufficient to truly understand what is going on, so I have attempted to add some information regarding the release schedules of new instruments.  Very roughly, we went from a small number of first generation instruments in 2003 to a few more real instruments in 2006 that performed a little better in some regards, plus a few promised instruments that didn't work out for one reason or another.  However, starting in about 2010, we began to see seriously improved instruments being released on an increasingly rapid schedule.  This improvement is the result of competition not just between firms, but also between technologies.  In addition, some of what we are seeing is the emergence of instruments that have niches; long reads but medium throughput, short reads but extraordinary throughput -- combine these two capabilities and you have the ability to crank out de novo sequences at pretty remarkable rate.  (For reference, the synthetic chromosome Venter et al published a few years ago was about one million bases; human chromosomes are in the range of 60 to 250 million bases.)

Carlson_Seq_Performance_Comp_2012a.png
And now something even more interesting is going on.  Because platforms like PacBio and IonTorrent can upgrade internal components used in the actual sequencing, where those components include hardware, software, and wetware, revisions can result in stunning performance improvements.  Below is a plot with all the same data as above, with the addition of one revision from PacBio.  It's true that the throughput per instrument didn't change so much, but such long read lengths mean you can process less DNA and still rapidly produce high resolution sequence, potentially over megabases (modulo error rates, about which there seems to be some vigorous discussion).  This is not to say that PacBio makes the best overall instrument, nor that the company will be commercially viable, but rather that the competitive environment is producing change at an extraordinary rate.

Carlson_Seq_Performance_Comp_2012b.png
If I now take the same plot as above and add a single (putative) MinION nanopore sequencer from Oxford Nanopore (where I have used their performance claims from public presentations; note the question mark on the date), the world again shifts quite dramatically.  Oxford also claims they will ship GridION instruments that essentially consist of racks of MinIONs, but I have not even tried to guess at the performance of that beast.  The resulting sequencing power will alter the shape of the commercial sequencing landscape.  Illumina and Life are not sitting still, of course, but have their own next generation instruments in development.  Jens Gundlach's (PDF) team at the University of Washington has demonstrated a nanopore that is argued to be better than the one Oxford uses, and I understand commercialization is proceeding rapidly, though of course Oxford won't be sitting still either.

One take home message from this, which is highlighted by taking the time to plot this data, is that over the next few years sequencing will become highly accurate, fast, and commonplace.  With the caveat that it is difficult to predict the future, continued competition will result in continued price decreases.

A more speculative take home emerges if you consider the implications of the MinION.  That device is described as a disposable USB sequencer.  If it -- or anything else like it -- works as promised, then some centralized sequencing operations might soon reach the end of their lives.  There are, of course, different kinds of sequencing operations.  If I read the tea leaves correctly, Illumina just reported that its clinical sequencing operations brought in about as much revenue as their other operations combined, including instrument sales.  That's interesting, because it points to two kinds of revenue: sales of boxes and reagents that enable other people to sequence, and certified service operations that provide clinically relevant sequence data.  At the moment, organizations like BGI appear to be generating revenue by sequencing everything under the sun, but cheaper and cheaper boxes might mean that the BGI operations outside of clinical sequencing aren't cost effective going forward.  Once the razors (electric, disposable, whatever) get cheap enough, you no longer bother going to the barber for a shave.

I will continue to work with the data in an effort to make the plots simpler and therefore hopefully more compelling.
Here are updated cost and productivity curves for DNA sequencing and synthesis.  Reading and writing DNA is becoming ever cheaper and easier.  The Economist and others call these "Carlson Curves", a name I am ambivalent about but have come to accept if only for the good advertising.  I've been meaning to post updates for a few weeks; the appearance today of an opinion piece at Wired about Moore's Law serves as a catalyst to launch them into the world.  In particular, two points need some attention, the  notions that Moore's Law 1) is unplanned and unpredictable, and 2) somehow represents the maximum pace of technological innovation.

DNA Sequencing Productivity is Skyrocketing

First up: the productivity curve.  Readers new to these metrics might want to have a look at my first paper on the subject, "The Pace and Proliferation of Biological Technologies" (PDF) from 2003, which describes why I chose to compare the productivity enabled by commercially available sequencing and synthesis instruments to Moore's Law.  (Briefly, Moore's Law is a proxy for productivity; more transistors putatively means more stuff gets done.)  You have to choose some sort of metric when making comparisons across such widely different technologies, and, however much I hunt around for something better, productivity always emerges at the top.

It's been a few years since I updated this chart.  The primary reason for the delay is that, with the profusion of different sequencing platforms, it became somewhat difficult to compare productivity [bases/person/day] across platforms.  Fortunately, a number of papers have come out recently that either directly make that calculation or provide enough information for me to make an estimate.  (I will publish a full bibliography in a paper later this year.  For now, this blog post serves as the primary citation for the figure below.)

carlson_productivity_feb_2013.png
Visual inspection reveals a number of interesting things.  First, the DNA synthesis productivity line stops in about 2008 because there have been no new instruments released publicly since then.  New synthesis and assembly technologies are under development by at least two firms, which have announced they will run centralized foundries and not sell instruments.  More on this later.

Second, it is clear that DNA sequencing platforms are improving very rapidly, now much faster than Moore's Law.  This is interesting in itself, but I point it out here because of the post today at Wired by Pixar co-founder Alvy Ray Smith, "How Pixar Used Moore's Law to Predict the Future".  Smith suggests that "Moore's Law reflects the top rate at which humans can innovate. If we could proceed faster, we would," and that "Hardly anyone can see across even the next crank of the Moore's Law clock."

Moore's Law is a Business Model and is All About Planning -- Theirs and Yours

As I have written previously, early on at Intel it was recognized that Moore's Law is a business model (see the Pace and Proliferation paper, my book, and in a previous post, "The Origin of Moore's Law").  Moore's Law was always about economics and planning in a multi-billion dollar industry.  When I started writing about all this in 2000, a new chip fab cost about $1 billion.  Now, according to The Economist, Intel estimates a new chip fab costs about $10 billion.  (There is probably another Law to be named here, something about exponential increases in cost of semiconductor processing as an inverse function of feature size.)  Nobody spends $10 billion without a great deal of planning, and in particular nobody borrows that much from banks or other financial institutions without demonstrating a long-term plan to pay off the loan.   Moreover, Intel has had to coordinate the manufacturing and delivery of very expensive, very complex semiconductor processing instruments made by other companies.  Thus Intel's planning cycle explicitly extends many years into the future; the company sees not just the next crank of the Moore's Law clock, but several cranks.  New technology has certainly been required to achieve these planning goals, but that is just part of the research, development, and design process for Intel.  What is clear from comments by Carver Mead and others is that even if the path was unclear at times, the industry was confident that they could to get to the next crank of the clock.

