Archive for the ‘Bio-nanotechnology’ Category

A billion dollar nanotech spinout?

Monday, March 19th, 2012

The Oxford University spin-out Oxford Nanopore Technologies created a stir last month by announcing that it would be bringing to market this year systems to read out the sequence of individual DNA molecules by threading them through nanopores. It’s claimed that this will allow a complete human genome to be sequenced in about 15 minutes for a few thousand dollars; the company also is introducing a cheap, disposable sequencer which will sell for less that $900. Speculation has now begun about the future of the company, with valuations of $1-2 billion dollars being discussed if they decide to take the company public in the next 18 months.

It’s taken a while for this idea of sequencing a single DNA molecule by directly reading out its bases to come to fruition. The original idea came from David Deamer and Harvard’s Dan Branton in the mid-1990s; from Hagen Bayley, in Oxford, came the idea of using an engineered derivative of a natural pore-forming protein to form the hole through which the DNA is threaded. I’ve previously reported progress towards this goal here, in 2005, and in more detail here, in 2007. The Oxford Nanopore announcement gives us some clues as to the key developments since then. The working system uses a polymer membrane, rather than a lipid bilayer, to carry the pore array, which undoubtedly makes the system much more robust. The pore is still created from a pore forming protein, though this has been genetically engineered to give greater discrimination between different combinations of bases as the DNA is threaded through the hole. And, perhaps most importantly, an enzyme is used to grab DNA molecules from solution and feed them through the pore. In practise, the system will be sold as a set of modular units containing the electronics and interface, together with consumables cartridges, presumably including the nanopore arrays and the enzymes. The idea is to take single molecule analysis beyond DNA to include RNA and proteins, as well as various small molecules, with a different cartridge being available for each type of experiment. This will depend on the success of their program to develop a whole family of different pores able to discriminate between different types of molecules.

What will the impact of this development be, if everything works as well as is being suggested? (The prudent commentator should stress the if here, as we haven’t yet seen any independent trials of the technology). Much has already been written about the implications of cheap – less than $1000 – sequencing of the human genome, but I can’t help wondering whether this may not actually be the big story here. And in any case, that goal may end being reached with or without Oxford Nanopore, as this recent Nature News article makes clear. We still don’t know whether the Oxford Nanopore technique will be yet competitive on accuracy and price with the other contending approaches. I wonder, though, whether we are seeing here something from the classic playbook for a disruptive innovation. The $900 device in particular looks like it’s intended to create new markets for cheap, quick and dirty sequencing, to provide an income stream while the technology is improved further – with better, more selective pores and better membranes (inevitably, perhaps, Branton’s group at Harvard reported using graphene membranes for threading DNA in Nature last year). As computers continue to get faster, cheaper and more powerful, the technology will automatically benefit from these advances too – fragmentary and perhaps imperfect sequence information has much greater value in the context of vast existing sequence libraries and the data processing power to use them. Perhaps applications for this will be found in forensic and environmental science, diagnostics, microbiology and synthetic biology. The emphasis on molecules other than DNA is interesting too; single molecule identification and sequencing of RNA opens up the possibility of rapidly identifying what genes are being transcribed in a cell at a given moment (the so-called “transcriptome”).

The impact on the investment markets for nanotechnology is likely to be substantial. Existing commercialisation efforts around nanotechnology have been disappointing so far, but a company success on the scale now being talked about would undoubtedly attract more money into the area – perhaps it might also persuade some of the companies currently sitting on huge piles of cash that they might usefully invest some of this in a little more research and development. What’s significant about Oxford Nanopore is that it is operating in a sweet spot between the mundane and the far-fetched. It’s not a nanomaterials company, essentially competing in relatively low margin speciality chemicals, nor is it trying to make a nanofactory or nanoscale submarine or one of the other more radical visions of the nanofuturists. Instead, it’s using the lessons of biology – and indeed some of the components of molecular biology – to create a functional device that operates on the true single molecule level to fill real market needs. It also seems to be displaying a commendable determination to capture all the value of its inventions, rather than licensing its IP to other, bigger companies.

Finally, not the least of the impacts of a commercial and technological success on the scale being talked about would be on nanotechnology itself as a discipline. In the last few years the field’s early excitement has been diluted by a sense of unfulfilled promise, especially, perhaps, in the UK; last year I asked “Why has the UK given up on nanotechnology?” Perhaps it will turn out that some of that disillusionment was premature.

