The UK’s Science Minister, David Willetts, gave a speech last week on “Our High Tech Future”. The headlines about it were dominated by one somewhat odd policy announcement, which I’ll come to later, but what’s more interesting is the fact that he chose (apparently at quite short notice) to give the speech at all, only weeks after the publication of a strategy for “Innovation and Research for Growth”, that was widely regarded as, at best, a retrospective attempt to give coherence to a series of rather random acts of policy. I’m tempted to interpret the speech as a signal that a not completely formed government policy is still evolving in some quite interesting directions. In short, after 32 years, the Conservatives are rediscovering the need for industrial policy.
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Slouching towards an industrial policy
January 12th, 2012A little history of bionanotechnology and nanomedicine
December 19th, 2011I 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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Science in hard times
December 12th, 2011How should the hard economic times we’re going through affect the amount of money governments spend on scientific and technological research? The answer depends on your starting point – if you think that science is an optional extra that we do if we’re prosperous, then decreasing prosperity must inevitably mean we can afford to do less science. But if you think that our prosperity depends on the science we do, then if growth is starting to stall, that’s a signal telling you to devote more resources to research. This is a huge oversimplification, of course; the link between science and prosperity can never be automatic. How effective that link will be will depend on the type of science and technology you support, and on the nature of the wider economic system that translates innovations into economic growth. It’s worth taking a look at recent economic history to see some of the issues at play.
UK Government spending on research and development compared to the real growth in per capita GDP.
R&D data (red) from the Royal Society Report The Scientific Century adjusted to constant 2005 £s. GDP per person data (blue) from Measuring Worth. Dotted blue line – current projections from the November 2011 forecast of the UK Office of Budgetary Responsibility (uncorrected for population changes).
The graph shows both the real GDP per person in the UK from 1946 up to the present, together with the amount of money, again in real terms, spent by the government on research and development. The GDP graph tells an interesting story in itself, making very clear the discontinuity in economic policy that happened in 1979. In this year Margaret Thatcher’s new Conservative government overthrew a thirty year broad consensus, shared by both parties, on how the economy should be managed. Before 1979, we had a mixed economy, with substantial industrial sectors under state control, highly regulated financial markets, including controls on the flow of capital in and out of the country, and the macro-economy governed by the principles of Keynesian demand management. After 1979, it was not Keynes, but Hayek, who supplied the intellectual underpinning, and we saw progressive privatisation of those parts of the economy under state control, the abolition of controls on capital movements and deregulation of financial markets. In terms of economic growth, measured in real GDP per person, the period between 1946 and 1979 was remarkable, with a steady increase of 2.26% per year – this is, I think, the longest sustained period of high growth in the modern era. Since 1979, we’ve seen a succession of deep recessions, followed by periods of rapid, and evidently unsustainable growth, sustained by asset price bubbles. The peaks of these periods of growth have barely attained the pre-1979 trend line, while in our current economic travails we find ourselves about 9% below trend. Not only does there seem no imminent prospect of the rapid growth we’d need to return to that trend line, but there now seems to be a likelihood of another recession.
The plot for public R&D spending tells its own story, which also shows a turning point with the Thatcher government. From 1980 until 1998, we see a substantial long-term decline in research spending, not just as a fraction of GDP, but in absolute terms; since 1998 research spending has increased again in real terms, though not substantially faster than the rise in GDP over the same period. Underlying the decline were a number of factors. There was a real squeeze on spending in research in Universities, well remembered by those who were working in them at the time. Meanwhile the research spending in those industries that were being privatised – such as telecommunications and energy – was removed from the government spending figures. And the activities of government research laboratories – particularly those associated with defense and the nuclear industry – were significantly wound down. Underlying this winding down of research was both a political motive and an ideological one. Big government spending on high technology was associated with the corporate politics of the 1960′s, subscribed to by both parties but particularly associated with Labour, and the memorable slogan “The White Heat of Technology”. To its detractors this summoned up associations with projects like the supersonic passenger aircraft Concord, a technological triumph but a commercial disaster. To the adherents of the Hayekian free market ideology that underpinned the Thatcher government, the state had no business doing any research but the most basic and far from market. In fact, state-supported research was likely to be not only less efficient and less effectively directed than research in the private sector, but by “squeezing out” such private sector research it would actually make the economy less efficient.