Moore's Law served a second purpose for Intel, and one that is less well recognized but arguably more important; Moore's Law was a pace selected to enable Intel to win.  That is why Andy Grove ran around Intel pushing for financial scale (see "The Origin of Moore's Law").  I have more historical work to do here, but it is pretty clear that Intel successfully organized an entire industry to move at a pace only it could survive.  And only Intel did survive.  Yes, there are competitors in specialty chips and in memory or GPUs, but as far as high volume, general CPUs go, Intel is the last man standing.  Finally, and alas I don't have a source anywhere for this other than hearsay, Intel could have in fact gone faster than Moore's Law.  Here is the hearsay: Gordon Moore told Danny Hillis who told me that Intel could have gone faster.  (If anybody has a better source for that particular point, give me a yell on Twitter.)  The inescapable conclusion from all this is that the management of Intel made a very careful calculation.  They evaluated product roll-outs to consumers, the rate of new product adoption, the rate of semiconductor processing improvements, and the financial requirements for building the next chip fab line, and then set a pace that nobody else could match but that left Intel plenty of headroom for future products.  It was all about planning.

The reason I bother to point all this out is that Pixar was able to use Moore's Law to "predict the future" precisely because Intel meticulously planned that future.  (Calling Alan Kay: "The best way to predict the future is to invent it.")  Which brings us back to biology.  Whereas Moore's Law is all about Intel and photolithography, the reason that productivity in DNA sequencing is going through the roof is competition among not just companies but among technologies.  And we only just getting started.  As Smith writes in his Wired piece, Moore's Law tells you that "Everything good about computers gets an order of magnitude better every five years."  Which is great: it enabled other industries and companies to plan in the same way Pixar did.  But Moore's Law doesn't tell you anything about any other technology, because Moore's Law was about building a monopoly atop an extremely narrow technology base.  In contrast, there are many different DNA sequencing technologies emerging because many different entrepreneurs and companies are inventing the future.

The first consequence of all this competition and invention is that it makes my job of predicting the future very difficult.  This emphasizes the difference between Moore's Law and Carlson Curves (it still feels so weird to write my own name like that): whereas Intel and the semiconductor industry were meeting planning goals, I am simply keeping track of data.  There is no real industry-wide planning in DNA synthesis or sequencing, other than a race to get to the "$1000 genome" before the next guy.  (Yes, there is a vague road-mappy thing promoted by the NIH that accompanied some of its grant programs, but there is little if any coordination because there is intense competition.)

Biological Technologies are Hard to Predict in Part Because They Are Cheaper than Chips

Compared to other industries, the barrier to entry in biological technologies is pretty low.  Unlike chip fabs, there is nothing in biology that costs $10 billion commercially, nor even $1 billion.  (I have come to mostly disbelieve pharma industry claims that developing drugs is actually that expensive, but that is another story for another time.)  The Boeing 787 reportedly cost $32 billion to develop as of 2011, and that is on top of a century of multi-billion dollar aviation projects that had to come before the 787.

There are two kinds of costs that are important to distinguish here.  The first is the cost of developing and commercializing a particular product.  Based on the money reportedly raised and spent by Life, Illumina, Ion Torrent (before acquisition), Pacific Biosciences, Complete Genomics (before acquisition), and others, it looks like developing and marketing second-generation sequencing technology can cost upwards of about $100 million.  Even more money gets spent, and lost, in operations before anybody is in the black.  My intuition says that the development costs are probably falling as sequencing starts to rely more on other technology bases, for example semiconductor processing and sensor technology, but I don't know of any real data.  I would also guess that nanopore sequencing, should it actually become a commercial product this year, will have cost less to develop than other technologies, but, again, that is my intuition based on my time in clean rooms and at the wet bench.  I don't think there is great information yet here, so I will suspend discussion for the time being.

The second kind of cost to keep in mind is the use of new technologies to get something done.  Which brings in the cost curve.  Again, the forthcoming paper will contain appropriate references.
carlson_cost per_base_oct_2012.png
The cost per base of DNA sequencing has clearly plummeted lately.  I don't think there is much to be made of the apparent slow-down in the last couple of years.  The NIH version of this plot has more fine grained data, and it also directly compares the cost of sequencing with the cost per megabyte for memory, another form of Moore's Law.  Both my productivity plot above and the NIH plot show that sequencing has at times improved much faster than Moore's Law, and generally no slower.

If you ponder the various wiggles, it may be true that the fall in sequencing cost is returning to a slower pace after a period in which new technologies dramatically changed the market.  Time will tell.  (The wiggles certainly make prediction difficult.)  One feature of the rapid fall in sequencing costs is that it makes the slow-down in synthesis look smaller; see this earlier post for different scale plots and a discussion of the evaporating maximum profit margin for long, double-stranded synthetic DNA (the difference between the orange and yellow lines above).

Whereas competition among companies and technologies is driving down sequencing costs, the lack of competition among synthesis companies has contributed to a stagnation in price decreases.  I've covered this in previous posts (and in this Nature Biotech article), but it boils down to the fact that synthetic DNA has become a commodity produced using relatively old technology.

Where Are We Headed?

Now, after concluding that the structure of the industry makes it hard to prognosticate, I must of course prognosticate.  In DNA sequencing, all hell is breaking loose, and that is great for the user.  Whether instrument developers thrive is another matter entirely.  As usual with start-ups and disruptive technologies, surviving first contact with the market is all about execution.  I'll have an additional post soon on how DNA sequencing performance has changed over the years, and what the launch of nanopore sequencing might mean.

DNA synthesis may also see some change soon.  The industry as it exists today is based on chemistry that is several decades old.  The common implementation of that chemistry has heretofore set a floor on the cost of short and long synthetic DNA, and in particular the cost of synthetic genes.  However, at least two companies are claiming to have technology that facilitates busting through that cost floor by enabling the use of smaller amounts of poorer quality, and thus less expensive, synthetic DNA to build synthetic genes and chromosomes.

Gen9 is already on the market with synthetic genes selling for something like $.07 per base.  I am not aware of published cost estimates for production, other than the CEO claiming it will soon drop by orders of magnitude.  Cambrian Genomics has a related technology and its CEO suggests costs will immediately fall by 5 orders of magnitude.  Of course, neither company is likely to drop prices so far at the beginning, but rather will set prices to undercut existing companies and grab market share.  Assuming Gen9 and Cambrian don't collude on pricing, and assuming the technologies work as they expect, the existence of competition should lead to substantially lower prices on genes and chromosomes within the year.  We will have to see how things actually work in the market.  Finally, Synthetic Genomics has announced it will collaborate with IDT to sell synthetic genes, but as far as I am aware nothing new is actually shipping yet, nor have they announced pricing.