A little history of bionanotechnology and nanomedicine

Monday, December 19th, 2011

I wrote this piece as a briefing note in connection with a study being carried out by the Nuffield Council on Bioethics about Emerging Biotechnologies. I’m not sure whether bionanotechnology or nanomedicine should be considered as emerging biotechnologies, but this is an attempt to sketch out the connections.

Nanotechnology is not a single technology; instead it refers to a wide range of techniques and methods for manipulating matter on length scales from a nanometer or so – i.e. the typical size of molecules – to hundreds of nanometers, with the aim of creating new materials and functional devices. Some of these methods represent the incremental evolution of well-established techniques of applied physics, chemistry and materials science. In other cases, the techniques are at a much earlier state, with promises about their future power being based on simple proof-of-principle demonstrations.

Although nanotechnology has its primary roots in the physical sciences, it has always had important relationships with biology, both at the rhetorical level and in practical outcomes. The rhetorical relationship derives from the observation that the fundamental operations of cell biology take place at the nanoscale, so one might expect there to be something particularly powerful about interventions in biology that take place on this scale. Thus the idea of “nanomedicine” has been prominent in the promises made on behalf of nanotechnology from its earliest origins, and as a result has entered popular culture in the form of the exasperating but ubiquitous image of the “nanobot” – a robot vessel on the nano- or micro- scale, able to navigate through a patient’s bloodstream and effect cell-by-cell repairs. This was mentioned as a possibility in Richard Feynman’s 1959 lecture, “Plenty of Room at the Bottom”, which is widely (though retrospectively) credited as the founding manifesto of nanotechnology, but it was already at this time a common device in science fiction. The frequency with which conventionally credentialed nanoscientists have argued that this notion is impossible or impracticable, at least as commonly envisioned, has had little effect on the enduring hold it has on the popular imagination.
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Three things that Synthetic Biology should learn from Nanotechnology

Friday, April 15th, 2011

I’ve been spending the last couple of days at a meeting about synthetic biology – The economic and social life of synthetic biology. This has been a hexalateral meeting involving the national academies of science and engineering of the UK, China and the USA. The last session was a panel discussion, in which I was invited to reflect on the lessons to be learnt for new emerging technologies like synthetic biology from the experience of nanotechnology. This is more or less what I said.

It’s quite clear from the many outstanding talks we’ve heard over the last couple of days that synthetic biology will be an important part of the future of the applied life sciences. I’ve been invited to reflect on the lessons that synbio and other emerging technologies might learn from the experience of my own field, nanotechnology. Putting aside the rueful reflection that, like synbio now, nanotechnology was the future once, I’d like to draw out three lessons.

1. Mind that metaphor
Metaphors in science are powerful and useful things, but they come with two dangers:
a. it’s possible to forget that they are metaphors, and to think they truly reflect reality,
b. and even if this is obvious to the scientists using the metaphors, the wider public may not appreciate the distinction.

Synthetic biology has been associated with some very powerful metaphors. There’s the idea of reducing biology to software; people talk about booting up cells with new operating systems. This metaphor underlies ideas like the cell chassis, interchangeable modules, expression operating systems. But it is only a metaphor; biology isn’t really digital and there is an inescabable physicality to the biological world. The molecules that carry information in biology – RNA and DNA – are physical objects embedded in a Brownian world, and it’s as physical objects that they interact with their environment.

Similar metaphors have surrounded nanotechnology, in slogans like “controlling the world atom by atom” and “software control of matter”. They were powerful tools in forming the field, but outside the field they’ve caused confusion. Some have believed these ideas are literally becoming true, notably the transhumanists and singularitarians who rather like the idea of a digital transcendence.

On the opposite side, people concerned about science and technology find plenty to fear in the idea. We’ll see this in synbio if ideas like biohacking get wider currency. Hackers have a certain glamour in technophile circles, but to the rest of the world they write computer viruses and send spam emails. And while the idea of reducing biotech to software engineering is attractive to techie types, don’t forget that the experience of most people of software is that it is buggy, unreliable, annoyingly difficult to use, and obsolete almost from the moment you buy it.

Finally, investors and venture capitalists believed, on the basis of this metaphor, that they’d get returns from nano start-ups on the same timescales that the lucky ones got from dot-com companies, forgetting that, even though you could design a marvellous nanowidget on a computer, you still had to get a chemical company to make it.