The idea that state support of research reduces support of research by the private sector by “squeezing out” remains attractive to free market ideologues, but the empirical evidence points to the opposite conclusion – state spending and private sector spending on research support each other, with increases in state R&D spending leading to increases in R&D by business (see for example Falk M (2006). What drives business research and development intensity across OECD countries? (PDF), Applied Economics 38 p 533). Certainly, in the UK, the near-halving of government R&D spend between 1980 and 1999 did not lead to an increase in R&D by business; instead, this also fell from about 1.4% of GDP to 1.2%. Not only did those companies that had been privatised substantially reduce their R&D spending, but other major players in industrial R&D – such as the chemical company ICI and the electronics company GEC – substantially cut back their activities. At the time many rationalised this as the inevitable result of the UK economy changing its mix of sectors, away from manufacturing towards service sectors such as the financial service industry.
None of this answers the questions: how much should one spend on R&D, and what difference do changes in R&D spend make to economic performance? It is certainly clear that the decline in R&D spending in the UK isn’t correlated with any improvement in its economic performance. International comparisons show that the proportion of GDP spent on R&D in the UK is significantly lower than most of its major competitors, and within this the proportion of R&D supported by business is itself unusually low . On the other hand, the performance of the UK science base, as measured by academic measures rather than economic ones, is strikingly good. Updating a much-quoted formula, the UK accounts for 3% of the total world R&D spend, it has 4.3% of the world’s researchers, who produce 6.4% of the world’s scientific articles, which attract 10.9% of the world’s citations and produce 13.8% of the world’s top 1% of highly cited papers (these figures come from the analysis in the recent report The International Comparative Performance of the UK Research Base).
This formula is usually quoted to argue for the productivity and effectiveness of the UK research base, and it clearly tells a powerful story about its strength as measured in purely academic terms. But does this mean we get the best out of our research in economic terms? The partial recovery in government R&D spending that we saw from 1998 until last year brought real terms increases in science budgets (though without significantly increasing the fraction of GDP spent on science). These increases were focused on basic research, whose importance as a proportion of total government science spending doubled between 1986 and 2005. This has allowed us to preserve the strength of our academic research base, but the decline in more applied R&D in both government and industrial laboratories has weakened our capacity to convert this strength into economic growth.
Our national economic experiment in deregulated capitalism ended in failure, as the 2008 banking collapse and subsequent economic slump has made clear. I don’t know how much the systematic running down of our national research and development capability in the 1980′s and 1990′s contributed to this failure, but I suspect that it’s a significant part of the bigger picture of misallocation of resources associated with the booms and the busts, and the associated disappointingly slow growth in economic productivity.
What should we do now? Everyone talks about the need to “rebalance the economy”, and the government has just released an “Innovation and Research Strategy for Growth”, which claims that “The Government is putting innovation and research at the heart of its growth agenda”. The contents of this strategy – in truth largely a compendium of small-scale interventions that have already been announced, which together still don’t fully reverse last year’s cuts in research capital spending – are of a scale that doesn’t begin to meet this challenge. What we should have seen is, not just a commitment to maintain the strength of the fundamental science base, important though that is, but a real will to reverse the national decline in applied research.
Some questions for British research policy
July 22nd, 2011This piece is based on a summing-up I did at a meeting in London this March: A New Mandate? Research Policy in the 21st Century.
There seem to be two lurking worries that concern people in science policy in the UK at the moment. The first is the worry that, having built a case for state support of science on the basis that this will lead to innovation and economic growth, that innovation and economic growth may not be delivered. The second is that the scientific enterprise doesn’t have a sufficiently broad base of popular support. In short, are we suffering from an innovation deficit, and does our research effort have a democratic deficit?
An innovation deficit
The letter with the funding settlement from BIS to the Research Councils called for “even more impact” – the impact agenda in research councils and funding agencies really is accompanied by a sense of increased urgency of an argument that is by no means settled.
To many scientists the economic case for supporting science may seem self-evident, but the solid evidence in support of this is surprisingly slippery. There is certainly the feeling in some quarters – and not just the Guardian’s Simon Jenkins – that the economic impact of science has been oversold. The Royal Society’s “The Scientific Century” document was a serious attempt to assemble the evidence. What strikes me, though, is that it doesn’t make a great deal of sense to try and give an answer to the primary question – to what extent should the state support science – without considering the much broader question of how our political and economic system is set up to support innovation.
