Not a great deal to report this week, as I've been suffering from a particularly painful seasonal disorder (i.e. marking). The delay to our Madrid trip due to Icelandic intervention was a blessing in disguise, I think, as it allowed me to clear the decks of a load of scripts before jetting off to give three afternoons of lectures at the Universidad Polytecnica de Madrid. If we'd gone when we'd originally planned to then the scripts would have been sitting there in my study at home, a distant yet malign cloud hanging over the trip.
Arrived in Madrid yesterday, after a relatively painless flight from Liverpool with EasyJet. It was all going too well, however; on arrival at the hotel, our daughter ran towards a display of flowers in the lobby, caught her foot on a rug and went face-down onto a table. She cut her eye quite badly, but she's a hardy little thing, and was back on top form today.
I gave my first set of lectures this afternoon/evening, as the guest of Alfonso Rodriguez-Paton. He's the "Madrid node" of our BACTOCOM project, and kindly invited me to teach some of their postgraduates (others involved this year include Christof Teuscher, Milan Stojanovic and Friedrich Simmel, who's also involved with BACTOCOM). I'm here to talk about "molecular and cellular computing"; ŧoday was motivation and historial background, a bit of biology and an overview of Adleman's experiment. Tomorrow is formal models of DNA computation followed by self-assembly and DNA origami. The final set of lectures on Wednesday will deal mainly with synthetic biology, so I hope Fritz has left me something to talk about.
Monday, May 31, 2010
Monday, May 24, 2010
Weeknote #2 (w/e 23/5/10)
It's been a big week for synthetic biology, with the announcement by Craig Venter that he'd succeeded in creating a "synthetic cell". My previous post describes my take on the technical aspects of his achievement; it's not entirely accurate to call it a "synthetic cell", since they used existing cells as the recipients (that is, it was only the genome that was synthetic). It's more like "genomic transplantation" with de novo sequences. Technically challenging, but not the earth-shattering breakthrough that it's being sold/hyped as. They certainly didn't turn "inanimate chemicals into a living organism".
My own little piece of press coverage looked pretty low-key by comparison. I was interviewed ages ago by Louise Tickle for the Education section of the Guardian, and the story finally appeared last week.
This week, members of my group (specifically, Pete and Naomi) contributed to an event hosted by MMU. I'm a Director of ArcSpace Manchester, a Community Interest Company to support creative and ethical exchange, and on May 19th we held a video conference with collaborators in Sao Paolo, Brazil, to discuss "eco-techno" and public engagement. Unfortunately, other commitments meant that I was unable to attend either in person or in the form of an avatar, but my co-director, Vicky Sinclair, wrote up the event.
On the work front, I've been busy marking projects and exam scripts, although I did also submit this conference paper.
Friday, May 21, 2010
Team Venter's synthetic cell, explained
I've been asked to comment on this week's news that Craig Venter's team have succeeded in building a "synthetic living cell" (you can read the full paper, for free, here), so I thought it might be useful to write a short post to explain just what they've achieved.
Cells may be thought of as biological "wetware", in the same way that the physical components of a personal computer (hard drive, processor, memory, etc.) form the "hardware". A computer can't work without an operating system; the central controller program that runs in the background, coordinating the various activities of the machine. Most people use Windows as their operating system, although there are others, such as Ubuntu Linux and MacOS. Similarly, a cell cannot survive without a working genome; the collection of genes that control and influence an organism's internal operation.
The core kernel (ie. the central "brain") of the Ubuntu Linux operating system running on my netbook is (roughly) 4 Megabytes in size, which is about four times the size of the genome of Mycoplasma mycoides. This is a bacterial parasite found in cattle and goats, and it was selected by Venter and his team because (a) it has a relatively small genome that has been fully-sequenced, and (b) it grows more quickly than bacteria they've used in the past.
Venter and his team have created an entirely synthetic copy of the genome of M. mycoides, which they then inserted into a related bacterium, M. capricolum. This new genome was "booted up" by the recipient, which then started "running" the new genetic program.
Importantly, the synthetic genome was completely pristine, in the sense that it had not been physically derived in any way from existing genetic material. Standard genetic engineering splices short synthetic sequences in to existing, "natural" DNA sequences, but Venter's "synthia" genome was created from scratch. It's the equivalent of taking the known binary sequence of a small operating system kernel, typing it into a text editor in small chunks, combining the chunks together into one big file, and then using it to boot up a PC. At no stage was the "new" kernel physically derived (copied) from a version stored on CD, DVD, or downloaded from the 'net.
Venter's team use a DNA synthesizer to piece together the A, G, C and T bases to form brand-new building blocks, which were then stitched together into a single sequence. This is the key technical achievement of the paper - a strategy for assembling an entire genome, from scratch, using synthetic components, and to get it "running" in a host cell. It's important to note that it was only the genome that was synthetic; the recipient cell was a pre-existing, "natural" bacterium.
This breakthrough is significant in that it demonstrates the feasibility of large-scale whole-genome transplantation, which will be an important component of the emerging field of synthetic biology. However, the real challenge lies in gaining a systems-level understanding of how even simple genomes operate, so that they may be fundamentally (re-)engineered.
Science has opened up a forum for posting questions, which will be answered later today by news writer Elizabeth Pennisi and philosopher and scientist Mark Bedau.
Update, 21/5/10, 11:13: Corrected kernel size assertions; Windows kernel is much larger than previously thought.
Cells may be thought of as biological "wetware", in the same way that the physical components of a personal computer (hard drive, processor, memory, etc.) form the "hardware". A computer can't work without an operating system; the central controller program that runs in the background, coordinating the various activities of the machine. Most people use Windows as their operating system, although there are others, such as Ubuntu Linux and MacOS. Similarly, a cell cannot survive without a working genome; the collection of genes that control and influence an organism's internal operation.
The core kernel (ie. the central "brain") of the Ubuntu Linux operating system running on my netbook is (roughly) 4 Megabytes in size, which is about four times the size of the genome of Mycoplasma mycoides. This is a bacterial parasite found in cattle and goats, and it was selected by Venter and his team because (a) it has a relatively small genome that has been fully-sequenced, and (b) it grows more quickly than bacteria they've used in the past.
Venter and his team have created an entirely synthetic copy of the genome of M. mycoides, which they then inserted into a related bacterium, M. capricolum. This new genome was "booted up" by the recipient, which then started "running" the new genetic program.
Importantly, the synthetic genome was completely pristine, in the sense that it had not been physically derived in any way from existing genetic material. Standard genetic engineering splices short synthetic sequences in to existing, "natural" DNA sequences, but Venter's "synthia" genome was created from scratch. It's the equivalent of taking the known binary sequence of a small operating system kernel, typing it into a text editor in small chunks, combining the chunks together into one big file, and then using it to boot up a PC. At no stage was the "new" kernel physically derived (copied) from a version stored on CD, DVD, or downloaded from the 'net.
Venter's team use a DNA synthesizer to piece together the A, G, C and T bases to form brand-new building blocks, which were then stitched together into a single sequence. This is the key technical achievement of the paper - a strategy for assembling an entire genome, from scratch, using synthetic components, and to get it "running" in a host cell. It's important to note that it was only the genome that was synthetic; the recipient cell was a pre-existing, "natural" bacterium.
This breakthrough is significant in that it demonstrates the feasibility of large-scale whole-genome transplantation, which will be an important component of the emerging field of synthetic biology. However, the real challenge lies in gaining a systems-level understanding of how even simple genomes operate, so that they may be fundamentally (re-)engineered.
Science has opened up a forum for posting questions, which will be answered later today by news writer Elizabeth Pennisi and philosopher and scientist Mark Bedau.
Update, 21/5/10, 11:13: Corrected kernel size assertions; Windows kernel is much larger than previously thought.
Monday, May 17, 2010
Weeknote #1 (w/e 16/5/10)
In an effort to blog more regularly, I've decided to adopt the Weeknote model of short seven-day updates on what's been going on.
The weekend was dominated by my inability to leave the country; I was due to fly to Madrid to give a series of lectures on molecular and cellular computing to Masters and Doctoral students at the Universidad Politécnica de Madrid. It was also an opportunity to take a couple of days of much-needed time with my wife and daughter, who'd be travelling with me. As the airspace in Northern Ireland had already been closed, we checked the status of the flight before we set off for Liverpool Airport. Everything was ok, but by the time we got there a couple of hours later, they'd shut down. A maudlin hen party, wearing mandatory pink fluffy stetsons, were told that the next available flight was on Thursday; we just returned home, where I quickly rescheduled the lectures for two weeks time. My host, Alfonso Rodríguez-Patón, was incredibly understanding and helpful, managing to book a new hotel for us, despite the fact that my new schedule coincides with a major festival on the Thursday (making hotel rooms extremely rare).