So, supposedly we are soon going to have lots more, lots cheaper DNA.  But you have to ask yourself who is going to use all this DNA, and for what.  The important business point here is that both Gen9 and Cambrian Genomics are working on the hypothesis that demand will increase markedly (by orders of magnitude) as the price falls.  Yet nobody can design a synthetic genetic circuit with more than a handful of components at the moment, which is something of a bottleneck on demand.  Another option is that customers will do less up-front predictive design and instead do more screening of variants.  This is how Amyris works -- despite their other difficulties, Amyris does have a truly impressive metabolic screening operation -- and there are several start-ups planning to provide similar (or even improved) high-throughput screening services for libraries of metabolic pathways.  I infer this is the strategy at Synthetic Genomics as well.  This all may work out well for both customers and DNA synthesis providers.  Again, I think people are working on an implicit hypothesis of radically increased demand, and it would be better to make the hypothesis explicit in part to identify the risk of getting it wrong.  As Naveen Jain says, successful entrepreneurs are good at eliminating risk, and I worry a bit that the new DNA synthesis companies are not paying enough attention on this point.

There are relatively simple scaling calculations that will determine the health of the industry.  Intel knew that it could grow financially in the context of exponentially falling transistor costs by shipping exponentially more transistors every quarter -- that is the business model of Moore's Law.  Customers and developers could plan product capabilities, just as Pixar did, knowing that Moore's Law was likely to hold for years to come.  But that was in the context of an effective pricing monopoly.  The question for synthetic gene companies is whether the market will grow fast enough to provide adequate revenues when prices fall due to competition.  To keep revenues up, they will then have to ship lots of bases, probably orders of magnitudes more bases.  If prices don't fall, then something screwy is happening.  If prices do fall, they are likely to fall quickly as companies battle for market share.  It seems like another inevitable race to the bottom.  Probably good for the consumer; probably bad for the producer.

(Updated)  Ultimately, for a new wave of DNA synthesis companies to be successful, they have to provide the customer something of value.  I suspect there will be plenty of academic customers for cheaper genes.  However, I am not so sure about commercial uptake.  Here's why: DNA is always going to be a small cost of developing a product, and it isn't obvious making that small cost even cheaper helps your average corporate lab.

In general, the R part of R&D only accounts for 1-10% of the cost of the final product.  The vast majority of development costs are in polishing up the product into something customers will actually buy.  If those costs are in the neighborhood of $50-100 million, the reducing the cost of synthetic DNA from $50,000 to $500 is nice, but the corporate scientist-customer is more worried about knocking a factor of two, or an order of magnitude, off the $50 million.  This means that in order to make a big impact (and presumably to increase demand adequately) radically cheaper DNA must be coupled to innovations that reduce the rest of the product development costs.  As suggested above, forward design of complex circuits is not going to be adequate innovation any time soon.  The way out here may be high-throughput screening operations that enable testing many variant pathways simultaneously.  But note that this is not just another hypothesis about how the immediate future of engineering biology will change, but another unacknowledged hypothesis.  It might turn out to be wrong.

The upshot, just as I wrote in 2003, is that the market dynamics of biological technologies will  remain difficult to predict precisely because of the diversity of technology and the difficulty of the tasks at hand.  We can plan on prices going down; how much, I wouldn't want to predict.

Upcoming Talks in New York Area

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I'm headed to the New York area this week and will be giving three talks (two of which are open to the public).

May 4th, Noon, Princeton University: "Biology is Technology: Garage Biology, Microbrewing and the Economic Drivers of Distributed Biological Production"

May 5th, 1 pm, Genspace (33 Flatbush Avenue, Brooklyn): "Biology Is Technology: The Implications of Global Biotechnology"

May 7th-8th, The Hastings Institute, "Progress and Prospects for Microbial Biofuels" for the next round of conversations on ethics, synthetic biology, and public policy.  The previous round of conversations is captured in this set of essays, which includes my contribution, "Staying Sober About Science" (free after registration).

Synthetic biology and "green" explosives

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Here is my article with Dan Grushkin for Slate and Future Tense on "The Military's Push to Green Our Explosives", about using synthetic biology to make things go boom.  We had way more material than space, and we should probably write something else on the topic.

Here are the first three 'graphs:

Last year, when the United States military debuted footage of an iridescent drone the size and shape of a hummingbird buzzing around a parking lot, the media throated a collective hooah! Time magazine even devoted a cover to it. Meanwhile, with no fanfare at all--despite the enormous potential to reshape modern warfare--the military issued a request for scientists to find ways to design microbes that could produce explosives for weapons. Imagine a vat of genetically engineered yeast that produces chemicals for bombs and missiles instead of beer.

The request takes advantage of new research in synthetic biology, a science that applies engineering principles to genetics. To its humanitarian credit, in the field's short existence, scientists have genetically programmed bacteria and yeast to cheaply produce green jet fuels (now being tested by major airplane makers) and malaria medicines (scheduled for market in 2013). It's an auspicious beginning for a science that portends to revolutionize how we make things. In the future, we may harness cells to self-assemble into far more complex objects like cell phone batteries or behave like tiny programmable computers. The promise, however, comes yoked with risks.

The techniques that make synthetic biology such a powerful tool for positive innovation may be also used for destruction. The military's new search for biologically brewed explosives threatens to reopen an avenue of research that has been closed for 37 years: biotechnology developed for use in war.


Deliberating Over the Danger from H5N1

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Here is an article from The Scientist in which I and others debate the wisdom of pursuing and publishing research into influenza viruses: "Deliberating Over Danger".

For background, see my earlier post "Censoring Science is Detrimental to Security".

Playing God, from BBC Horizons

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Here is the full video of the BBC Horizons show on synthetic biology that aired earlier this year.  My bit starts at 38:30, but you would do well to watch the whole thing.  Oh, and spidergoats!

The Arrival of Nanopore Sequencing

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(Update 1 March: Thanks to the anonymous commenter who pointed out the throughput estimates for existing instruments were too low.)

You may have heard a little bit of noise about nanopore sequencing in recent weeks.  After many years of development, Oxford Nanopore promises that by the end of the year we will be able to read DNA sequences by threading them through the eye of a very small needle.