2. Blowing bubbles in the economy of promises

Emerging areas of technology all inhabit an economy of promises, in which funding for the now needs to be justified by extravagant claims for the future. These claims may be about the economic impact – “the trillion dollar market” – or on revolutions in fields such as sustainable energy and medicine. It’s essential to be able to make some argument about why research needs to be funded and it’s healthy that we make the effort to anticipate the impact of what we do, but there’s an inevitable tendency for those claimed benefits to inflate to bubble proportions.

The mechanisms by which this inflation takes place are well known. People do believe the metaphors; scientists need to get grants, the media demand big and unqualified claims to attract their attention. Even the process of considering the societal and ethical aspects of research, and of doing public engagement can have the effect of giving credence to the most speculative possible outcomes.

There’s a very familiar tension emerging about synthetic biology – is it a completely new thing, or an evolution of something that’s been going on for some time – i.e. industrial biotechnology? This exactly mirrors a tension within nanotechnology – the promise is sold on the grand vision and the big metaphors, but the achievements are largely based on the aspects of the technology with the most continuity with the past.

The trouble with all bubbles, of course, is that reality catches up on unfulfilled promises, and in this environment people are less forgiving of the reality of the hard constraints faced by any technology. If you overdo the promise, disillusionment will set in amongst funders, governments, investors and the public. This might discredit even the genuine achievements the technology will make possible. Maybe our constant focus on revolutionary innovation blinds us to the real achievements of incremental innovation – a better drug, a more efficient process for processing a biofuel, a new method of pest control, for example.

3. It’s not about risk, it’s about trust

The regulation of new technologies is focused on controlling risks, and it’s important that we try and identify and control those risks as the technology emerges. But there’s a danger in focusing on risk too much. When people talk about emerging technologies, by default it is to risk that conversation turns. But often, it isn’t really risk that is fundamentally worrying people, but trust. In the face of the inevitable uncertainties with new technologies, this makes complete sense. If you can’t be confident in identifying risks in advance, the question you naturally ask is whether the bodies and institutions that are controlling these technologies can be trusted. It must be a priority, then, that we think hard about how to build trust and trustworthy institutions. General principles like transparency and openness will certainly be helpful, but we have to ask whether it is realistic for these principles alone to be maintained in an environment demanding commercial returns from large scale industrial operations.

On Descartes and nanobots

Sunday, May 23rd, 2010

A couple of weeks ago I was interviewed for the Robots podcast special on on 50 years of robotics, and predictions for the next half century. My brief was nanorobots, and you can hear the podcast here. My pitch was that on the nanoscale we’d be looking to nature for inspiration, exploiting design principles such as self-assembly and macromolecular shape change; as a particularly exciting current development I singled out progress in DNA nanotechnology, and in particular the possibility of using this to do molecular logic. As it happens, last week’s edition of Nature included two very interesting papers reporting further developments in this area – Molecular robots guided by prescriptive landscapes from Erik Winfree’s group in Caltech, and A proximity-based programmable DNA nanoscale assembly line from Ned Seeman’s group in NYU.

The context and significance of these advances is well described in a News and Views article (full text); the references to nanorobots and nanoscale assembly lines have led to considerable publicity. James Hayton (who reads the Daily Mail so the rest of us don’t have to), in his 10e-9 blog comments very pertinently on the misleading use of classical nanobot imagery to illustrate this story. The Daily Mail isn’t the only culprit here – even the venerable Nature uses a still from the film Fantastic Voyage to illustrate their story, with the caption “although such machines are still a fantasy, molecular ‘robots’ made of DNA are under development.”

What’s wrong with these illustrations is that they are graphic representations of bad metaphors. DNA nanotechnology falls squarely in the soft nanotechnology paradigm – it depends on the weak interactions by which complementary sequences are recognised to enable the self-assembly of structures whose design is coded within the component molecules themselves, and macromolecular shape changes under the influence of Brownian motion to effect motion. Soft machines aren’t mechanical engineering shrunk, as I’ve written about at length on this blog and elsewhere.

But there’s another, more subtle point here. Our classical conception of a robot is something with sensors feeding information into a central computer, which responds to this sensory input by a computation, which is then effected by the communication of commands to the actuators that drive the robot’s actions. This separation of the “thinking” function of the robot from its sensing and action is something that we find very appealing; we are irresistibly drawn to the analogy with the way we have come to think about human beings since Descartes – as machines animated by an intelligence largely separate from our bodies.