Another significant event this week was the Future Everything festival, which was (if you read the various reviews and tweets) wildly successful. I contributed to a panel discussion on New Creativity, which also featured Anab Jain, a TED Fellow who talked about her Power of 8 project, Kerenza McClarnan of Buddleia, who's facilitating artist-led enquiry into urban spaces, and Adrian Hon of award-winning games company Six to Start, who talked about the purpose of play. It was a fascinating session, with a lot of dynamic connections made between the panelists (none of whom really knew anything in advance about what the others would say). The session was recorded, so I'll post a link if and when the video is made available.
In mid-week we had our latest brain-storming "away"-day for our Bridging the Gaps: NanoInfoBio (NIB) project. This is a two-year initiative, supported by the EPSRC, to encourage cross-disciplinary research within MMU (with specific focus on the life sciences/engineering/computing/maths/nanotechnology interface(s)). We're almost ten months into the project now, and are beginning to develop a coherent set of themes around which we can coalesce. We're giving out a few project grants of £25K in order to boot-strap small feasibility studies, so we arranged an afternoon at a Manchester hotel to generate some ideas. Experience has shown that it's best to get everyone away from the distractions of email, and the temptation to "just pop back to the office", and I think everyone was happy with how it went. Rather than dividing everyone into groups, as might seem natural, we first performed a general "audit" of possible project ideas (this first pass generated 12), and then "drilled down" as a whole group to examine each idea in turn. Once a page or so of flip-chart paper had been filled for each project, only then did we split up in order to go over the fine details of costings and so on. The group-level discussion led to some surprising contributions, which would have been lost if we'd split up too quickly. I think it worked.
The weekend was dominated by my inability to leave the country; I was due to fly to Madrid to give a series of lectures on molecular and cellular computing to Masters and Doctoral students at the Universidad Politécnica de Madrid. It was also an opportunity to take a couple of days of much-needed time with my wife and daughter, who'd be travelling with me. As the airspace in Northern Ireland had already been closed, we checked the status of the flight before we set off for Liverpool Airport. Everything was ok, but by the time we got there a couple of hours later, they'd shut down. A maudlin hen party, wearing mandatory pink fluffy stetsons, were told that the next available flight was on Thursday; we just returned home, where I quickly rescheduled the lectures for two weeks time. My host, Alfonso Rodríguez-Patón, was incredibly understanding and helpful, managing to book a new hotel for us, despite the fact that my new schedule coincides with a major festival on the Thursday (making hotel rooms extremely rare).
Another significant event this week was the Future Everything festival, which was (if you read the various reviews and tweets) wildly successful. I contributed to a panel discussion on New Creativity, which also featured Anab Jain, a TED Fellow who talked about her Power of 8 project, Kerenza McClarnan of Buddleia, who's facilitating artist-led enquiry into urban spaces, and Adrian Hon of award-winning games company Six to Start, who talked about the purpose of play. It was a fascinating session, with a lot of dynamic connections made between the panelists (none of whom really knew anything in advance about what the others would say). The session was recorded, so I'll post a link if and when the video is made available.
In mid-week we had our latest brain-storming "away"-day for our Bridging the Gaps: NanoInfoBio (NIB) project. This is a two-year initiative, supported by the EPSRC, to encourage cross-disciplinary research within MMU (with specific focus on the life sciences/engineering/computing/maths/nanotechnology interface(s)). We're almost ten months into the project now, and are beginning to develop a coherent set of themes around which we can coalesce. We're giving out a few project grants of £25K in order to boot-strap small feasibility studies, so we arranged an afternoon at a Manchester hotel to generate some ideas. Experience has shown that it's best to get everyone away from the distractions of email, and the temptation to "just pop back to the office", and I think everyone was happy with how it went. Rather than dividing everyone into groups, as might seem natural, we first performed a general "audit" of possible project ideas (this first pass generated 12), and then "drilled down" as a whole group to examine each idea in turn. Once a page or so of flip-chart paper had been filled for each project, only then did we split up in order to go over the fine details of costings and so on. The group-level discussion led to some surprising contributions, which would have been lost if we'd split up too quickly. I think it worked.
Tuesday, May 11, 2010
The need for hacking
The following post is a lightly-edited version of an article I've just had published in the Spring 2010 issue of MMU's Success magazine:
The word "hacker" has, in recent years, acquired an unfortunate and perjorative meaning. The media portrayal is of a pale-faced teenage boy (for they are invariably male) crouched over a keyboard in a fetid room, determined to make their mark on the world through cyber-vandalism or malware scams. My teenage years were partly shaped by the movie WarGames, in which an inquisitive youth accidentally triggers the countdown to armageddon by wandering into a US military computer, while the recent case of the "UFO hacker" Gary McKinnon has merely reinforced the "misfit" stereotype.
They are almost universally despised by mainstream commentators, and yet the infrastructure on which all of us rely (mobile phones, computers and the internet) would not even exist in its current form were it not for the hacker.
The original hackers were the pioneers of the electronic age, when the term simply meant "one who hacks". A hack, back then, was just a clever or "pretty" solution to a difficult problem, rather than an attempt to gain unauthorised access to a system. These early hobbyists and developers created the first microcomputers, as well as the foundations of the global information network.
One of the key principles of the hacker ethic (as described in Steven Levy's book Hackers: Heroes of the Computer Revolution) is that the best computer system is one that may be inspected, dissected and improved upon. When I started programming back in the 1980s, games were often distributed as listings printed in magazines, which had to be typed in before playing. By messing around with this code, I picked up various tricks and learned important new techniques. As my programs became more sophisticated, I had to get "under the bonnet" of the machine and interact with the computer at a fundamental level. The so-called "hard skills" that I learned in those early years have stayed with me ever since.
Modern teaching increasingly promotes the "soft skills" agenda, such as the need for team-working, communication and negotiation. Whilst these abilities are undoubtedly important, we need to protect and promote technical content. I wouldn't want a mechanic delving under the bonnet of my car if all he or she had ever done was change a tyre or top up the screen-wash, even if they did describe themself as a personable, motivated team-player...
Computers now take many forms (consoles, phones and PCs, for example) and they're increasingly viewed as sealed appliances, intended for gaming, chatting or browsing. Members of tomorrow's workforce are immersed in social networking, app downloads and file sharing, but they often lack the fundamental knowledge that can only come by (either physically or metaphorically) opening up the box and tinkering with its insides. By that, I mean the acquisition of technical insights and skills required in order for a person to become a software producer, rather than simply a consumer of apps. New innovations such mobile and cloud computing mean that hard skills are more important than ever, as the digital infrastructure becomes ever more firmly rooted in our day-to-day lives.
The beauty of the situation is that these skills are no longer the sole domain of computing professionals. The availability of modern computers means that we are ideally-placed to develop the next hacker generation, capable of creating ingenious applications and web-based systems. We need to return to the playful principles of the original hackers, by promoting programming as a recreational activity. Modern software packages such as Alice allow us to teach complex concepts almost by stealth, through the medium of computer animation. Open-source operating systems encourage tinkering, and mobile app development is now a legitimate career path. The new generation of twenty-first century hackers may well be digital natives, but they first need to learn to speak the language.
The word "hacker" has, in recent years, acquired an unfortunate and perjorative meaning. The media portrayal is of a pale-faced teenage boy (for they are invariably male) crouched over a keyboard in a fetid room, determined to make their mark on the world through cyber-vandalism or malware scams. My teenage years were partly shaped by the movie WarGames, in which an inquisitive youth accidentally triggers the countdown to armageddon by wandering into a US military computer, while the recent case of the "UFO hacker" Gary McKinnon has merely reinforced the "misfit" stereotype.
They are almost universally despised by mainstream commentators, and yet the infrastructure on which all of us rely (mobile phones, computers and the internet) would not even exist in its current form were it not for the hacker.
The original hackers were the pioneers of the electronic age, when the term simply meant "one who hacks". A hack, back then, was just a clever or "pretty" solution to a difficult problem, rather than an attempt to gain unauthorised access to a system. These early hobbyists and developers created the first microcomputers, as well as the foundations of the global information network.
One of the key principles of the hacker ethic (as described in Steven Levy's book Hackers: Heroes of the Computer Revolution) is that the best computer system is one that may be inspected, dissected and improved upon. When I started programming back in the 1980s, games were often distributed as listings printed in magazines, which had to be typed in before playing. By messing around with this code, I picked up various tricks and learned important new techniques. As my programs became more sophisticated, I had to get "under the bonnet" of the machine and interact with the computer at a fundamental level. The so-called "hard skills" that I learned in those early years have stayed with me ever since.
Modern teaching increasingly promotes the "soft skills" agenda, such as the need for team-working, communication and negotiation. Whilst these abilities are undoubtedly important, we need to protect and promote technical content. I wouldn't want a mechanic delving under the bonnet of my car if all he or she had ever done was change a tyre or top up the screen-wash, even if they did describe themself as a personable, motivated team-player...