How It Works: Directly Reading DNA

The basic idea is not new: as a long string of DNA pass through a small hole, its components -- the bases A, T, G, and C -- plug that hole to varying degrees.  As they pass through the hole, in this case an engineered pore protein derived from one found in nature, each base has slightly different interactions with the walls of the pore.  As a result, while passing through the pore each base lets different numbers of salt ions through, which allows one to distinguish between the bases by measuring changes in electrical current.  Because this method is a direct physical interrogation of the chemical structure of each base, it is in principal much, much faster than any of the indirect sequencing technologies that have come before.

There have been a variety of hurdles to clear to get nanopore sequencing working.  First you have to use a pore that is small enough to produce measurable changes in current.  Next the speed of the DNA must be carefully controlled so that the signal to noise ratio is high enough.  The pore must also sit in an insulating membrane of some sort, surrounded by the necessary electrical circuitry, and to become a useful product the whole thing must be easily assembled in an industrial manner and be mechanically stable through shipping and use.

Oxford Nanopore claims to have solved all those problems.  They recently showed off a disposable version of their technology -- called the MinIon -- containing 512 pores built into a disposable USB stick.  This puts to shame the Lava Amp, my own experiment with building a USB peripheral for molecular biology.  Here is one part I find extremely impressive -- so impressive it is almost hard to believe: Oxford claims they have reduced the sample handling to single (?) pipetting step.  Clive Brown, Oxford CTO, says "Your fluidics is a Gilson."  (A "Gilson" would be a brand of pipetter.)  That would be quite something.

I've spent a good deal of my career trying to develop simple ways of putting biological samples into microfluidic doo-dads of one kind or another.  It's never trivial, it's usually a pain in the ass, and sometimes it's a showstopper.  Blood, in particular, is very hard to work with.  If Oxford has made this part of the operation simple, then they have a winning technology just based on everyday ease of use -- what sometimes goes by the labels of "user experience" or "human factors".  Compared to the complexity of many other laboratory protocols, it would be like suddenly switching from MS DOS to OS X in one step.

How Well Does it Work?

The challenge for fast sequencing is to combine throughput (bases per hour) with read length (the number of contiguous bases read in one go).  Existing instruments have throughputs in the range of 10-55,000 megabases/day and read lengths from tens of bases to about 800 bases.  (See chart below.)  Nick Loman reports that using the MinIon Oxford has already run DNA of 5000 to 100,000 bases (5 kB to 100 kB) at speeds of 120-1000 bases per minute per pore, though accuracy suffers above 500 bases per minute.  So a single USB stick can run easily run at 150 megabases (MB) per hour, which basically means you can sequence full-length eukaryotic chromosomes in about an hour.  Over the next year or so, Oxford will release the GridIon instrument that will have 4 and then 16 times as many pores.  Presumably that means it will be 16 times as fast.  The long read lengths mean that processing the resulting sequence data, which usually takes longer than the actual sequencing itself, will be much, much faster.

This is so far beyond existing commercial instruments that it sounds like magic.  Writing in Forbes, Matthew Herper quotes Jonathan Rothberg, of sequencing competitor Ion Torrent, as saying "With no data release how do you know this is not cold fusion? ... I don't believe it."  Oxford CTO Clive Brown responded to Rothberg in the comments to Herper's post in a very reasonable fashion -- have a look.

Of course I want to see data as much as the next fellow, and I will have to hold one of those USB sequencers in my own hands before I truly believe it.  Rothberg would probably complain that I have already put Oxford on the "performance tradeoffs" chart before they've shipped any instruments.  But given what I know about building instruments, I think immediately putting Oxford in the same bin as cold fusion is unnecessary.

Below is a performance comparison of sequencing instruments originally published by Bio-era in Genome Synthesis and Design Futures in 2007.  (Click on it for a bigger version.)  I've hacked it up to include the approximate performance range of 2nd generation sequencers from Life, Illumina, etc, as well for a single MinIon.  That's one USB stick, with what we're told is a few minutes worth of sample prep.  How many can you run at once?  Notice the scale on the x-axis, and the units on the y-axis.  If it works as promised, the MinIon is so vastly better than existing machines that the comparison is hard to make.  If I replotted that data with log axis along the bottom then all the other technologies would be cramped up together way off to the left. (The data comes from my 2003 paper, The Pace and Proliferation of Biological Technologies (PDF), and from Service, 2006, The Race for the $1000 Genome).
 
Carlson_sequencer_performanc_2012.png The Broader Impact

Later this week I will try to add the new technologies to the productivity curve published in the 2003 paper.  Here's what it will show: biological technologies are improving at exceptional paces, leaving Moore's Law behind.  This is no surprise, because while biology is getting cheaper and faster, the density of transistors on chips is set by very long term trends in finance and by SEMATECH; designing and fabricating new semiconductors is crazy expensive and requires coordination across an entire industry. (See The Origin of Moore's Law and What it May (Not) Teach Us About Biological Technologies.)  In fact, we should expect biology to move much faster than semiconductors. 

Here are a few graphs from the 2003 paper:

...The long term distribution and development of biological technology is likely to be largely unconstrained by economic considerations. While Moore's Law is a forecast based on understandable large capital costs and projected improvements in existing technologies, which to a great extent determined its remarkably constant behavior, current progress in biology is exemplified by successive shifts to new technologies. These technologies share the common scientific inheritance of molecular biology, but in general their implementations as tools emerge independently and have independent scientific and economic impacts. For example, the advent of gene expression chips spawned a new industrial segment with significant market value. Recombinant DNA, gel and capillary sequencing, and monoclonal antibodies have produced similar results. And while the cost of chip fabs has reached upwards of one billion dollars per facility and is expected to increase [2012 update: it's now north of $6 billion], there is good reason to expect that the cost of biological manufacturing and sequencing will only decrease. [Update 2012: See "New Cost Curves" for DNA synthesis and sequencing.]

These trends--successive shifts to new technologies and increased capability at decreased cost--are likely to continue. In the fifteen years that commercial sequencers have been available, the technology has progressed ... from labor intensive gel slab based instruments, through highly automated capillary electrophoresis based machines, to the partially enzymatic Pyrosequencing process. These techniques are based on chemical analysis of many copies of a given sequence. New technologies under development are aimed at directly reading one copy at a time by directly measuring physical properties of molecules, with a goal of rapidly reading genomes of individual cells.  While physically-based sequencing techniques have historically faced technical difficulties inherent in working with individual molecules, an expanding variety of measurement techniques applied to biological systems will likely yield methods capable of rapid direct sequencing.

Cue nanopore sequencing. 