And it is in relation to innovation that there are some more general worries, both at a global level and in our own national circumstances:
A democratic deficit
The idea that we’re in the midst of a popular crisis in trust in science is deeply embedded. I’m not convinced that the crisis in trust is with science itself, rather than the use of science in politics and commerce, which is something slightly different, but nonetheless this idea has been a driving force for much of the new enthusiasm for public engagement and dialogue, and for taking this public engagement upstream. While some people (including me) would want to set this move as part of a broader move to steer technology to meet widely shared societal goals, there is still a sense that for many, this is still seen as being about gaining acceptance for new technologies.
On the face of it, these two worries – of an innovation deficit and of a democratic deficit – look to be in opposition. The idea of an innovation deficit suggests that our problem is that technology isn’t moving fast enough, and we have to work to remove obstacles in the way of innovation, while the negative perception of public engagement holds that its job is to put those obstacles back in the way. In fact, in times like now this perception is a real danger.
But actually they’re quite closely connected. Underneath these dilemmas are two worries – a loss of confidence in the self-organising capability of the scientific enterprise, and a sense that something’s missing in our innovation system.
Research councils – “from funder to sponsor”
It’s these worries that underly current moves in the UK research councils, perhaps most explicitly defined by EPSRC, in their aim of “moving from funder to a sponsor” – i.e. moving from the position of responding to the agenda of the scientific community, towards commissioning research in support of national needs.
The issues then are, how is national need defined, and how is the process of defining that national need given legitimacy?
This is a big problem in our current system, where our political fashion is explicitly not to define such a need in anything other than rather general and vacuous terms (like saying we need to have a “knowledge economy”). To pose the question in its most pointed form, does it make sense to have a science policy if you don’t have an industrial policy?
This situation puts research councils in a very difficult position. If governments are not prepared to develop such an industrial policy, how can the research councils do this – how can they do it practically, and how can their decisions acquire legitimacy?
These legitimacy problems come in three directions:
1. with the scientific community
2. with the government
3. with the population at large.
The scientific community will see a potential clash with the Haldane principle (invented tradition though David Edgerton says this is), which could be interpreted as saying that the scientific community is the primary source, as an embodiment of the principle of autonomy of the scientific enterprise.
With the government, a research council like EPSRC is in a very difficult position. They have to deliver the science in support of a national policy which does not, in fact, exist, but they will be judged by very instrumental measures of wealth creation.
Can “challenge-led” research help?
“Societal challenges” offer a new synthesis that can be considered a response to this. I find this attractive as a way of getting beyond a sterile dichotomy between applied and basic research, but the definitions of what might be meant by a societal challenge are contested, value-laden and full of interpretive flexibility.
Societal challenges do have an advantage, in having a certain security in the face of political uncertainty and lack of direction, and a certain independence from political whims. Who can really disagree with the idea that sustainable energy will be a big deal on rather long timescales, for example?
But there are problems – can governments genuinely take a long enough view? How can we avoid fads and the herd mentality? How can we be prepared for the inevitable unanticipated changes in direction in world events? how can we move from generalities to the particularities of real technologies?
What is the place of public engagement? On the one hand, what better way of getting a direct view about what national need should be than consulting the public directly? Public engagement then presents itself as a partial solution to the problem of legitimacy, but one that isn’t necessarily going to make their relationship with government any easier.
There is one other set of institutions that, strangely, don’t get mentioned very often. Those are the Universities. What’s their role? Can they be more than just a loose coalition of individual researchers responding to the incentives and demands of the research councils and other funders? Universities have their own considerable intellectual resources across the disciplines, and they have their own long history and independence, so one might hope that Universities themselves could be another focus for reasserting the public value of research. For a civic university like my own, Sheffield, surely the University should as a focus for the aspirations of the community it serves.
Science and politics
There is another driving force for public engagement; the sense that representative government is failing to provide a space for discussing big issues about our future choices and how people want to live their lives. Science and technology have to be a part of this discussion, and this is why discussions about science and technology must have a political dimension. There are those who assert the opposite – that science doesn’t have or shouldn’t have a political dimension, and that technology is autonomous, out of control, and can’t be directed. But these assertions are themselves profoundly political statements.