Computers now take many forms (consoles, phones and PCs, for example) and they're increasingly viewed as sealed appliances, intended for gaming, chatting or browsing. Members of tomorrow's workforce are immersed in social networking, app downloads and file sharing, but they often lack the fundamental knowledge that can only come by (either physically or metaphorically) opening up the box and tinkering with its insides. By that, I mean the acquisition of technical insights and skills required in order for a person to become a software producer, rather than simply a consumer of apps. New innovations such mobile and cloud computing mean that hard skills are more important than ever, as the digital infrastructure becomes ever more firmly rooted in our day-to-day lives.
The beauty of the situation is that these skills are no longer the sole domain of computing professionals. The availability of modern computers means that we are ideally-placed to develop the next hacker generation, capable of creating ingenious applications and web-based systems. We need to return to the playful principles of the original hackers, by promoting programming as a recreational activity. Modern software packages such as Alice allow us to teach complex concepts almost by stealth, through the medium of computer animation. Open-source operating systems encourage tinkering, and mobile app development is now a legitimate career path. The new generation of twenty-first century hackers may well be digital natives, but they first need to learn to speak the language.
Friday, April 02, 2010
"It's alive! ALIVE!"
I've decided to revive the blog, as several new projects have started recently, and I think it's useful to pass on news through informal channels such as this, as well as via the "official" websites. I'll be posting regular updates on our BACTOCOM project, funded by the European Commission, as well as news of Bridging the Gaps: NanoInfoBio, and any other snippets that I think might be of interest.
Sunday, June 01, 2008
Synthetic biology and Howard Hughes
The Howard Hughes Medical Institute has announced its latest set of investigator appointments. Awards are made to individuals, as opposed to the usual mode of funding, where money is assigned to a project, and the field of synthetic biology is represented by two of its leading figures in the current crop. Jim Collins at Boston and Michael Elowitz at Caltech both had papers in the important 2000 issue of Nature, which reported some of the first experimental results in the area (specific papers are here and here.)
Thursday, May 29, 2008
Genesis Machines in the USA
I'm pleased to report that Genesis Machines has just been published in the USA by The Overlook Press. The book is available via Amazon, and I'm delighted to be associated with another independent award-winning publisher (after Toby Mundy's 2005 triumph with Atlantic at the 2005 British Book Awards).
Sunday, February 24, 2008
Engineering biology, with Drew Endy
There's a fascinating essay by/interview with Drew Endy on the Edge website, which appears to be the latest in a series to have emerged from an event they organised last August. I've written about Endy in the past, and he features prominently in the final chapters of Genesis Machines; indeed, I wish I'd had such an illuminating transcript available when I wrote the book.
Endy is an Assistant Professor of Biological Engineering at MIT, and one of the leading figures in synthetic biology. In one particular paragraph, he captures the excitement of this emerging new discipline:
"Programming DNA is more cool, it's more appealing, it's more powerful than silicon. You have an actual living, reproducing machine; it's nanotechnology that works. It's not some Drexlarian (Eric Drexler) fantasy. And we get to program it. And it's actually a pretty cheap technology. You don't need a FAB Lab like you need for silicon wafers. You grow some stuff up in sugar water with a little bit of nutrients. My read on the world is that there is tremendous pressure that's just started to be revealed around what heretofore has been extraordinarily limited access to biotechnology."
Friday, February 15, 2008
Insect lab
I've spend all week running simulation experiments for our ongoing work on ant-based computing, so when I came across the Insect Lab it seemed strangely appropriate.The artist takes real (dead) insects and customizes their bodies with parts taken from watches and other mechanical devices, to create "cybernetic sculptures".
I'd like to see him do an ant, though... Which train of thought lead me circuitously to Bill Bailey performing his wonderful song Insect Nation (if you just want the lyrics, they're here).
Friday, February 08, 2008
Dr Who
A wonderful present arrived in today's post, courtesy of our equally wonderful friend Eventhia; a signed photograph of Tom Baker! He is, of course, best known for playing the fourth Dr Who, but is probably most familiar to a younger generation as the narrator of Little Britain (and even the delightfully barmy Stagecoach adverts).Most people of sound mind would name Baker as the best ever Dr Who, despite ludicrous polls to the contrary. A case can be made that the choice of favourite depends on which Doctor a person grew up with, and since Baker's tenure extended from 1974-1981, I would certainly agree.
Anyway, he recently did a signing in Norwich, attended by our friends Kris and Eventhia. They very kindly got Tom to sign the photo "For Martyn," (eventually, I think he had it down as "Martin", and you can see where he's corrected it at E's prompting) "Genetically yours, Tom Baker"
Sigh!
Tuesday, February 05, 2008
Biological complexity: from molecules to systems
I'm delighted to have been invited to speak at an event titled "Biological complexity: from molecules to systems", to be held at University College London from 12-13 June this year. The meeting is sponsored by both UCL and the Weizmann Institute of Science in Israel, and will feature speakers from the fields of immunology, computer science, mathematics, biological chemistry, molecular genetics and bioinformatics. I'll try my best to summarize below the research interests of the other invited speakers (but apologies to anyone whose work I misrepresent!)
Stephen Emmott from Microsoft Research in Cambridge will give the keynote address. Stephen is the founder and Director of Microsoft's European Science Programme, and was the driving force behind the influential Towards 2020 Science project and report.
Representing Israeli activity, Nir Friedman works in computational biology, and recently published a paper arguing that gene duplication may drive the "modularisation" of functional genetic networks (that is, genetic networks that are relatively self-contained, and which perform a specific task).
David Harel is a celebrated computer scientist, having carried out important work in logic, software engineering and computability theory. As a student, I often referred to his award-winning book Algorithmics: The Spirit of Computing, and he is currently working on topics that include the modelling and analysis of biological systems (eg. the nematode worm) and the synthesis and communication of smell.
Shmuel Pietrokovski works in bioinformatics, with particular interest in inteins (protein introns); "selfish" DNA elements that are converted into proteins together with their hosts.
Yitzhak Pilpel's lab takes a systems-level approach to how genes are regulated: "By applying genome wide computational approaches, backed-up by in house laboratory experiments, [the lab] devotes itself to both establishing an in-depth understanding of the different processes controlling gene expression, and to understand[ing] how these processes are orchestrated to establish robustness of the regulatory code."
Gideon Schreiber studies the precise nature of protein-protein interactions and the implications these have for complex biological processes.
Eran Segal is a computer scientist (predominantly) working in computational biology, who has recently reported some fascinating work on a "higher level" genetic code, as well as research on predicting expression patterns from their regulatory sequences in fruit flies.
I've already written at some length about Ehud Shapiro (also here); his recent work has centred on the construction of biological computing devices (known as automata) using DNA molecules and enzymes.
Yoav Soen's group is "using embryonic stem cells models to study how different layers of regulation interact to specify morphogenetic decisions, how these decisions are shaped by interactions between emerging precursors and how they are coordinated across a developing embryonic tissue." He has also worked with a colleague of mine, Netta Cohen at Leeds.
Representing activities in the UK, we have Cyrus Chothia from the Laboratory of Molecular Biology at Cambridge, who studies the "nature of the protein repertoires in different organisms and the molecular mechanisms that have produced these differences."
Jasmin Fisher is leading the new Executable Biology Group at Microsoft Research, and is primarily interested in systems/computational biology.
Mike Hoffman and Ewan Birney are at the European Bioinformatics Institute (EBI) in Cambridge, where Birney leads the EBI contribution to Ensembl. There's a transcript of an interview with him here.
Jaroslav Stark is the Director of the Centre for Integrative Systems Biology at Imperial College. He was recently interviewed for a piece on systems biology on BBC Radio 4's The Material World.
Michael Sternberg heads the Structural Bioinformatics Group and the Imperial College Centre for Bioinformatics. He was previously the head of biomolecular modelling at the Imperial Cancer Research Fund now part of Cancer Research UK.
Perdita Stevens is at Edinburgh, where she works on software engineering and theoretical computer science (with a growing interest in modelling viral infection).
The meeting organisers are particularly keen to encourage the participation of young researchers, and the registration fee for this two-day event is a very reasonable 50 pounds (30 for students). To register and for further information, please contact Michelle Jacobs at Weizmann UK at post@weizmann.org.uk or on 020 7424 6860. Attendance will be limited to 180 delegates.
Thursday, January 31, 2008
The human genome, in book form
I've just been sorting through some old files, and came across this picture (click for a larger version), which I took on my last visit to London. I had some free time before a meeting at the BT Tower, so I popped into the Wellcome Collection. Quite apart from the fact that the Wellcome Trust spends around 400 million pounds a year on biomedical research, I have a personal affinity with the trust, since my shortlisted entry to their Book Prize (which was won that year by Chris McManus' Right Hand, Left Hand) was picked up by Toby Mundy and eventually evolved into Genesis Machines (pictured below in the Wellcome Collection bookshop, in a nice circular turn of fate).