A few months ago I tweeted that I had seen single strand DNA sequence data generated using a nanopore -- it wasn't from Oxford. (Drat, can't find the tweet now.)  I am certain there are other labs out there making similar progress.  On the commercial front, Illumina is an investor in Oxford, and Life has invested in Genia.  As best I can tell, once you get past the original pore sequencing IP, which it appears is being licensed broadly, there appear to be many measurement approaches, many pores, and many membranes that could be integrated into a device.  In other words, money and time will be the primary barriers to entry.

(For the instrumentation geeks out there, because the pore is larger than a single base, the instrument actually measures the current as three bases pass through the pore.  Thus you need to be able to distinguish 4^3=64 levels of current, which Oxford claims they can do.  The pore set-up I saw in person worked the same way, so I certainly believe this is feasible.  Better pores and better electronics might reduce the physical sampling to 1 or 2 bases eventually, which should result in faster instruments.)

It may be that Oxford will have a first mover advantage for nanopore instruments, and it may be that they have amassed sufficient additional IP to make it rough for competitors.  But, given the power of the technology, the size of the market, and the number of academic competitors, I can't see that over the long term this remains a one-company game.

Not every sequencing task has the same technical requirements, so instruments like the Ion Torrent won't be put to the curbside.  And other technologies will undoubtedly come along that perform better in some crucial way than Oxford's nanopores.  We really are just at the beginning of the revolution in biological technologies.  Recombinant DNA isn't even 40 years old, and the electronics necessary for nanopore measurements only became inexpensive and commonplace in the last few years.  However impressive nanopore sequencing seems today, the greatest change is yet to come.

Bumps for Biofuels and Growing Pains for the BioEconomy

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I found this post, written in early 2008, for some reason sitting unpublished in my archives.  It is just as relevant today, now that we are through the worst of the economic meltdown, so I'll push the "publish" button in just a moment.  I updated the revenue numbers for the US, but otherwise it is unchanged.  I note that high farm prices are again putting pressure on the amount of land in the conservation reserve program.

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Just as we reach the point where biological technologies can begin to economically replace the industrial chemistry we have relied on for the last two centuries, the price of raw materials is going through the roof.  As explored in my recent article, "Laying the Foundations for a Bio-Economy", the contribution of "genetically modified stuff" to the U.S. economy already amounts to the equivalent of more than 2% of GDP, or north of $300 billion.  [See the Biodesic 2011 Bioeconomy Update for the updated revenue numbers.]  About 80% of this total is from agriculture and industrial products, where revenues from the latter are growing 15-20% a year.  But as more products of industrial biotechnology hit the market, they will compete for more expensive feedstock resources.

The New York Times carried two stories on 9 April that illustrate some of the attendant issues.  In, "Harnessing Biology, and Avoiding Oil, for Chemical Goods", Yudhijit Bhattacharjee" gives a short summary of the shift from chemistry to biology for producing everything from plastic to fuel.  I've written here before about DuPont's success with Sorona, a plastic made using corn processed by engineered bacteria.  By itself, Sorona is already a billion-dollar product.  It seems DuPont has discovered additional uses for materials that are produced using biology:

The payoffs from developing biobased chemicals could be huge and unexpected, said Dr. John Pierce, DuPont's vice president for applied biosciences-technology. He pointed to DuPont's synthesis of propanediol, which was pushed along by the company's goal to use the chemical to make Sorona, a stain-resistant textile that does not lose color easily.

Soon DuPont scientists realized that bioderived propanediol could also be used as an ingredient in cosmetics and products for de-icing aircraft. The high-end grades that are now used in cosmetics are less irritating than traditional molecules, Dr. Pierce said, and the industrial grade used in de-icing products is biodegradable, which makes it better than other options.

DuPont is, of course, not the only one in this game.  Cathay Industrial Biotech, for example, ships many different polymers composed of long chain dicarboxylic acids, which are derived from corn and used in anticorrosion products for cars.  Both firms are buying more corn just as prices for commodities are headed through the roof.  Higher prices are now leading U.S farmers to pull land out of conservation programs for use in producing more crops, as described by David Streitfeld in, "As Prices Rise, Farmers Spurn Conservation Program".  Corn, wheat, and soy prices are all up, but so are the prices of oil and fertilizer.

Ostensibly, the Conservation Reserve Program pays farmers to keep environmentally sensitive land out of production.   In the context of a grain surplus, this has the effect of reducing the total amount of land in production, thereby keeping prices a bit higher.  But the surplus of recent decades is over, due in large part to increases in demand in developing countries (see, for example, my post "China and Future Resource Demands"). 

The utility of keeping lands in conservation programs is debated intensely by a range of interested parties, including farmers, policy makers, conservationists, hunters, and even bakers.  From Streitfled's article:

"We're in a crisis here. Do we want to eat, or do we want to worry about the birds?" asked JR Paterakis, a Baltimore baker who said he was so distressed at a meeting last month with Edward T. Schafer, the agriculture secretary, that he stood up and started speaking "vehemently."

The Paterakis bakery, H&S, produces a million loaves of rye bread a week. The baker said he could not find the rye flour he needed at any price.   

..."The pipeline for wheat is empty," said Michael Kalupa, a bakery owner in Tampa, Fla., who is president of the Retail Bakers of America. Mr. Kalupa said the price he paid for flour had doubled since October. He cannot afford to absorb the cost and he cannot afford to pass it on. Sales have been falling 16 percent to 20 percent a month since October. He has laid off three employees.

Among farmers, the notion of early releases from conservation contracts is prompting sharp disagreement and even anger. The American Soybean Association is in favor. "We need more food," said John Hoffman, the association's president.

The National Association of Wheat Growers is against, saying it believes "in the sanctity of contracts." It does not want more crops to be grown, because commodity prices might go down.

That is something many of its members say they cannot afford, even with wheat at a robust $9 a bushel. Their own costs have increased, with diesel fuel and fertilizer up sharply. "It would decrease my profit margin, which is slim," said Jeff Krehbiel of Hydro, Okla. "Let's hurt the farmer in order to shut the bakers up, is that what we're saying?"

Mr. Krehbiel said his break-even last year was $4 a bushel. This summer it will be $6.20; the next crop, $7.75.

That a baker  in the U.S. can't even find the flour he needs is remarkable, though it may not actually be a harbinger of food shortages.  One reason that baker is having trouble is no doubt an increase in demand, and another, equally without doubt, is due to shifting grain production priorities that accommodate increased use of biofuels.

Much in the news the last couple of months has been the assertion that production and use of biofuels is largely responsible for recent increases in food prices.  But how much of the price increase is due to shifting crops to fuel use?