On Impact
June 13th, 2011This somewhat policy-heavy piece is an updated version of a talk I gave at a higher education policy conference last September – my apologies for blog readers not directly concerned with science and University funding in the UK, who may find it less enthralling.
What is this thing called “impact”, which has such a grip on Universities and funding agencies in the UK at the moment? Of course, it isn’t a thing at all; it’s a word that’s been adopted to stand for a number of overlapping, but still distinct, imperatives that are being felt by different public agencies concerned with different aspects of funding research in higher education in the UK, and which, in turn, different constituencies within UK higher education are attempting to steer.
The most immediate sources of talk about “impact” are the Higher Education Funding Council of England (HEFCE) and the different research councils, who operate jointly in this area under the umbrella of Research Councils UK (RCUK). These two manifestations of this impact agenda are, in fact, two rather different and separate issues. HEFCE wish to measure the impact of past research, as part of their overall program to assess the past research performance of Universities – the Research Excellence Framework – which subsequently will inform future allocations of funding to the Universities. RCUK, on the other hand, wishes to ensure that the research it funds is carried out in a way that maximises the chance that it does have impact. Both HEFCE and RCUK want the idea of impact to have a greater influence on funding decisions. But HEFCE’s version of impact is backward looking and concerned with measurement, RCUK’s interest is forward looking and concerned with changing behaviours.
It is important to understand the wider context which has driven this concern with impact. The immediate pressure has come from the funding council’s perception of a growing need to convince the Treasury that public spending on research brings a proportionate return to the UK as a whole. During the process of settling the science budget last autumn, in a very tight public spending round, this argument within government, has been dominant. And, to the extent that the budget settlement was not as bad as many had feared, perhaps this idea of impact did gain some traction. Certainly, last December’s letter (PDF here) announcing the science settlement called for “even more impact” – saying “Research Councils and Funding Councils will be able to focus their contribution on promoting impact through excellent research, supporting the growth agenda. They will provide strong incentives and rewards for universities to improve further their relationships with business and deliver even more impact in relation to the economy and society.”
But this focus on impact is only one manifestation of a much wider discussion about the value of research to society at large and how the values that underly publicly funded research should be aligned with widely shared societal values. The broader question is how we organise publicly funded research to realise its public value. For leaders and managers of HE institutions engaged in publicly funded research, this leads to fundamental questions about the missions and visions of their institutions and how this is communicated to their members.
What do we actually mean by “impact”? This, of course, is a highly contested question – there is a growing perception that the degree to which a particular discipline has a greater or lesser degree of impact on the wider world is directly connected to its value in the eyes of funding agencies, and so it’s not surprising that disciplines will wish to influence the definition of impact to maximise their contributions. Clearly science, engineering, medicine, social sciences, arts and the humanities will come at the problem with different emphases. The funding agencies will reflect a compromise position back to the academic communities they serve, while tailoring the message a different way in their interactions with their political masters.
HEFCE must, necessarily, take a broad view of impacts, as they serve the whole academic community. Engineers may emphasise the direct economic benefits that come from their research, social scientists information to underpin good public policy, while the humanities possibly more intangible cultural benefits. The task that HEFCE has set itself is devising a framework to measure and compare these incommensurable qualities. The methodology is starting to become clear. A pilot exercise tested a trial methodology in a number of different Universities in a handful of rather different subjects. The methodology combines the use of quantitative indicators, where appropriate, and narrative case studies, in which the external impact of research carried out by groups of researchers over some past period is described. The results of the pilot highlighted some predictable difficulties, and suggested some mitigating strategies. The timescales on which impact appears vary greatly from subject to subject, and even within subjects. For much research, impacts are captured outside higher education, whether that’s as a result of transfer of people from HE into industry or public service, or by the picking up of research ideas that are effectively in the public domain. As a result, the originators of research may well not be in a position to know about the impacts of their research.
The research councils have the apparent advantage that they can tailor the idea of impact more closely to their own constituencies. For the Medical Research Council (MRC), for example, it’s clear that improved health and well-being will be the primary category of their impact (though even here there may be many different routes to achieving those broad goals). The Engineering and Physical Sciences Research Council (EPSRC) will tend to emphasise economic impacts through spin-outs and partnerships with existing industry. Many researchers will be concerned that the growing emphasis on impact will lead inexorably towards a move from pure, curiosity-driven research to more applied research. The counter-argument from the research councils will be to emphasise that this is not what they want; instead they seek a more conscious consideration of why the impact of the research they sponsor matters. This emphasises the forward-looking nature of the impact agenda as understood by RCUK – the sections in research council grant applications about “pathways to impact” don’t seek to ask researchers to predict the future, instead they seek to change the behavior of researchers.