One of the most striking exhibits they have (alongside a sample of droppings from Dolly the sheep) is the human genome, printed out in book form. As I've said before, we're rank amateurs compared to nature in the information storage stakes (of course, reading and writing data quickly is another matter...) Since the size of the human genome is estimated at around 3 billion genetic letters (taken from the set {A, G, C, T}), then (assuming that one byte is used to store each letter), each cell with a nucleus (that is, every one except red blood cells and the like) contains 3 Gigabytes of genetic "memory". Of course, we don't need an entire byte (8 bits) to store a quarternary (base 4) value, so we could compress this figure by three quarters, and cells actually contain two genomic copies, but I don't want to over-complicate things...
The fact of the matter is that our genome is large: in the past, I've compared it, if printed out in full, to 200 copies of the Manhattan telephone book. This analogy was arrived at by some back-of-an-envelope calculations, and I don't think I really understood its significance until I visited the Wellcome Collection.
There, in a corner of one of the galleries, stand a single set of white shelves, almost 5 metres by 2 metres, containing 120 hefty volumes. One of them stands open, and a closer inspection reveals page upon page of genetic data, rows and rows of A's, G's, C's and T's tightly-set in 4.5-point text.
The sheer scale of the artifact is mind-blowing, both as an illustration of nature's nanotechnology, but also as a reminder of how far we have to go in terms of beginning to piece together even a small fraction of the human circuitry.
Tuesday, January 29, 2008
Sex with robots
One of the sure signs of impending middle age, especially in a university town, is when people stop handing you flyers. There was a time not so long ago when I could nip out of the office for a sandwich and come back burdened with glossy adverts for progressive house, 2-for-1 vodka shots and foam. But no longer. Now, the bright young things actively avoid me as my thirty-something, corduroy-clad figure shambles into view. The kinder ones simply pretend not to see me.
So imagine my delight when, walking down Oxford Road this afternoon after picking up some grapes, I was handed a flyer. And one offering sex with robots, to boot! Impressed by the targeted precision of whoever was marketing such an opportunity, I was about to kick my heels when I realised that it was actually advertising a club night in Manchester. Now, as a long-time veteran of nights such as House of God and Voodoo, I might have been interested...ooh, ten years ago, but with a responsible job and a young daughter, "'avin it large" now means having that third shot of espresso in my cappucino.
Which is a really cheap and tenuous way of introducing a new play that I think you should go and see. Involution is by a new author, Rachel Welch, and deals with many urgent contemporary themes, such as genetic engineering, religion and the human self-image. One of the plot threads concerns "cybernetic companionship", so I'll leave it to you to make the link...
Alfie Talman, a member of the production team and cast (and, coincidentally, a fellow Ipswich fan) enjoyed Genesis Machines, and thought I might be interested in the play. It's on from February 21st to March 15th at the Pacific Playhouse in London, and there are more details here (and here).
Monday, January 14, 2008
"I'm burning, I'm burning!..."
Although every side in an argument tends to have its own complement of fools, the idiocy exhibited by fundamentalist Christians, in debates over evolution or the origin of the universe, often takes us into the realm of comedy.
Take this example, lifted from a list of fundie "bloopers":
"Everyone knows scientists insist on using complex terminology to make it harder for True Christians to refute their claims.
Deoxyribonucleic Acid, for example... sounds impressive, right? But have you ever seen what happens if you put something in acid? It dissolves! If we had all this acid in our cells, we'd all dissolve! So much for the Theory of Evolution, Check MATE!"
The full amusing-and-yet-slightly-scary list is here.
Take this example, lifted from a list of fundie "bloopers":
"Everyone knows scientists insist on using complex terminology to make it harder for True Christians to refute their claims.
Deoxyribonucleic Acid, for example... sounds impressive, right? But have you ever seen what happens if you put something in acid? It dissolves! If we had all this acid in our cells, we'd all dissolve! So much for the Theory of Evolution, Check MATE!"
The full amusing-and-yet-slightly-scary list is here.
Tuesday, January 08, 2008
Genesis Machines in Japan
Readers in Japan may be interested in the forthcoming edition of Genesis Machines, which is now available for preorder. It's been translated by Kyoko Gibbons, and is out on the 17th of this month.
Friday, August 31, 2007
My Edinburgh talk
I had a wonderful time at the Edinburgh Book Festival over the weekend; a full venue and books to sign afterwards makes for a happy author! Here is a lightly edited version of what I had to say.
In 1959, a great personal hero of mine, the Nobel Prize-winning physicist Richard Feynman gave a visionary talk entitled “There's Plenty of Room at the Bottom”. In his speech, Feynman outlined the possibility of individual molecules, even individual atoms making up the component parts of computers in the future. Remember, this was back when computers filled entire rooms, and were tended by teams of lab-coated technicians, so the idea that you could compute with individual molecules was pretty outlandish. I was struck by a quotation in Oliver's book, attributed to the microbiologist A. J. Kluyver, who said, over fifty years ago, that “The most fundamental character of the living state is the occurrence in parts of the cell of a continuous and directed movement of electrons.” At their most basic, level computers work in exactly the same way; by funnelling electrons around silicon circuits, so I think this hints at the linkages between biology and computers that are only now coming to fruition.
Indeed, it wasn't until 1994 that someone demonstrated, for the first time, the feasibility of building computers from molecular-scale bits. Feynman's vision had waited, not only for the technology to catch up, but for a person with the required breadth of understanding and the will to try something slightly bizarre. That person was Len Adleman, who won the computer science equivalent of the Nobel Prize for his role in the development of the encryption scheme that protects our financial details whenever we buy something on the Internet. Len has always had an interest in biology; when one of his students showed him a program that could take over other programs and force them to replicate it, Len said “Hmmm.... that looks very much like how a virus behaves.” The student was Fred Cohen, author of the first ever computer virus, and Len's term stuck. (Update, 2/9/07: Cohen made the first reference to a "computer virus" in an academic article, but did not write the first virus).
One night in the early 90's, Len was lying in bed reading a classic molecular biology textbook. He came across the section describing a particular enzyme inside the cell that reads and copies DNA, and he was struck by its similarity with an abstract device in computer science known as the Turing Machine. By bringing together two seemingly disparate concepts, Adleman knew at once that, in his own words, “Geez, these things could compute.”
He found a lab at the University of Southern California, where he is a professor, and got down to building a molecular computer. He knew that DNA, the molecule of life that contains the instructions needed to build every organism on the planet, from a slug to.... John Redwood can be thought of as a series of characters from the set A, G, C and T, each character being the first letter of the name of a particular chemical. The title of the film Gattaca, which considers a dystopian future in which genetic discrimination defines a society, is simply a string of characters from the alphabet A, G, C and T.
As Oliver highlights in his own book, molecular biology has always been about the transformation of information, usually inside the living cell. This information is coded in the AGCT sequences of genes and in the proteins that these genes represent. Adleman immediately saw how this mechanism could be harnessed, not to represent proteins, but to store digital data, just like a computer encodes a file as a long sequence of zeroes and ones.
Adleman decided to use this fact to solve a small computational problem. Some of you might have heard of the Travelling Salesman Problem, and Adleman's was a variant of that; given a set of cities connected by flights, does there exist a sequence of flights that starts and ends at particular cities, and which visits every other city only once? This problem is easy to describe, but fiendishly difficult to solve for an even relatively small number of cities. This inherent difficulty is what made the problem interesting in Adleman's eyes, “interesting” being, to a mathematician, a synonym for “hard”.
Len decided to build his computer using the simplest possible algorithm; generate all possible answers (right or wrong), and then throw away the wrong ones. He would build a molecular haystack of answers, and then throw away huge swathes of hay encoding bad answers until he was left with the needle encoding the correct solution (of which there may be just a single copy). For Adleman, the key to his approach was that you can make DNA in the laboratory. A machine the size of a microwave oven will sit in a lab connected to four pots, each containing either A, G, C or T. Type in the sequence you require, and the machine gets to work, threading the letters together like molecular beads on a necklace, making trillions of copies of your desired sequence.
Adleman ordered DNA strands representing each city and each flight for his particular problem. Because DNA sticks together to form the double helix in a very well-defined way, he chose his sequences carefully, such that city and flight strands would glue together like Lego blocks to form long chains, each chain encoding a sequence of flights. Because of the sheer numbers involved, he was pretty sure that a chain encoding the single correct answer would self-assemble. The problem then was to get it out. In a way, Len had built a molecular memory, containing a huge file of lines of text. What he then had to do was sort the file, removing lines that were too long or too short, that started or ended with the wrong words, or which contained duplication. He used various standard lab techniques to achieve this, and, after about a week of molecular cutting and sorting, he was left with the correct solution to his problem.
The example that he solved could be figured out in a minute by a bright 10-year-old using a pen and paper. But that wasn't the point. Adleman had realised, for the first time, Feynman's vision of computing using molecules. After he published his paper, there was a flood of interest in the new field of DNA computing, a tide on which I was personally carried. The potential benefits were huge, since we can fit a vast amount of data into a very small volume of DNA. If you consider that every cell with a nucleus in your body contains a copy of your genome - 3 gigabytes of data, corresponding to 200 copies of the Manhattan phone book – you begin to understand just how advanced nature is in terms of information compression. Suddenly my 4 gig iPod nano doesn't look quite so impressive.