Censoring Science is Detrimental to Security

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Restricting access to science and technology in the name of security is historically a losing proposition.  Censorship of information that is known to exist incentivizes innovation and rediscovery. 

As most readers of this blog know, there has been quite a furor over new results demonstrating mutations in H5N1 influenza strains that are both deadly and highly contagious in mammals.  Two groups, led by Ron Fouchier in the The Netherlands and Yoshihiro Kawaoka at The University of Wisconsin, have submitted papers to Nature and Science describing the results.  The National Science Advisory Board for Biosecurity (NSABB) has requested that some details, such as sequence information, be omitted from publication.  According to Nature, both journals are "reserving judgement about whether to censor the papers until the US government provides details of how it will allow genuine researchers to obtain redacted information".

For those looking to find more details about what happened, I suggest starting with Dorveen Caraval's interview with Fouchier in the New York Times, "Security in Flu Study Was Paramount, Scientist Says"; Kathleen Harmon's firsthand account of what actually happened when the study was announced; and Heidi Ledford's post at Nature News about the NSABB's concerns.

If you want to go further, there is more good commentary, especially the conversation in the comments (including from a member of the NSABB), in "A bad day for science" by Vincent Racaniello.  See also Michael Eisen's post "Stop the presses! H5N1 Frankenflu is going to kill us all!", keeping in mind that Eisen used to work on the flu.

Writing at Foreign Policy, Laurie Garrett has done some nice reporting on these events in two posts, "The Bioterrorist Next Door" and "Flu Season".  She suggests that attempts to censor the results would be futile: "The genie is out of the bottle: Eager graduate students in virology departments from Boston to Bangkok have convened journal-review debates reckoning exactly how these viral Frankenstein efforts were carried out."

There is much I agree with in Ms. Garrett's posts.  However, I must object to her assertion that the work done by Fouchier and Kawaoka can be repeated easily using the tools of synthetic biology.  She writes "The Fouchier episode laid bare the emptiness of biological-weapons prevention programs on the global, national, and local levels.  Along with several older studies that are now garnering fresh attention, it has revealed that the political world is completely unprepared for the synthetic-biology revolution."   As I have already written a book that discusses this confusion (here is an excerpt about synthetic biology and the influenza virus), it is not actually what I want to write about today.  But I have to get this issue out of the way first.

As far as I understand from reading the press accounts, both groups used various means to create mutations in the flu genome and then selected viruses with properties they wanted to study.  To clarify, from what I have been able to glean from the sparse accounts thus far, DNA synthesis was not used in the work.  And as far as I understand from reading the literature and talking to people who build viruses for a living, it is still very hard to assemble a functioning, infectious influenza virus from scratch.   

If it were easy to write pathogen genomes -- particularly flu genomes -- from scratch, we would quite frankly be in deep shit. But, for the time being, it is hard.  And that is important.  Labs who do use synthetic biology to build influenza viruses, as with those who reconstructed the 1918 H1N1 influenza virus, fail most of the time despite great skill and funding.  Synthesizing flu viruses is simply not a garage activity.  And with that, I'll move on.

Regardless of how the results might be reproduced, many have suggested that the particular experiments described by Fouchier and Kawaoka should not have been allowed.  Fouchier himself acknowledges that selecting for airborne viruses was not the wisest experiment he could have done; it was, he says, "really, really stupid".  But the work is done, and people do know about it.  So the question of whether this work should have been done in the first place is beside the point.  If, as suggested by Michael Eisen, that "any decent molecular biologist" could repeat the work, then it was too late to censor the details as soon as the initial report came out. 

I am more interested in the consequences of trying to contain the results while somehow allowing access to vetted individuals.  Containing the results is as much about information security as it is biological security.  Once such information is created, the challenge is to protect it, to secure it.  Unfortunately, the proposal to allow secure access only by particular individuals is at least a decade (if not three decades) out of date.

Any attempt to secure the data would have to start with an assessment of how widely it is already distributed.  I have yet to meet an academic who regularly encrypts email, and my suspicion is that few avail themselves of the built-in encryption on their laptops.  So, in addition to the university computers and email servers where the science originated, the information is sitting in the computers of reviewers, on servers at Nature and Science, at the NSABB, and, depending on how the papers were distributed and discussed by members of the NSABB, possibly on their various email servers and individual computers as well.  And let's not forget the various unencrypted phones and tablets all of those reviewers now carry around.

But never mind that for a moment.  Let's assume that all these repositories of the relevant data are actually secure.  The next step is to arrange access for selected researchers.  That access would inevitably be electronic, requiring secure networks, passwords, etc.  In the last few days the news has brought word that computer security firms Stratfor and Symantec have evidently been hacked recently.  Such attacks are not uncommon.  Think back over the last couple of years: hacks at Google, various government agencies, universities.  Credit card numbers, identities, and supposedly secret DoD documents are all for sale on the web.  To that valuable information we can now add a certain list of influenza mutations.  If those mutations are truly a critical biosecurity risk -- as asserted publicly by various members of the NSABB -- then that data has value far beyond its utility in virology and vaccinology.

The behavior of various hackers (governments, individuals, other) over the last few years make clear that what the discussion thus far has done is to stick a giant "HACK HERE" sign on the data.  Moreover, if Ms. Garrett is correct that students across the planet are busy reverse engineering the experiments because they don't have access to the original methods and data, then censorship is creating a perverse incentive for innovation.  Given today's widespread communication, restriction of access to data is an invitation, not a proscription.

This same fate awaits any concentration of valuable data.  It obviously isn't a problem limited to collections of sensitive genetic sequences or laboratory methods.  And there is certainly a case to be made for attempting to maintain confidential or secret caches of data, whether in the public or private interest.  In such instances, compartmentalization and encryption must be implemented at the earliest stages of communication in order to have any hope of maintaining security. 

However, in this case, if it true that reverse engineering the results is straightforward, then restriction of access serves only to slow down the general process of science.  Moreover, censorship will slow the development of countermeasures.  It is unlikely that any collection of scientists identified by the NSABB or the government will be sufficient to develop all the technology we need to respond to natural pathogens, let alone any artificial ones.

As with most other examples of prohibition, these restrictions are doomed before they are even implemented.  Censorship of information that is known to exist incentivizes innovation and rediscovery.  As I explored in my book, prohibition in the name of security is historically a losing proposition.  Moreover, science is inherently a networked human activity that is fundamentally incompatible with constraints on communication, particularly of results that are already disclosed.  Any endeavor that relies upon science is, therefore, also fundamentally incompatible with constraints on communication.  Namely developing technologies to defend against natural and artificial pathogens.  Censorship threatens not just science but also our security.