It’s clear that defining and assessing impact isn’t easy; the Science Minister, David Willetts, had earlier made his reservations about this clear. In a speech in July last year he announced a delay in the Research Excellence Framework, saying “The surprising paths which serendipity takes us down is a major reason why we need to think harder about impact. There is no perfect way to assess impact, even looking backwards at what has happened. I appreciate why scientists are wary, which is why I’m announcing today a one-year delay to the implementation of the Research Excellence Framework, to figure out whether there is a method of assessing impact which is sound and which is acceptable to the academic community. This longer timescale will enable HEFCE, its devolved counterparts, and ministers to make full use of the pilot impact assessment exercise which concludes in the Autumn, and then to consider whether it can be refined. “
At the moment, though, the views of the Treasury are as important as the views of the Minister. It’s difficult to avoid the suspicion that, for all the subtlety with which RCUK and HEFCE have defined the many dimensions of impact, the Treasury is interested in only one type of impact – money. This sounds more straightforward, but it’s still not easy – we need for a robust evidence base for the assertion that spending on research yields tangible commensurate economic returns.
It isn’t just in the UK that these arguments are being carried on. In the USA, for example, the large injection of funding into science as part of the economic stimulus package have prompted the “Star Metrics” programme. In the UK, the Royal Society released in March last year an extensive study – “The Scientific Century” – which marshalled the evidence for the returns on investment in publicly funded R&D (concentrating on science, medicine and engineering).
Even in this restricted domain, the complications of the routes by which public investment in research produce returns become apparent. There was, for many years, a clear consensus in western countries about the way in which the value of publicly funded science emerges. This consensus originates in an enormously influential document written by the US science administrator, Vannevar Bush, in 1945 – “Science: the Endless Frontier”. This is the document that led to the foundation of the USA’s National Science Foundation. It encapsulated what, to many people, has become known as the “linear model of innovation” – the idea that pure science, curiosity driven and carried out without any consideration of its end-uses, would be converted into national prosperity through a linear process of applied science and technological development. Of course, the impact agenda, as conceived by the research councils, is in direct contradiction of this world-view – and since this view is deeply ingrained in many parts of the scientific community, this accounts for the deep-seated unease in those quarters that the RCUK view of impact gives rise to. And, if it were that simple, surely the measurement of past impacts would be straightforward?
However, the linear model is now very much out of fashion – it is considered by many to be neither an accurate picture of how research has worked in the past, nor a desirable prescription for how research ought to work in the future. To return to our current Science Minister, it is clear that he doesn’t believe it at all. In his July speech, he said: “The previous government appeared to think of innovation as if it were a sausage machine. You’re supposed to put money into university-based scientific research, which leads to patents and then spinout companies that secure venture capital backing….The world does not work like this as often as you might think…. There are many other ways of harvesting benefits from research. But the benefits are real”.
One of the most influential critiques of the linear model came in a came in a book by Donald Stokes called Pasteur’s quadrant. This argued that the separation of basic research from considerations of potential applications which is made explicit in Bush’s picture didn’t always correspond to the reality of how research has been done. There have certainly been scientists who have carried out fundamental investigations without any thought of potential use – Niels Bohr is the example Stokes used. And, as Bush argued, sometimes very practical applications do in fact emerge from such work. There have been technologists who have focused solely on the need to get their inventions to work and to market, without a great deal of curiosity about the fundamental underpinnings of those technologies – Thomas Edison being a classical example. But a scientist like Louis Pasteur carried out fundamental research – in his case, laying many of the foundations of modern microbiology, while at the same time being motivated by the very practical considerations of how wine ferments and milk sours.