After a few years, though, people began to wonder if molecular computing would ever be used for anything important. They were looking for the “killer application”, the thing that people are willing to pay serious money for, like the spreadsheet, that persuaded small businesses to buy their first ever computer. The fundamental issue with Adleman's approach is tied to the difficulty of the problem; as the number of cities grows only slightly, the amount of DNA required to store all possible sequences of flights grows much more quickly; a small increase in the number of cities quickly leads to a requirement for bathtubs full of DNA, which is enough to induce hysterical laughter in even the sanest biologist. Indeed, it was estimated that if Len's algorithm were to be applied to a map with 200 cities in it, the DNA memory required to store all possible routes would weigh more than the Earth.
It would appear that DNA computing has reached the end of the line, if we are to insist on applying it to computational problems in a head-to-head battle against traditional silicon-based computers. Let's be straight, you're never going to be able to go into PC World and buy a DNA-based computer any time soon. When DNA computing first emerged as a discipline, I was dismayed to see a rash of papers making claims that within a few years we'd be cracking military codes using DNA computers and building artificial molecular memories vastly larger than the human brain. I was dismayed because I knew what had happened 30 years previously to the embryonic field of artificial intelligence. Again, hubristic claims were made for their discipline by the young Turks, ranging from personal robot butlers to automated international diplomacy. When the promised benefits failed to materialise, AI suffered a savage backlash in terms of credibility and funding, from which it is only just beginning to recover. I was very keen to avoid the same thing happening to molecular computing, but I, like many others, knew that we needed to look beyond simply using DNA as a tiny memory storage device.
The next key breakthrough was in realising that, far from being simply a very small storage medium that can be manipulated in a test tube, within its natural environment – the cell – DNA carries meaning. As the novelist Richard Powers observes in The Gold Bug Variations, “The punched tape running along the inner seam of the double helix is much more than a repository of enzyme stencils. It packs itself with regulators, suppressors, promoters, case-statements, if-thens.” Computational structures, that is. DNA encodes a program that controls its own execution. DNA, and the cellular machinery that operates on it, pre-dates electronic computers by billions of years. By re-programming the code of life, we may finally be able to take full advantage of the wonderful opportunities offered by biological wetware.
As Oliver observes in his book, “The world is not just a set of places. It is also a set of processes.” This nicely illustrates the shift in thinking that has occurred in the last few years since the human genome has been sequenced. The notion of a human “blueprint” is outdated a useless. A blueprint encodes specific locational information for the various components of whatever it's intended to represent, whether it be a car or a skyscraper. Nowhere in the human genome will you find a section that reads “place two ears, on on either side of head” or “note to self: must fix design for appendix.” Instead, genes talk to one another, turning each other (and often themselves) on and off in a complex molecular dance. The genome is an electrician's worst nightmare, a tangle of wiring and switches, where turning down a dimmer switch in Hull can switch off the Manhattan underground system.
The human genome project (and the many other projects that are sequencing other organisms, from the orang-utan to the onion) is effectively generating a biological “parts catalogue”; a list of well-understood genes, whose behaviour we can predict in particular circumstances. This is the reductionist way of doing science; break things down, in a top-down fashion, into smaller and smaller parts, through a series of levels of description (for example, organism, molecule, atom). The epitome of this approach is the very well-funded physicists smashing together bits of nature in their accelerators in an attempt to discover what some call the God Particle.
Of course, smashing together two cats and seeing what flies off is only going to give you a limited understanding of how cats work, and it'll probably annoy the cats, so the reductionist approach is of limited use to biologists. Systems biology has emerged in recent years to address this, by integrating information from many different levels of complexity. By studying how different biological components interact, rather then just looking at their structure, as before, systems biologists try to understand biological systems from the bottom up.
An even more recent extension of systems biology is synthetic biology. When a chemist discovers a new compound, the first thing they do is break it down into bits, and the next thing they do it try to synthesise it. As Richard Feynman said just before his death, “What I cannot build I cannot understand.” Synthetic biologists play, not with chemicals, but with the genetic components being placed daily in the catalogue. It's where top down meets bottom up – break things down into their genetic parts, and then put them back together in new and interesting ways. By stripping down and rebuilding microbial machines, synthetic biologists hope to better understand their basic biology, as well as getting them to do weird and wonderful things. It's the ultimate scrapheap challenge.
If we told someone in the field of nanotechnology that we had a man-made device that doesn't need batteries, can move around, talk to its friends and even make copies of itself – and all this in a case the size of a bacterium – they would sell their grandmother for a glimpse. Of course, we already have such devices available to us, but we know them better as microbes. Biology is the nanotechnology that works. By modelling and building new genetic circuits, synthetic biologists are ushering in a new era of biological engineering, where microbial devices are built to solve very pressing problems.
As Oliver notes towards the end of his book, the planet is facing a very real energy crisis. One team is therefore trying to build a microbe to produce hydrogen. Another massive problem facing the developing world is that of arsenic contamination in drinking water. A team here in Edinburgh, made up mainly of undergraduates, has built a bacterial sensor that can quickly and easily monitor arsenic concentrations from a well sample, to within safe tolerances. Jay Keasling, a colleague in California has recently been awarded 43 million dollars by the Bill and Melinda Gates Foundation to persuade E. coli to make substances that are alien to them, but which provide the raw ingredients for antimalarial drugs. The drug is found naturally in the wormwood plant, but it's not cheap – providing it to 70 per cent of the malaria victims in Africa would cost $1 billion, and they can be repeatedly infected. It's been estimated that drug companies would need to cover the entire state of Rhode Island in order to grow enough wormwood, so Keasling wants to produce it in vats, eventually at half the cost.
There are, of course, safety issues with synthetic biology, as well as legal and ethical considerations. I worry that people have this idea that the bugs we use are snarling microbes that have to be physically restrained for fear of them erupting from a Petri dish into the face of an unfortunate researcher, like something from the Alien movies. In reality, the bacteria used in synthetic biology experiments are docile creatures, pathetic even, the crack addicts of the microbial world. They have to be nurtured and cossetted, fed a very specific nutrient brew. Like some academics, they wouldn't last two minutes in the real world. Of course, nature has a habit of weeding out the weak and encouraging the fit, so we still have to be very careful and build in as many safeguards as are practical. The potential for using synthetic biology for weaponry is, to my mind, overstated. As one of the leading researchers said to me, “If I were a terrorist looking to commit a bio-based atrocity, there are much cheaper and easier ways to do it than engineering a specific microbe – anthrax, say.” Synthetic biology will not, in the foreseeable future, return many “bangs per buck”.
Many of the legal concerns centre on the patenting of gene sequences. This was going on well before synthetic biology, but it recently hit the headlines when Craig Venter, head of the private corporation that tied with the Human Genome Project, announced that they intended to patent a synthetic organism.
We must remember that Venter is, first and foremost, a businessman, and it is very much in his interests to keep his company in the public eye. The scientific rationale for some of these patents is not immediately clear. But we should also remember that, for every Craig Venter, there are probably ten or more Jay Keaslings, placing their research in the public domain and working in an open and transparent fashion for the greater good.
On that positive note, I'd like to thank you for listening, and I'll stop there.
In 1959, a great personal hero of mine, the Nobel Prize-winning physicist Richard Feynman gave a visionary talk entitled “There's Plenty of Room at the Bottom”. In his speech, Feynman outlined the possibility of individual molecules, even individual atoms making up the component parts of computers in the future. Remember, this was back when computers filled entire rooms, and were tended by teams of lab-coated technicians, so the idea that you could compute with individual molecules was pretty outlandish. I was struck by a quotation in Oliver's book, attributed to the microbiologist A. J. Kluyver, who said, over fifty years ago, that “The most fundamental character of the living state is the occurrence in parts of the cell of a continuous and directed movement of electrons.” At their most basic, level computers work in exactly the same way; by funnelling electrons around silicon circuits, so I think this hints at the linkages between biology and computers that are only now coming to fruition.
Indeed, it wasn't until 1994 that someone demonstrated, for the first time, the feasibility of building computers from molecular-scale bits. Feynman's vision had waited, not only for the technology to catch up, but for a person with the required breadth of understanding and the will to try something slightly bizarre. That person was Len Adleman, who won the computer science equivalent of the Nobel Prize for his role in the development of the encryption scheme that protects our financial details whenever we buy something on the Internet. Len has always had an interest in biology; when one of his students showed him a program that could take over other programs and force them to replicate it, Len said “Hmmm.... that looks very much like how a virus behaves.” The student was Fred Cohen, author of the first ever computer virus, and Len's term stuck. (Update, 2/9/07: Cohen made the first reference to a "computer virus" in an academic article, but did not write the first virus).
One night in the early 90's, Len was lying in bed reading a classic molecular biology textbook. He came across the section describing a particular enzyme inside the cell that reads and copies DNA, and he was struck by its similarity with an abstract device in computer science known as the Turing Machine. By bringing together two seemingly disparate concepts, Adleman knew at once that, in his own words, “Geez, these things could compute.”