Further Thoughts on iGEM 2011

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Following up on my post of several weeks ago (iGEM 2011: First Thoughts), here is a bit more on last year's Jamboree.  I remain very, very impressed by what the teams did this year.  And I think that watching iGEM from here on out will provide a sneak peak of the future of biological technologies.

I think the biggest change from last year is the choice of applications, which I will describe below.  And related to the choice of applications is change of approach to follow a more complete design philosophy.  I'll get to the shift in design sensibility further on in the post.

The University of Washington: Make it or Break it

I described previously the nuts and bolts of the University of Washington's Grand Prize winning projects.  But, to understand the change in approach (or perhaps change in scope?) this project represents, you also have to understand a few details about problems in the real world.  And that is really the crux of the matter -- teams this year took on real world problems as never before, and may have produced real world solutions.

Recall that one of the UW projects was the design of an enzyme that digests gluten, with the goal of using that enzyme to treat gluten intolerance.  Candidate enzymes were identified through examining the literature, with the aim of finding something that works at low pH.  The team chose a particular starter molecule, and then used the "video game" Foldit to re-design the active site in silico so that it would chew up gluten (here is a very nice Youtube video on the Foldit story from Nature).  They then experimentally tested many of the potential improvements.  The team wound up with an enzyme that in a test tube is ~800 times better than one already in clinical trials.  While the new enzyme would of course itself face lengthy clinical trials, the team's achievement could have an enormous impact on people who suffer from celiac disease, among many other ailments.

From a story in last week's NYT Magazine ("Should We All Go Gluten-Free?"), here are some eye-opening stats on celiac disease, which can cause symptoms ranging from diarrhea to dramatic weight loss:

  • Prior to 2003, prevalence in the US was thought to be just 1 in 10,000: widespread testing revealed the actual rate was 1 in 133.
  • Current estimates are that 18 million Americans have some sort of gluten intolerance, which is about 5.8% of the population.
  • Young people were 5x more likely to have the disease by the 1990s than in the 1950s based on looking at old blood samples.
  • Prevalence is increasing not just in US, but also worldwide.
In other words, celiac disease is a serious metabolic issue that for some reason is affecting ever larger parts of the global population.  And as a summer project a team of undergraduates may have produced a (partial) treatment for the disease.  That eventual treatment would probably require tens of millions of dollars of further investment and testing before it reaches the market.  However, the market for gluten-free foods, as estimated in the Times, is north of $6 billion and growing rapidly.  So there is plenty of market potential to drive investment based on the iGEM project.

The other UW project is a demonstration of using E. coli to directly produce diesel fuel from sugar.  The undergraduates first reproduced work published last year from LS9 in which E. coli was modified to produce alkanes (components of diesel fuel -- here is the Science paper by Schirmer et al).  Briefly, the UW team produced biobricks -- the standard format used in iGEM -- of two genes that turn fatty acids into alkanes.  Those genes were assembled into a functional "Petrobrick".  The team then identified and added a novel gene to E. coli that builds fatty acids from 3 carbon seeds (rather than the native coli system that builds on 2 carbon seeds).  The resulting fatty acids then served as substrates for the Petrobrick, resulting in what appears to be the first report anywhere of even-chain alkane synthesis.  All three genes were packaged up into the "FabBrick", which contains all the components needed to let E. coli process sugar into a facsimile of diesel fuel.

The undergraduates managed to substantially increase the alkane yield by massaging the culture conditions, but the final yield is a long way from being useful to produce fuel at volume.  But again, not bad for a summer project.  This is a nice step toward turning first sugar, then eventually cellulose, directly into liquid fuels with little or no purification or post-processing required.  It is, potentially, also a step toward "Microbrewing the Bioeconomy".  For the skeptics in the peanut gallery, I will be the first to acknowledge that we are probably a long way from seeing people economically brew up diesel in their garage from sugar.  But, really, we are just getting started.  Just a couple of years ago people thought I was all wet forecasting that iGEM teams would contribute to technology useful for distributed biological manufacturing of fuels.  Now they are doing it.  For their summer projects.  Just wait a few more years.

Finally -- yes, there's more -- the UW team worked out ways to improve the cloning efficiency of so-called Gibson cloning.  They also packaged up as biobricks all the components necessary to produce magnetosomes in E. coli.  The last two projects didn't make it quite as far as the first two, but still made it further than many others I have seen in the last 5 years.

Before moving on, here is a thought about the mechanics of participating in iGEM.  I think the UW wiki is the about best I have seen.   I like very much the straightforward presentation of hypothesis, experiments, and results.  It was very easy to understand what they wanted to do, and how far they got.  Here is the "Advice to Future iGEM Teams" I posted a few years ago.  Aspiring iGEM teams should take note of the 2011 UW wiki -- clarity of communication is part of your job.

Lyon-INSA-ENS: Cobalt Buster

The team from Lyon took on a very small problem: cleaning up cooling water from nuclear reactors using genetically modified bacteria.  This was a nicely conceived project that involved identifying a problem, talking to stakeholders, and trying to provide a solution.  As I understand it, there are ongoing discussions with various sponsors about funding a start-up to build prototypes.  It isn't obvious that the approach is truly workable as a real world solution -- many questions remain -- but the progress already demonstrated indicates that dismissing this project would be premature.

Before continuing, I pause to reflect on the scope of Cobalt Buster.  One does wonder about the eventual pitch to regulators and the public: "Dear Europe, we are going to combine genetically modified organisms and radiation to solve a nuclear waste disposal problem!"  As the team writes on its Human Practices page: "In one project, we succeed to gather Nuclear Energy and GMOs. (emphasis in original)"  They then acknowledge the need to "focus on communication".  Indeed.

Here is the problem they were trying to solve: radioactive Cobalt (Co) is a contaminant emitted during maintenance of nuclear reactors.  The Co is typically cleaned up with ion exchange resins, which are both expensive and when used up must be appropriately disposed of as nuclear waste.  By inserting a Co importer pump into E. coli, the Lyon team hopes to use bacteria to concentrate the Co and thereby clean up reactor cooling water.  That sounds cool, but the bonus here is that modelling of the system suggests that using E. coli as a biofilter in this way would result in substantially less waste.  The team reports that they expect 8000kg of ion exchange resins could be replaced with 4kg of modified bacteria.  That factor of 2000 in volume reduction would have a serious impact on disposal costs.  And the modified bug appears to work in the lab (with nonradioactive Cobalt), so this story is not just marketing.