On Stokes’s diagram, which has two axes defined by the degree to which considerations of use and fundamental interest motivate research, we have three quadrants typified by the approach of Bohr, Edison and Pasteur. What occupies the fourth quadrant, where the work is characterised by being neither fundamentally interesting nor practically useful? In the past this undesirable quadrant hasn’t had a name, but I propose to call it “Cable’s quadrant”, after the UKs secretary of state for Business, Innovation and Skills, who said in a speech on 8 September last year “there is no justification for taxpayers money being used to support research which is neither commercially useful nor theoretically outstanding.” Of course, no-one sets out to carry out research of this kind; the question is how to minimise the chance of research turning out this way without the risk of discouraging high-risk research that, if it did succeed, would be truly transformative.
There remains an unanswered question in Stokes’s formulation – who decides what is practically useful? Is this simply a matter of what has commercial applications? In the context of UK publicly funded research, this must be related to the broader question of who we, in Universities, work for. Universities are independent and autonomous institutions, so while they must respond to the immediate demands of their funders, they must always be mindful of their enduring sense of mission. How can we resolve this tension? One idea that might be helpful is the notion of “public value”, as applied to science policy in a pamphlet from Demos – The public value of science”. But it should be clear that the drive for research councils, in particular, to move beyond criteria for “good science” that are entirely defined by scientists, on the basis of their own disciplinary norms, to judging science on the basis of what are perceived as the needs of the nation, will present some severe problems of its own, which I will perhaps discuss in a later post.
What would a truly synthetic biology look like?
May 4th, 2011This is the pre-edited version of an article first published in Physics World in July 2010. The published version can be found here (subscription required). Some of the ideas here were developed in a little more technical detail in an article published in the journal Faraday Discussions, Challenges in Soft Nanotechnology (subscription required). This can be found in a preprint version here. See also my earlier piece Will nanotechnology lead to a truly synthetic biology?.
On the corner of Richard Feynman’s blackboard, at his death, was the sentence “What I cannot create, I do not understand”. This slogan has been taken as the inspiration for the emerging field of synthetic biology. Biologists are now unravelling the intricate and complex mechanisms that underlie life, even in its simplest forms. But, can we be said truly to understand biology, until it proves possible to create a synthetic life-form?
Craig Venter’s well-publicised program to replace the DNA in a simple microorganism with a new, synthetic genome has been widely reported as the moment when humans have created a new, synthetic living organism. This achievement was certainly a technical tour-de-force, but many would argue that just replacing the genome of an existing organism isn’t the same as creating a complete organism from the bottom up. Making a truly synthetic biology, in which all the components and mechanisms are designed and made without the use of existing biological materials or parts, is a much more distant and challenging prospect. But it is this, hugely more ambitious, act of creation that would fulfil Feynman’s criterion for truly understanding even the simplest forms of life.
What we have learnt from biology is how similar all life is – when we study biology, we are studying the many diverse branches from a single trunk, huge and baroque variety on one hand, but all variants on a single basic theme based on DNA, RNA and proteins. We’d like to find some general rules, not just about the one particular biology we know about, but about all possible biologies. It is this more general understanding that will help us in one of science’s deepest questions – was the origin of life on earth a random and improbably event, or should we expect to find life all over the universe, perhaps on many of the the exo-planets we’re now discovering? Exo-biology has a practical difficulty, though – even if we can detect the signatures of alien life-forms, distance will make it difficult to study them in detail. So what better way of understanding alien life than trying to build it ourselves?
But we can’t start building life without having an understanding of what life is. The history of attempts to provide a succinct, water-tight definition of life is very long and rather inconclusive. There are some recurring themes, though. Many definitions focus on life’s ability to self-replicate and evolve and the ability of living organisms to maintain themselves by transforming external matter and free energy into their own components. The principle of living things as being autonomous agents – able to sense their environment and choose between actions on the basis of this information – is appealing. But while people may agree on the ingredients of a definition, putting these together to make one which is neither too exclusive nor too inclusive is difficult. (I very much like the discussion of this issue in Pier Luigi Luisi’s excellent book The emergence of life).
An experimental approach to the problem might change the question – instead of asking “what life is” we could ask “what life does”. Rather than asking for a waterproof definition of life itself, we can make progress by asking what sort of things living things do, and then consider how we might execute these functions experimentally. Here we’re thinking explicitly of biology as a series of engineering problems. Given the scale of the basic unit of biology – the cell – what we’re considering is essentially a form of nanotechnology.