He found a lab at the University of Southern California, where he is a professor, and got down to building a molecular computer. He knew that DNA, the molecule of life that contains the instructions needed to build every organism on the planet, from a slug to.... John Redwood can be thought of as a series of characters from the set A, G, C and T, each character being the first letter of the name of a particular chemical. The title of the film Gattaca, which considers a dystopian future in which genetic discrimination defines a society, is simply a string of characters from the alphabet A, G, C and T.
As Oliver highlights in his own book, molecular biology has always been about the transformation of information, usually inside the living cell. This information is coded in the AGCT sequences of genes and in the proteins that these genes represent. Adleman immediately saw how this mechanism could be harnessed, not to represent proteins, but to store digital data, just like a computer encodes a file as a long sequence of zeroes and ones.
Adleman decided to use this fact to solve a small computational problem. Some of you might have heard of the Travelling Salesman Problem, and Adleman's was a variant of that; given a set of cities connected by flights, does there exist a sequence of flights that starts and ends at particular cities, and which visits every other city only once? This problem is easy to describe, but fiendishly difficult to solve for an even relatively small number of cities. This inherent difficulty is what made the problem interesting in Adleman's eyes, “interesting” being, to a mathematician, a synonym for “hard”.
Len decided to build his computer using the simplest possible algorithm; generate all possible answers (right or wrong), and then throw away the wrong ones. He would build a molecular haystack of answers, and then throw away huge swathes of hay encoding bad answers until he was left with the needle encoding the correct solution (of which there may be just a single copy). For Adleman, the key to his approach was that you can make DNA in the laboratory. A machine the size of a microwave oven will sit in a lab connected to four pots, each containing either A, G, C or T. Type in the sequence you require, and the machine gets to work, threading the letters together like molecular beads on a necklace, making trillions of copies of your desired sequence.
Adleman ordered DNA strands representing each city and each flight for his particular problem. Because DNA sticks together to form the double helix in a very well-defined way, he chose his sequences carefully, such that city and flight strands would glue together like Lego blocks to form long chains, each chain encoding a sequence of flights. Because of the sheer numbers involved, he was pretty sure that a chain encoding the single correct answer would self-assemble. The problem then was to get it out. In a way, Len had built a molecular memory, containing a huge file of lines of text. What he then had to do was sort the file, removing lines that were too long or too short, that started or ended with the wrong words, or which contained duplication. He used various standard lab techniques to achieve this, and, after about a week of molecular cutting and sorting, he was left with the correct solution to his problem.
The example that he solved could be figured out in a minute by a bright 10-year-old using a pen and paper. But that wasn't the point. Adleman had realised, for the first time, Feynman's vision of computing using molecules. After he published his paper, there was a flood of interest in the new field of DNA computing, a tide on which I was personally carried. The potential benefits were huge, since we can fit a vast amount of data into a very small volume of DNA. If you consider that every cell with a nucleus in your body contains a copy of your genome - 3 gigabytes of data, corresponding to 200 copies of the Manhattan phone book – you begin to understand just how advanced nature is in terms of information compression. Suddenly my 4 gig iPod nano doesn't look quite so impressive.
After a few years, though, people began to wonder if molecular computing would ever be used for anything important. They were looking for the “killer application”, the thing that people are willing to pay serious money for, like the spreadsheet, that persuaded small businesses to buy their first ever computer. The fundamental issue with Adleman's approach is tied to the difficulty of the problem; as the number of cities grows only slightly, the amount of DNA required to store all possible sequences of flights grows much more quickly; a small increase in the number of cities quickly leads to a requirement for bathtubs full of DNA, which is enough to induce hysterical laughter in even the sanest biologist. Indeed, it was estimated that if Len's algorithm were to be applied to a map with 200 cities in it, the DNA memory required to store all possible routes would weigh more than the Earth.
It would appear that DNA computing has reached the end of the line, if we are to insist on applying it to computational problems in a head-to-head battle against traditional silicon-based computers. Let's be straight, you're never going to be able to go into PC World and buy a DNA-based computer any time soon. When DNA computing first emerged as a discipline, I was dismayed to see a rash of papers making claims that within a few years we'd be cracking military codes using DNA computers and building artificial molecular memories vastly larger than the human brain. I was dismayed because I knew what had happened 30 years previously to the embryonic field of artificial intelligence. Again, hubristic claims were made for their discipline by the young Turks, ranging from personal robot butlers to automated international diplomacy. When the promised benefits failed to materialise, AI suffered a savage backlash in terms of credibility and funding, from which it is only just beginning to recover. I was very keen to avoid the same thing happening to molecular computing, but I, like many others, knew that we needed to look beyond simply using DNA as a tiny memory storage device.
The next key breakthrough was in realising that, far from being simply a very small storage medium that can be manipulated in a test tube, within its natural environment – the cell – DNA carries meaning. As the novelist Richard Powers observes in The Gold Bug Variations, “The punched tape running along the inner seam of the double helix is much more than a repository of enzyme stencils. It packs itself with regulators, suppressors, promoters, case-statements, if-thens.” Computational structures, that is. DNA encodes a program that controls its own execution. DNA, and the cellular machinery that operates on it, pre-dates electronic computers by billions of years. By re-programming the code of life, we may finally be able to take full advantage of the wonderful opportunities offered by biological wetware.
As Oliver observes in his book, “The world is not just a set of places. It is also a set of processes.” This nicely illustrates the shift in thinking that has occurred in the last few years since the human genome has been sequenced. The notion of a human “blueprint” is outdated a useless. A blueprint encodes specific locational information for the various components of whatever it's intended to represent, whether it be a car or a skyscraper. Nowhere in the human genome will you find a section that reads “place two ears, on on either side of head” or “note to self: must fix design for appendix.” Instead, genes talk to one another, turning each other (and often themselves) on and off in a complex molecular dance. The genome is an electrician's worst nightmare, a tangle of wiring and switches, where turning down a dimmer switch in Hull can switch off the Manhattan underground system.
The human genome project (and the many other projects that are sequencing other organisms, from the orang-utan to the onion) is effectively generating a biological “parts catalogue”; a list of well-understood genes, whose behaviour we can predict in particular circumstances. This is the reductionist way of doing science; break things down, in a top-down fashion, into smaller and smaller parts, through a series of levels of description (for example, organism, molecule, atom). The epitome of this approach is the very well-funded physicists smashing together bits of nature in their accelerators in an attempt to discover what some call the God Particle.
Of course, smashing together two cats and seeing what flies off is only going to give you a limited understanding of how cats work, and it'll probably annoy the cats, so the reductionist approach is of limited use to biologists. Systems biology has emerged in recent years to address this, by integrating information from many different levels of complexity. By studying how different biological components interact, rather then just looking at their structure, as before, systems biologists try to understand biological systems from the bottom up.
An even more recent extension of systems biology is synthetic biology. When a chemist discovers a new compound, the first thing they do is break it down into bits, and the next thing they do it try to synthesise it. As Richard Feynman said just before his death, “What I cannot build I cannot understand.” Synthetic biologists play, not with chemicals, but with the genetic components being placed daily in the catalogue. It's where top down meets bottom up – break things down into their genetic parts, and then put them back together in new and interesting ways. By stripping down and rebuilding microbial machines, synthetic biologists hope to better understand their basic biology, as well as getting them to do weird and wonderful things. It's the ultimate scrapheap challenge.
If we told someone in the field of nanotechnology that we had a man-made device that doesn't need batteries, can move around, talk to its friends and even make copies of itself – and all this in a case the size of a bacterium – they would sell their grandmother for a glimpse. Of course, we already have such devices available to us, but we know them better as microbes. Biology is the nanotechnology that works. By modelling and building new genetic circuits, synthetic biologists are ushering in a new era of biological engineering, where microbial devices are built to solve very pressing problems.
As Oliver notes towards the end of his book, the planet is facing a very real energy crisis. One team is therefore trying to build a microbe to produce hydrogen. Another massive problem facing the developing world is that of arsenic contamination in drinking water. A team here in Edinburgh, made up mainly of undergraduates, has built a bacterial sensor that can quickly and easily monitor arsenic concentrations from a well sample, to within safe tolerances. Jay Keasling, a colleague in California has recently been awarded 43 million dollars by the Bill and Melinda Gates Foundation to persuade E. coli to make substances that are alien to them, but which provide the raw ingredients for antimalarial drugs. The drug is found naturally in the wormwood plant, but it's not cheap – providing it to 70 per cent of the malaria victims in Africa would cost $1 billion, and they can be repeatedly infected. It's been estimated that drug companies would need to cover the entire state of Rhode Island in order to grow enough wormwood, so Keasling wants to produce it in vats, eventually at half the cost.