What is striking about these rudimentary DNA robots is that what “intelligence” they possess – their capacity to sense the environment and process this information to determine which of a limited set of outcomes will be effected – arises from the molecules from which the robot is made and their interaction with a (specially designed) environment. There’s no sense in which the robot’s “program” is loaded into it; the program is implicit in the construction of the robot and its interaction with the environment. In this robot, “thought” and “action” are inseparable; the same molecules both store and process information and drive its motion.

In this, these proto-robots operate on similar general principles to bacteria, whose considerable information processing power arises from the interaction of many individual molecules with each other and with their physical environment (as beautifully described in Dennis Bray’s book Wetware: a computer in every living cell). Is this the only way to build a nanobot with the capacity to process and act on information about the environment? I’m not sure, but for the moment it seems to be the direction we’re moving in.

Targeted delivery of siRNA by nanoparticles in humans

Sunday, April 18th, 2010

An important milestone in the use of nanoparticles to deliver therapeutic molecules is reported in this week’s Nature – full paper (subscription required), editors summary. See also this press release. The team, led by Mark Davis from Caltech, used polymer nanoparticles to deliver small interfering RNA (siRNA) molecules into tumour cells in humans, with the aim of preventing the growth of these tumours.

I wrote in more detail about siRNA back in 2005 here. If one can introduce the appropriate siRNA molecules into a cell, they can selectively turn off the expression of any gene in that cell’s genome, potentially giving us a new class of powerful drugs which would be an absolutely specific treatment both for viral diseases and cancers. When I last wrote about this subject, it was clear that the problem of delivering of these small strands of RNA to their target cells was going to be a major barrier to fulfilling the promise of this very exciting new technology. In this paper, we see that substantial progress has been made towards overcoming this barrier. In this study the RNA was incorporated in self-assembled polymer nanoparticles, the surfaces of which were decorated with groups that selectively bind to proteins that are found on the surfaces of the tumour cells being targeted.

The experiments were carried out as part of a phase 1 clinical trial on humans. What the Nature paper shows is that the nanoparticles do indeed accumulate at tumour cells and are incorporated within them (see the micrograph below), and that the siRNA does suppress the synthesis of the particular protein at which it is aimed, a protein which is necessary for the growth of the tumour. If this trial doesn’t demonstrate unacceptable harmful effects, further clinical trials will be needed to demonstrate whether the therapy works clinically to arrest the growth of these tumours.

Targeted nanoparticles carrying therapeutic siRNA molecules entering a tumor cell - Caltech/Swaroop Mishra

Targeted nanoparticles carrying therapeutic siRNA molecules entering a tumor cell - Caltech/Swaroop Mishra

Soft machines and robots

Sunday, July 19th, 2009

Robots is a website featuring regular podcasts about various aspects of robotics; currently it’s featuring a podcast of an interview with me by Sabine Hauert, from EPFL’s Laboratory of Intelligent Systems. This was prompted by my talk at the IEEE Congress on Evolutionary Computing, which essentially was about how to build a nanobot. Regular readers of this blog will not be surprised to hear that a strong theme of both interview and talk is the need to take inspiration from biology when designing “soft machines”, which need to be optimised for the special, and to us very unfamiliar, physics of the nanoworld, rather than using inappropriate design principles derived from macroscopic engineering. For more on this, the interested reader might like to take a look at my earlier essay, “Right and wrong lessons from biology”.

Accelerating evolution in real and virtual worlds

Friday, May 22nd, 2009

Earlier this week I was in Trondheim, Norway, for the IEEE Congress on Evolutionary Computing. Evolutionary computing, as its name suggests, refers to a group of approaches to computer programming that draws inspiration from the natural processes of Darwinian evolution, hoping to capitalise on the enormous power of evolution to find good solutions to complex problems from a very large range of possibilities. How, for example, might one program a robot to carry out a variety of tasks in a changing and unpredictable environment? Rather than an attempting to anticipate all the possible scenarios that your robot might encounter, and then writing control software that specified appropriate behaviours for all these possibilities, one could use evolution to select a robot controller that worked best for your chosen task in a variety of environments.