But not all nanotechnologies are the same; we’re asking how to make functional machines and devices in an environment dominated by the presence of water, the effects of Brownian motion, and some subtle but important interactions between surfaces. This nanoscale physics – very different to the rules that govern macroscopic engineering – gives rise to some new design principles, much exploited in biological systems. These principles include the idea of self-assembly – molecules that put themselves together under the influence of Brownian motion and surface forces, constructing complex structures whose design is entirely encoded within the molecules themselves. This is one example of the mutability that is so characteristic of soft and biological matter – a shifting balance between weak interactions in the face of subtle changes in external conditions causes changes in the organisation and shape of molecules and assemblies of molecules in response to changes in the environment.
It’s quite difficult to imagine a living organism that doesn’t have some kind of closed compartment to separate the organism from its environment. Cells have membranes and walls of greater or lesser complexity, but at their simplest these are bags made from a double layer of phospholipid molecules, arranged so their hydrophobic tails are sandwiched between two layers of hydrophilic head groups. The synthetic analogue of these membranes are called liposomes; they are easily made and commonly used in cosmetics and drug delivery systems. Polymer chemists make analogues of phospholipids – amphiphilic block copolymers – which make bags called polymersomes which, in some respects, offer much more flexibility of design, often being more robust and allowing precise control of wall thickness. From such synthetic artificial bags, it is a short step to encapsulating systems of chemicals and biochemicals to mimic some kind of metabolism, and in some cases even some level of self-replication. What is more difficult is to be able to control the traffic in and out of the compartment; this ideally would require pores which only allowed certain types of molecules in and out, or that could be opened and closed on certain triggers.
It is this sensitivity to the environment that proves more complex to mimic synthetically. It’s still not generally appreciated how much information processing power is possessed even by the most apparently simple single celled organisms. This is because biological computing is carried out, not by electrons within transistors, but by molecules acting on other molecules. (Dennis Bray’s book Wetware is well worth reading on this subject). The key elements of this chemical logic are enzymes that perform logical operations, reacting the presence or absence of input molecules by synthesising, or not synthesising, output molecules.
Efforts to make synthetic analogues of this molecular logic are only at the earliest stages. What is needed is a molecule that changes shape in the presence of an input molecule, and for this shape change to turn on or off some catalytic activity. In biology, it is proteins that carry out this function; the only synthetic analogues made so far are built from DNA (see my earlier essay Molecular Computing for more details and references).
Given molecular logic elements whose outputs are other molecules, one can start to build networks linking many logic gates. In biology these networks integrate information about the cell’s environment and make decisions about different courses of action the cell can take – to swim towards food, or away from danger, for example.
In order for a bacteria sized object to be able to move – to swim through a fluid or crawl along a surface – it needs to solve some very interesting physics problems. For such a small object, it’s the viscosity of the fluid that dominates resistance to motion, in contrast to the situation at human scales, where it’s the inertia of the fluid that needs to be overcome. In these situations of very low Reynolds number new swimming strategies need to be found. Bacteria often use the beating motion of tiny threads – flagellae or ciliae – to push themselves forward. At Sheffield we’ve been exploring another way of making microscopic swimmers – catalysing a chemical reaction on one half of the particle, producing an asymmetric cloud of reaction products that pushes the particle forward by osmotic pressure (more details here. But even though we can make artificial swimmers, we still don’t know how to control and steer them.
By now it should be obvious that the task of creating a truly synthetic biology remains a very distant goal. The more that biologists discover –particularly now they can use the tools of single molecule biophysics to unravel the mechanisms of the sophisticated molecular machines within even the simplest types of organism – the cruder our efforts to mimic some of the features of cell biology seem. We do have a reasonable understanding of some important principles of nano-scale design – how to design macromolecules to make to self-assembled structures resembling cell membranes, for example. But other areas are still wide open, from the fundamental theoretical issues around how to understand small systems driven far from equilibrium, through the intricacies of mechanisms to achieve accurate self-replication, to the challenge of designing chemical computers. On a practical level, to cope with this level of complexity we’re probably going to have to do what Nature does, and use evolutionary design methods. But if the goal is distant, we’ll learn a great deal from trying. Even to speculate about what a truly synthetic life-form might look like is itself helpful in sharpening our notions of what we might consider to be alive. It is this kind of experimental approach that will help us to find out the physical principles that underlie biology – not just the biology we know about, but all possible biologies.