The Lyons team also inserted a Co sensor into their E. coli strain.  The sensor then drove expression of a protein that forms amyloid fibers, causing the coli in turn to form a biofilm.  This biofilm would stabilize the biofilter in the presence of Co.  The filter would only be used for a few hours before being replaced, which would not give the strain enough time to lose this circuit via selection.

Imperial College London: Auxin

Last, but certainly not least, is the very well thought through Imperial College project to combat soil erosion by encouraging plant root growth.  I saved this one for last because, for me, the project beautifully reflects the team's intent to carefully consider the real-world implications of their work.  There are certainly skeptics out there who will frown on the extension of iGEM into plants, and who feel the project would never make it into the field due to the many regulatory barriers in Europe.  I think the skeptics are completely missing the point.

To begin, a summary of the project: the Imperial team's idea was to use bacteria as a soil treatment, applied in any number of ways, that would be a cost-effective means of boosting soil stability through root growth.  The team designed a system in which genetically modified bacteria would be attracted to plant roots, would then take up residence in those roots, and would subsequently produce a hormone that encourages root growth.

The Auxin system was conceived to combine existing components in very interesting ways.  Naturally-occurring bacteria have already been shown to infiltrate plant roots, and other soil-dwelling bacteria produce the same growth hormone that encourages root proliferation.

Finally, the team designed and built a novel (and very clever) system for preventing leakage of transgenes through horizontal gene transfer.  On the plasmid containing the root growth genes, the team also included genes that produce proteins toxic to bacteria.  But in the chromosome, they included an anti-toxin gene.  Thus if the plasmid were to leak out and be taken up by a bacterium without the anti-toxin gene, any gene expression from the plasmid would kill the recipient cell.

The team got many of these pieces working independently, but didn't quite get the whole system working together in time for the international finals.  I encourage those interested to have a look at the wiki, which is really very good.

The Shift to Thinking About Design

As impressive as Imperial's technical results were, I was also struck by the integration of "human practices" into the design process.  The team spoke to farmers, economists, Greenpeace -- the list goes on -- as part of both defining the problem and attempting to finesse a solution given the difficulty of fielding GMOs throughout the UK and Europe.  And these conversations very clearly impacted the rest of the team's activities.

One of the frustrations felt by iGEM teams and judges alike is that "human practices" has often felt like something tacked on to the science for the sake of placating potential critics.  There is something to that, as the Ethical, Legal, and Social Implications (ELSI) components of large federal projects such as The Human Genome Project and SynBERC appear to have been tacked on for just that reason.  Turning "human practices" into an appendix on the body of science is certainly not the wisest way to go forward, for reasons I'll get to in a moment, nor is it politically savvy in the long term.  But if the community is honest about it, tacking on ELSI to get funding has been a successful short-term political hack.

The Auxin project, along with a few other events during the finals, helped crystallize for me the disconnect between thinking about "human practices" as a mere appendix while spouting off about how synthetic biology will be the core of a new industrial revolution, as some of us tend to do.  Previous technological revolutions have taught us the importance of design, of thinking the whole project through at the outset in order to get as much right as possible, and to minimize the stuff we get wrong.  We should be bringing that focus on design to synthetic biology now.

I got started down this line of thought during a very thought-provoking conversation with Dr. Megan Palmer, the Deputy Director for Practices at SynBERC.  (Apologies to you, Megan, if I step your toes in what follows -- I just wanted to get these thoughts on the page before heading out the door for the holidays.)  The gist of my chat with Megan was that the focus on safety and security as something else, as an activity separate from the engineering work of SB, is leading us astray.  The next morning, I happened to pass Pete Carr and Mac Cowell having a chat just as one of them was saying, "The name human practices sucks. We should really change the name."  And then my brain finally -- amidst the jet lag and 2.5 days of frenetic activity serving as a judge for iGEM -- put the pieces together.  The name does suck.  And the reason it sucks is that it doesn't really mean anything.

What the names "human practices" and "ELSI" are trying to get at is the notion that we shouldn't stumble into developing and using a powerful technology without considering the consequences.  In other fields, whether you are thinking about building a chair, a shoe, a building, an airplane, or a car, in addition to the shape you usually spend a great deal of time thinking about where the materials come from, how much the object costs to make, how it will be used, who will use it, and increasingly how it will be recycled at end of use.  That process is called design, and we should be practicing it as an integral part of manipulating biological systems.

When I first started as a judge for iGEM, I was confused by the kind of projects that wound up receiving the most recognition.  The prizes were going to nice projects, sure, but those projects were missing something from my perspective.  I seem to recall protesting at some point in that first year that "there is an E in iGEM, and it stands for Engineering."  I think part of that frustration was the pool of judges was dominated for many years by professors funded by the NIH, NRC, or the Welcome Trust, for example -- scientists who were looking for scientific results they liked to grace the pages of Science or Nature -- rather than engineers, hackers, or designers who were looking for examples of, you know, engineering.

My point is not that the process of science is deficient, nor that all lessons from engineering are good -- especially as for years my own work has fallen somewhere in between science and engineering.  Rather, I want to suggest that, given the potential impact of all the science and engineering effort going into manipulating biological systems, everyone involved should be engaging in design.  It isn't just about the data, nor just about shiny objects.  We are engaged in sorting out how to improve the human condition, which includes everything from uncovering nature's secrets to producing better fuels and drugs.  And it is imperative that as we improve the human condition we do not diminish the condition of the rest of the life on this planet, as we require that life to thrive in order that we may thrive.

Which brings me back to design.  It is clear that not every experiment in every lab that might move a gene from one organism to another must consider the fate of the planet as part of the experimental design.  Many such experiments have no chance of impacting anything outside the test tube in which they are performed.  But the practice of manipulating biological systems should be done in the context of thinking carefully about what we are doing -- much more carefully than we have been, generally speaking.  Many fields of human endeavor can contribute to this practice.  There is a good reason that ELSI has "ethical", "legal", and "social" in it.

There have been a few other steps toward the inclusion of design in iGEM over the years.  Perhaps the best example is the work designers James King and Daisy Ginsburg did with the 2009 Grand Prize Winning team from Cambridge (see iGEM 2009: Got Poo?).  That was lovely work, and was cleverly presented in the "Scatalog".  You might argue that the winners over the years have had increasingly polished presentations, and you might worry that style is edging out substance.  But I don't think that is happening.  The steps taken this year by Imperial, Lyon, and Washington toward solving real-world problems were quite substantive, even if those steps are just the beginning of a long path to get solutions into people's hands.  That is the way innovation works in the real world.

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