Evolution may be very effective, but in its natural incarnation it’s also very slow. One way of speeding things up is to operate in a virtual world. I saw a number of talks in which people were using simulations of robots to do the evolution; something like a computer game environment is used to simulate a robot doing a simple task like picking up an object or recognising a shape, with success or failure being used as input in a fitness function, through which the robot controller is allowed to evolve.

Of course, you could just use a real computer game. Simon Lucas, from Essex University, explained to me why classic computer games – his favourite is Ms Pac-Man – offer really challenging exercises in developing software agents. It’s sobering to realise that, while computers can beat a chess grand master, humans still have a big edge on computers in arcade games. The human high-score for Ms Pac-Man is 921,360; in a competition in the 2008 IEEE CEC meeting the winning bot achieved 15,970. Unfortunately I had to leave Trondheim before the results of the 2009 competition were announced, so I don’t know whether this year produced a big breakthrough in this central challenge to computational intelligence.

One talk at the meeting was very definitely rooted in the real, rather than virtual, world – this came from Harris Wang, a graduate student in the group of Harvard Medical School’s George Church. This was a really excellent overview of the potential of synthetic biology. At the core of the talk was a report of a recent piece of work that is due to appear in Nature shortly. This described the re-engineering of an micro-organism to increase its production of the molecule lycopene, the dye that makes tomatoes red (and probably confers significant health benefits, the basis for the seemingly unlikely claim that tomato ketchup is good for you). Notwithstanding the rhetoric of precision and engineering design that often accompanies synthetic biology, what made this project successful was the ability to generate a great deal of genetic diversity and then very rapidly screen these variants to identify the desired changes. To achieve a 500% increase in lycopene production, they needed to make up to 24 simultaneous genetic modifications, knocking out genes involved in competing processes and modifying the regulation of other genes. This produced a space of about 15 billion possible combinatorial variations, from which they screened 100,000 distinct new cell types to find their winner. This certainly qualifies as real-world accelerated evolution.

How to engineer a system that fights back

Sunday, May 10th, 2009

Last week saw the release of a report on synthetic biology from the UK’s Royal Academy of Engineering. The headline call, as reflected in the coverage in the Financial Times, is for the government to develop a strategy for synthetic biology so that the country doesn’t “lose out in the next industrial revolution”. The report certainly plays up the likelihood of high impact applications in the short term – within five to ten years, we’re told, we’ll see synbio based biofuels, “artificial leaf technology” to fix atmospheric carbon dioxide, industrial scale production of materials like spider silk, and in medicine the realisation of personalised drugs. An intimation that progress towards these goals may not be entirely smooth can be found in this news piece from a couple of months ago – A synthetic-biology reality check – which described the abrupt winding up earlier this year of one of the most prominent synbio start-ups, Codon Devices, founded by some of the most prominent US players in the field.

There are a number of competing visions for what synthetic biology might be; this report concentrates on just one of these. This is the idea of identifying a set of modular components – biochemical analogues of simple electronic components – with the aim of creating a set of standard parts from which desired outcomes can be engineered. This way of thinking relies on a series of analogies and metaphors, relating the functions of cell biology with constructs of human-created engineering. Some of these analogies have a sound empirical (and mathematical) basis, like the biomolecular realisation of logic gates and positive and negative feedback.

There is one metaphor that is used a lot in the report which seems to me to be potentially problematic – that’s the idea of a chassis. What’s meant by this is a cell – for example, a bacteria like E.coli – into which the artificial genetic components are introduced in order to produce the desired products. This conjures up an image like the box into which one slots the circuit boards to make a piece of electronic equipment – something that supplies power and interconnections, but which doesn’t have any real intrinsic functionality of its own. It seems to me difficult to argue that any organism is ever going to provide such a neutral, predictable substrate for human engineering – these are complex systems which have their own agenda. To quote from the report on a Royal Society Discussion Meeting about synthetic biology, held last summer: “Perhaps one of the more significant challenges for synthetic biology is that living systems actively oppose engineering. They are robust and have evolved to be self-sustaining, responding to perturbations through adaptation, mutation, reproduction and self-repair. This presents a strong challenge to efforts to ‘redesign’ existing life.”

Another step towards (even) cheaper DNA sequencing

Friday, April 17th, 2009

An article in the current Nature Nantechnology – Continuous base identification for single-molecule nanopore DNA sequencing (abstract, subscription required for full article) marks another important step towards the goal of using nanotechnology for fast and cheap DNA sequencing. The work comes from the group of Hagen Bayley, at Oxford University.