There are, of course, safety issues with synthetic biology, as well as legal and ethical considerations. I worry that people have this idea that the bugs we use are snarling microbes that have to be physically restrained for fear of them erupting from a Petri dish into the face of an unfortunate researcher, like something from the Alien movies. In reality, the bacteria used in synthetic biology experiments are docile creatures, pathetic even, the crack addicts of the microbial world. They have to be nurtured and cossetted, fed a very specific nutrient brew. Like some academics, they wouldn't last two minutes in the real world. Of course, nature has a habit of weeding out the weak and encouraging the fit, so we still have to be very careful and build in as many safeguards as are practical. The potential for using synthetic biology for weaponry is, to my mind, overstated. As one of the leading researchers said to me, “If I were a terrorist looking to commit a bio-based atrocity, there are much cheaper and easier ways to do it than engineering a specific microbe – anthrax, say.” Synthetic biology will not, in the foreseeable future, return many “bangs per buck”.
Many of the legal concerns centre on the patenting of gene sequences. This was going on well before synthetic biology, but it recently hit the headlines when Craig Venter, head of the private corporation that tied with the Human Genome Project, announced that they intended to patent a synthetic organism.
We must remember that Venter is, first and foremost, a businessman, and it is very much in his interests to keep his company in the public eye. The scientific rationale for some of these patents is not immediately clear. But we should also remember that, for every Craig Venter, there are probably ten or more Jay Keaslings, placing their research in the public domain and working in an open and transparent fashion for the greater good.
On that positive note, I'd like to thank you for listening, and I'll stop there.
Friday, August 24, 2007
My contribution to the synthetic biology debate
You may recall that the Royal Society is soliciting opinions on various aspects of the field of synthetic biology. What follows is a lightly edited version of my own submission, which I sent off today.
In what follows, I highlight some concerns and dangers, speaking as someone who has an definite interest in the field flourishing (and would therefore wish to see these concerns addressed).
1. Terminology
The first concern is over the term “synthetic biology” itself. The two main issues are “what does it mean?” and “what does it cover?” As pointed out at the BBSRC workshop, clinicians have used the term for a while to refer to prosthetic devices. In attempting to offer a fixed definition of the term, the community runs the risk of becoming overly exclusive at a premature stage. However, there is also a risk that “synthetic biology” will become a “catch-all” term that is too loosely applied. The emphasis on the term “biology” may also serve to alienate mathematicians, physicists, computer scientists and others, who may (wrongly) feel that they have no expertise to offer a “biological” discipline. As a counter-example, witness the success of the field of bioinformatics, which would appear to fairly represent the disciplinary expertise in the field (in terms of the general composition of the term, rather than the relative lengths of its components). As a very crude experiment, I searched in Google for both “computational biology” and “bioinformatics”; the first term returned around 1,530,000 hits, the second around 14,000,000.
This leads on to the issue of “language barriers”. This is always an issue in any new field that involves the collision of two or more (often very dissimilar) disciplines. Being seen to publically ask “stupid questions” is a daunting prospect to most young scientists, and yet many of the major breakthroughs have occurred through just that. This opens up the wider debate on inter-disciplinarity in 21st century science, and how we might best prepare its practitioners. Do we give students a broad, shallow curriculum to allow them to make connections, without necessarily having the background to “drill deeper” if required, or do we stick to the “old model” of “first degree” and subsequent training? My own intuition is that it is far better to intensively train in a single field at the outset, and then offer the opportunity to “cherry pick” topics from a different discipline at a later stage. This educational debate is, however, not one that should be the sole preserve of synthetic biology!
2. Expectation Management
Even when biologists and (say) computer scientists can agree a suitable shared terminology, there is still the risk of a mismatch occurring in terms of expectations of what might be achieved. For example, the notion of “scalability” might mean very different things to a computer scientist and a microbiologist. To the former, it means being able to increase by several orders of magnitude the number of data items processed by an algorithm, or double the (already vast) number of transistors we may place on the surface of a computer chip. To a biologist, the idea of scalability might currently be very different:
“What's needed to make synthetic biology successful, Rabaey said, are the same three elements that made microelectronics successful. These are a scalable, reliable manufacturing process; a scalable design methodology; and a clear understanding of a computational model. "This is not biology, this is not physics, this is hard core engineering," Rabaey said.
In electronics, photolithography provides a scalable, reliable manufacturing process for designs involving millions of elements. Biology has a long way to go. What's needed, Rabaey said, is a way to generate thousands of genes reliably in a very short time period with very few errors. The difference between what's available and what's needed is about a trillion to one.”
3. Conceptual Issues
As the leading nanotechnologist (and FRS) Richard Jones has pointed out, his field was dominated from an early stage by often inappropriate analogies with mechanical engineering (e.g., cogs). It may well be that case that we are in danger of the same thing happening with synthetic biology, where computer scientists impose rigid circuit/software design principles on "softer", more “fuzzy” substrates. Jones quotes, on his blog, an article in the New York Times:
“Most people in synthetic biology are engineers who have invaded genetics. They have brought with them a vocabulary derived from circuit design and software development that they seek to impose on the softer substance of biology. They talk of modules — meaning networks of genes assembled to perform some standard function — and of “booting up” a cell with new DNA-based instructions, much the way someone gets a computer going.”
4. Complexity
The issue of "grey goo" has persistently dogged the field of nanotechnology, and it would be tempting to dismiss similar criticisms of synthetic biology as well-intentioned but ultimately uninformed. However, if synthetic biologists are to avoid the mistake that researchers in GM research made (that is, to appear arrogant and dismissive, leading to mass public protest and restrictive legislation), then we should acknowledge and address the very real possibility of the biological systems under study behaving in very unpredictable ways. Anyone who has any degree of contact with studying biosystems will understand the notion of complexity; components that are connected in an unknown fashion behave in unpredictable ways, which may include evasion of any control mechanisms that have been put in place. As Douglas Kell and his colleagues have observed, it is perfectly possible to alter parameters of a system on an individual basis, and see no effect, only to observe wild variations in behaviour when exactly the same tweak is applied to two or more parameters at the same time. Working in an interdisciplinary fashion may address this issue, at least in part, if modellers work closely with bench scientists in a cycle of cooperation. Once again invoking the issue of scalability, studying the behaviour of complex biosystems through modelling alone will quickly become infeasible, due the the combinatorial explosion in the size of the search space (of parameter values). By actually making or modifying the systems under study in the lab, the problem may be reduced to manageable proportions.
5. Hype
In my own book, Genesis Machines (Atlantic Books, 2006), I illustrate the risk of promising too much at an early stage by describing the story of the “AI winter”. In the 1960s, researchers in artificial intelligence (AI) had promised human-level intelligence “in a box” within twenty years. By issuing such wild predictions, AI researchers set themselves up for a monumental fall, and, when the promised benefits failed to accrue, funding was slashed and interest dwindled. This AI winter (by analogy with “nuclear winter”) affected the field for over 15 years, and it would be disappointing (to say the least) if the same thing were to happen to synthetic biology.
Hubristic claims for synthetic biology should be avoided wherever possible; without singling out particular groups, I have already seen several predictions (again, often conflated with ambitions) that have absolutely no realistic chance of coming to fruition in any meaningful time-scale (if at all). In this more “media savvy” age, perhaps practitioners in synthetic biology might benefit, as their AI counterparts did not, from media training (I have personally benefited (June 2004) from the course provided by the Royal Society, and perhaps the Society might consider a “mass participation” version for new entrants to the field).
In what follows, I highlight some concerns and dangers, speaking as someone who has an definite interest in the field flourishing (and would therefore wish to see these concerns addressed).
1. Terminology
The first concern is over the term “synthetic biology” itself. The two main issues are “what does it mean?” and “what does it cover?” As pointed out at the BBSRC workshop, clinicians have used the term for a while to refer to prosthetic devices. In attempting to offer a fixed definition of the term, the community runs the risk of becoming overly exclusive at a premature stage. However, there is also a risk that “synthetic biology” will become a “catch-all” term that is too loosely applied. The emphasis on the term “biology” may also serve to alienate mathematicians, physicists, computer scientists and others, who may (wrongly) feel that they have no expertise to offer a “biological” discipline. As a counter-example, witness the success of the field of bioinformatics, which would appear to fairly represent the disciplinary expertise in the field (in terms of the general composition of the term, rather than the relative lengths of its components). As a very crude experiment, I searched in Google for both “computational biology” and “bioinformatics”; the first term returned around 1,530,000 hits, the second around 14,000,000.
This leads on to the issue of “language barriers”. This is always an issue in any new field that involves the collision of two or more (often very dissimilar) disciplines. Being seen to publically ask “stupid questions” is a daunting prospect to most young scientists, and yet many of the major breakthroughs have occurred through just that. This opens up the wider debate on inter-disciplinarity in 21st century science, and how we might best prepare its practitioners. Do we give students a broad, shallow curriculum to allow them to make connections, without necessarily having the background to “drill deeper” if required, or do we stick to the “old model” of “first degree” and subsequent training? My own intuition is that it is far better to intensively train in a single field at the outset, and then offer the opportunity to “cherry pick” topics from a different discipline at a later stage. This educational debate is, however, not one that should be the sole preserve of synthetic biology!