The original idea in this approach to sequencing was to pull a single DNA chain through a pore with an electric field, and detect the different bases one by one by changes in the current through the pore. I wrote about this in 2007 – Towards the $1000 human genome – and in 2005 – Directly reading DNA. Difficulties in executing this appealing scheme directly mean that Bayley is now taking a slightly different approach – rather than threading the DNA through the hole directly, he uses an enzyme to chop a single base of the end of the DNA; as each base goes through the pore the characteristic current change is sensitive enough to identify its chemical identity. The main achievement reported in this paper is in engineering the pore – this is based on a natural membrane protein, alpha-haemolysin, but a chemical group is covalently bonded to the inside of the pore to optimise its discrimination and throughput. What still needs to be done is to mount the enzyme next to the nanopore, to make sure bases are chopped off the DNA strand and read in sequence.

Nonetheless, commercialisation of the technology seems to be moving fast, through a spin-out company, Oxford Nanopore Technologies Ltd. Despite the current difficult economic circumstances, this company managed to raise another £14 million in January.

Despite the attractiveness of this technology, commercial success isn’t guaranteed, simply because the competing, more conventional, technologies are developing so fast. These so-called “second generation” sequencing technologies have already brought the price of a complete human genome sequence down well below $100,000 – this itself is an astounding feat, given that the original Human Genome Project probably cost about $3 billion to produce its complete sequence in 2003. There’s a good overview of these technologies in the October 2008 issue of Nature Biotechnology – Next-generation DNA sequencing (abstract, subscription required for full article). It’s these technologies that underlie the commercial instruments, such as those made by Illumina, that have brought large scale DNA sequencing within the means of many laboratories; a newly started company Complete Genomics – plans to introduce a service this year at $5,000 for a complete human genome. As often is the case with a new technology, competition from incremental improvements of the incumbent technology can be fierce. It’s interesting, though, that Illumina regards the nanopore technology to be significant enough for it to take a a substantial equity stake in Oxford Nanopore.

What’s absolutely clear, though, is that the age of large scale, low cost, DNA sequencing is now imminent, and we need to think through the implications of this without delay.

How cells decide

Monday, April 6th, 2009

One of the most important recent conceptual advances in biology, in my opinion, is the realization that much of the business carried out by the nanoscale machinery of the cell is as much about processing information as processing matter. Dennis Bray pointed out, in an important review article (8.4 MB PDF) published in Nature in 1995, that mechanisms such as allostery, by which the catalytic activity of an enzyme can be switched on and off by the binding of another molecule, mean that proteins can form the components of logic gates, which themselves can be linked together to form biochemical circuits. These information processing networks can take information about the environment from sensors at the cell surface, compute an appropriate action, and modify the cell’s behaviour in response. My eye was recently caught by a paper from 2008 which illustrates rather nicely how it is that the information processing capacity of a single cell can be quite significant.

The paper – Emergent decision-making in biological signal transduction networks (abstract, subscription required for full article in PNAS), comes from Tomáš Helikar, John Konvalina, Jack Heidel, and Jim A. Rogers at the University of Nebraska. What these authors have done is construct a large scale, realistic model of a cell signalling network in a generic eukaryotic cell. To do this, they’ve mined the literature for data on 130 different network nodes. Each node represents a protein; in a crucial simplification they reduce the complexities of the biochemistry to simple Boolean logic – the node is either on or off, depending on whether the protein is active or not, and for each node there is a truth table expressing the interactions of that node with other proteins. For some more complicated cases, a single protein may be represented by more than one node, expressing the fact that there may be a number of different modified states.

This model of the cell takes in information from the outside world; sensors at the cell membrane measure the external concentration of growth factors, extracellular matrix proteins, and calcium levels. This is the input to the cell’s information processing system. The outputs of the systems are essentially decisions by the cell about what to do in response to its environment. The key result of the simulations is that the network can take a wide variety of input signals, often including random noise, and for each combination of inputs produce one of a small number of biologically appropriate responses – as the authors write, “this nonfuzzy partitioning of a space of random, noisy, chaotic inputs into a small number of equivalence classes is a hallmark of a pattern recognition machine and is strong evidence that signal transduction networks are decision-making systems that process information obtained at the membrane rather than simply passing unmodified signals downstream.”