2. Expectation Management
Even when biologists and (say) computer scientists can agree a suitable shared terminology, there is still the risk of a mismatch occurring in terms of expectations of what might be achieved. For example, the notion of “scalability” might mean very different things to a computer scientist and a microbiologist. To the former, it means being able to increase by several orders of magnitude the number of data items processed by an algorithm, or double the (already vast) number of transistors we may place on the surface of a computer chip. To a biologist, the idea of scalability might currently be very different:
“What's needed to make synthetic biology successful, Rabaey said, are the same three elements that made microelectronics successful. These are a scalable, reliable manufacturing process; a scalable design methodology; and a clear understanding of a computational model. "This is not biology, this is not physics, this is hard core engineering," Rabaey said.
In electronics, photolithography provides a scalable, reliable manufacturing process for designs involving millions of elements. Biology has a long way to go. What's needed, Rabaey said, is a way to generate thousands of genes reliably in a very short time period with very few errors. The difference between what's available and what's needed is about a trillion to one.”
3. Conceptual Issues
As the leading nanotechnologist (and FRS) Richard Jones has pointed out, his field was dominated from an early stage by often inappropriate analogies with mechanical engineering (e.g., cogs). It may well be that case that we are in danger of the same thing happening with synthetic biology, where computer scientists impose rigid circuit/software design principles on "softer", more “fuzzy” substrates. Jones quotes, on his blog, an article in the New York Times:
“Most people in synthetic biology are engineers who have invaded genetics. They have brought with them a vocabulary derived from circuit design and software development that they seek to impose on the softer substance of biology. They talk of modules — meaning networks of genes assembled to perform some standard function — and of “booting up” a cell with new DNA-based instructions, much the way someone gets a computer going.”
4. Complexity
The issue of "grey goo" has persistently dogged the field of nanotechnology, and it would be tempting to dismiss similar criticisms of synthetic biology as well-intentioned but ultimately uninformed. However, if synthetic biologists are to avoid the mistake that researchers in GM research made (that is, to appear arrogant and dismissive, leading to mass public protest and restrictive legislation), then we should acknowledge and address the very real possibility of the biological systems under study behaving in very unpredictable ways. Anyone who has any degree of contact with studying biosystems will understand the notion of complexity; components that are connected in an unknown fashion behave in unpredictable ways, which may include evasion of any control mechanisms that have been put in place. As Douglas Kell and his colleagues have observed, it is perfectly possible to alter parameters of a system on an individual basis, and see no effect, only to observe wild variations in behaviour when exactly the same tweak is applied to two or more parameters at the same time. Working in an interdisciplinary fashion may address this issue, at least in part, if modellers work closely with bench scientists in a cycle of cooperation. Once again invoking the issue of scalability, studying the behaviour of complex biosystems through modelling alone will quickly become infeasible, due the the combinatorial explosion in the size of the search space (of parameter values). By actually making or modifying the systems under study in the lab, the problem may be reduced to manageable proportions.
5. Hype
In my own book, Genesis Machines (Atlantic Books, 2006), I illustrate the risk of promising too much at an early stage by describing the story of the “AI winter”. In the 1960s, researchers in artificial intelligence (AI) had promised human-level intelligence “in a box” within twenty years. By issuing such wild predictions, AI researchers set themselves up for a monumental fall, and, when the promised benefits failed to accrue, funding was slashed and interest dwindled. This AI winter (by analogy with “nuclear winter”) affected the field for over 15 years, and it would be disappointing (to say the least) if the same thing were to happen to synthetic biology.
Hubristic claims for synthetic biology should be avoided wherever possible; without singling out particular groups, I have already seen several predictions (again, often conflated with ambitions) that have absolutely no realistic chance of coming to fruition in any meaningful time-scale (if at all). In this more “media savvy” age, perhaps practitioners in synthetic biology might benefit, as their AI counterparts did not, from media training (I have personally benefited (June 2004) from the course provided by the Royal Society, and perhaps the Society might consider a “mass participation” version for new entrants to the field).
Friday, August 17, 2007
For the love of ants
To be published next week, one book on my Amazon wishlist is titled The Ants Are My Friends. Students of popular music may recognise the phrase as one of the great misheard lyrics of our time, up there with "Beelzebub had a devil for a sideboard", rather than an expression of insect infatuation (the response being, of course, "blowing in the wind").
But I rather like the idea of ants being my friends. I've always held these misunderstood creatures in high regard, and was charmed by the story, recounted in Surely You're Joking, Mr. Feynman! (p. 91 in the Vintage edition), of how Richard Feynman investigated ant trail-following behaviour in his Princeton accomodation. He eventually used his findings to persuade an ant colony to leave his larder; "No poison, you gotta be humane to the ants!"
Anyone who has ever watched an ant colony at work cannot fail to be entranced by its beauty and efficiency. A single colony can strip an entire moose carcass in under two hours, and their work is coordinated in an inherently decentralised fashion (that is, there is no "head ant" giving out orders). An ant colony can be considered as a class of "super-organism", that is, a "virtual" organism made up of many other single organisms. Other examples include bacterial colonies and (arguably) the Earth itself.
Ants communicate remotely by way of pheromones, chemicals that generate some sort of response amongst members of the same species. When ants forage for food, they lay a particular pheromone on the ground once they've found a source. When this signal is detected by other ants, they follow the trail and reinforce it by laying pheromone themselves. Chemical signals also evaporate over time, which allows colonies to "forget" good solutions (i.e., paths) and construct new solutions if the environment changes (e.g., a stone falls onto an existing path).
By describing this mechanism in abstract terms, computer scientists have managed to harness the power of positive feedback in order to solve difficult computational problems. Perhaps the leading scientist in the field of ant colony optimization (ACO) is Marco Dorigo, and he has described how to use models of artificial ants to solve the problem of how to route text messages through a busy network of mobile base stations. We've also done some initial work on how ants build spatial structures, using an abstract model of pheromone deposition to explain how certain species can construct "bullseye"-like patterns of differently-sized objects.
Fundamentally, ongoing work in ACO reflects a wider interest in the notion of decentralised control. Rather than controlling everything from "on high" with global instructions, "bottom up" control emphasises the value of small, local interactions in keeping systems running smoothly. Software packages such as Netlogo have brought so-called agent-based modelling to a wider audience. I've just taken on a Ph.D. student to study the evacuation of tall buildings using this approach, and it's clear that, with ever-increasing computational power being available, the notion of simulating large systems of interacting entities will gain increasing influence.
But I rather like the idea of ants being my friends. I've always held these misunderstood creatures in high regard, and was charmed by the story, recounted in Surely You're Joking, Mr. Feynman! (p. 91 in the Vintage edition), of how Richard Feynman investigated ant trail-following behaviour in his Princeton accomodation. He eventually used his findings to persuade an ant colony to leave his larder; "No poison, you gotta be humane to the ants!"
Anyone who has ever watched an ant colony at work cannot fail to be entranced by its beauty and efficiency. A single colony can strip an entire moose carcass in under two hours, and their work is coordinated in an inherently decentralised fashion (that is, there is no "head ant" giving out orders). An ant colony can be considered as a class of "super-organism", that is, a "virtual" organism made up of many other single organisms. Other examples include bacterial colonies and (arguably) the Earth itself.
Ants communicate remotely by way of pheromones, chemicals that generate some sort of response amongst members of the same species. When ants forage for food, they lay a particular pheromone on the ground once they've found a source. When this signal is detected by other ants, they follow the trail and reinforce it by laying pheromone themselves. Chemical signals also evaporate over time, which allows colonies to "forget" good solutions (i.e., paths) and construct new solutions if the environment changes (e.g., a stone falls onto an existing path).
By describing this mechanism in abstract terms, computer scientists have managed to harness the power of positive feedback in order to solve difficult computational problems. Perhaps the leading scientist in the field of ant colony optimization (ACO) is Marco Dorigo, and he has described how to use models of artificial ants to solve the problem of how to route text messages through a busy network of mobile base stations. We've also done some initial work on how ants build spatial structures, using an abstract model of pheromone deposition to explain how certain species can construct "bullseye"-like patterns of differently-sized objects.
Fundamentally, ongoing work in ACO reflects a wider interest in the notion of decentralised control. Rather than controlling everything from "on high" with global instructions, "bottom up" control emphasises the value of small, local interactions in keeping systems running smoothly. Software packages such as Netlogo have brought so-called agent-based modelling to a wider audience. I've just taken on a Ph.D. student to study the evacuation of tall buildings using this approach, and it's clear that, with ever-increasing computational power being available, the notion of simulating large systems of interacting entities will gain increasing influence.
Genesis Machines in the USA
I'm delighted to report that Atlantic have signed a deal to publish Genesis Machines in the USA. It's slated to appear on April 3rd of next year, and will be published by the Overlook Press (preorder here).
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