 Well, good afternoon. I'm Richard Kidney, I'm from Imperial College in London. I'm the co-director with Paul Fremont, who's sitting here in the front row of our synthetic biology hub, as we call it, which currently has about 130 people in it, so it's quite a big operation nowadays, which Paul and I have built up. The afternoon session is primarily about medical applications, and I've been asked to say a few words by way of introduction, so what I thought I would do is to just slightly broaden out the topic to sort of bracket in a way what people are going to say and talk a little bit about synthetic biology and biotech. And so, I thought I'd start off with a couple of quotations from the afternoon speakers here, where the key thing here is that basically since the 15th century, this is what Martin Fusenegger is saying, is that the basic treatment strategies have remained pretty much unchanged more or less until today when we're now starting to consider the interdependence of these pathophysiologies in terms of major diseases of the 21st century, like diabetes and cardiovascular disease, and I'll come back to cardiovascular disease in a few minutes. Also, a comment from one of our other speakers this afternoon, one of the major aims of synthetic biology is to design micro-organisms with novel capabilities that can be applied for the development of new vaccines, diagnostic sensors, therapeutic interventions for major diseases such as cancer, and again, I'm going to talk a little bit about that, but the other speakers rather will be talking about this in more detail. So, I wanted to start off by way of introduction here by talking about shaping the evolution of healthcare, and this is something that I've worked on for many years actually before we really even got involved in synthetic biology. And there are a number of changes which have occurred in terms of healthcare over the last 20 or 30 years. We've moved from an environment of data port to an environment where we have data rich. We've moved from patient homogeneity where clinicians typically considered patients to be homogenous groups to individual risk assessment, risk assessment and treatment selection. We've moved from intervention against clinically evident disease to disease protection prevention, and finally, in many advanced countries, we've moved effectively for a fragmented delivery system of healthcare to integrated networks and continuity of care. So, these are some of the significant changes which have occurred in healthcare. For me, one of the most important dates in terms, I mean obviously it's a continuum, but one of the most important dates was 2001 with the initial publication of the initial sequencing of the human genome, obviously in nature which everybody in the room will be familiar with this paper. But from a healthcare point of view, many observers see this as being effectively the dawn of molecular-based medicine, and that I think is a, in a way, a key benchmark. We work in synthetic biology, but from a medical point of view you can also think of this date as being potentially the dawn of molecular-based medicine. So, in terms of this new medicine, nowadays many people work in general applications of biology and physiology to healthcare. Now, I'll begin to think of not simply healthcare at individual levels in what I call, as you'll see in the moment, we published this in 2008, the biological continuum, but looking across the different levels of the biological continuum. Incidentally, this is obviously an image of the double helix. So, a couple of, I'll get on to talking about the molecular basis of this in a moment, but I wanted to just talk in a couple of slides just about the other big developments that occurred in medicine, which is visualisation and imaging, and the fact that there are all these different methods of imaging including the example I'm going to show you, which is cryo-em. With cryo-em now, and this is a slide actually from about five or six years ago, you can take conventional, conventionally air micrograph, you can then reconstruct in three dimensions, so this is a surface render volume, and that allows you actually, when you do pseudo-colour, to pull out things like the actin filaments, they're the ones in red. Complexes, they're the ones shown in green here, as it says here, mostly ribosomes and the membranes in blue. So you can get these exquisite images even at this level, but also now at even more refined levels of the biological continuum. But the other key development, which is I think primarily what we're focusing on this afternoon, is essentially omics data. It's the idea that different types of omics data resulted in high throughput science with microwaves, georelectrofluoresis, a lot of data comes into the lab every day, flow cytometry, et cetera. And so when I think about all this data and indeed information coming in, traditionally one thinks about the upper levels of what I call the biological continuum, so systems, viscera and tissues. But in terms of next steps, which is what we're all potentially working on, we have to think about the lower levels of the biological continuum, cell protein and gene level, but also how this, from a healthcare point of view, how this relates to what we called here in this paper in 2008, the care continuum. So not only primary, secondary and tertiary care, but also telecare and home care. And so now, from a healthcare point of view, we're beginning to see, and I for a few years was on the board of one of the main hospitals in London, and I observed the development of healthcare much more into these areas in terms of integration. So if we now think about some synthetic biology, when we think about synthetic biology at Imperial, we think about systematic design. And Paul may have touched on this the other day, but we see this very much as the basis of engineering biology. So from our point of view, synthetic biology is very much about the engineering of biology. And systematic design, you can break down into these major, if you like, principles, components, modularisation, standardisation, and characterisation linked to being able to control the complexity of the biology according to human design. That's the aim of the systematic design, but also a big trend, if you like, within the UK, but also in other parts of the European community is the whole issue about responsible research innovation. Another key area in terms of our strategy is the application of the design cycle, starting with specifications, going through design, modelling, building, testing and validation, and then learning and debugging. So this is a variant on the design-build test paradigm that is widely used within synthetic biology. So, in many ways, the vision is that many drugs which are currently available are based upon known therapeutic properties of various types of plants, and Jim Hasliff, I'll probably be talking about some of this tomorrow, but we believe within synthetic biology that we can use synthetic biology to engineer synthetic versions. And also synthetic biology devices for the detection of various types of infections. So these are two examples of the vision within synthetic biology. Okay, so many drugs are currently available and they're based upon known therapeutic properties, but there is a problem, and that is since 1975 where the average spend of R&D profits within, say, GSK, one of those large drug companies, was about 5%. Today it is at least 22%, so the cost is going up, and today the average time for a drug to get to market is 11 years, and when you think of that in an industrial context where you've only got, well, a maximum of about 20 years, that doesn't, in terms of patents, that doesn't leave a lot of time. So the perceived solution to this is, well, a few years ago, perceived solution was mergers, bigger is better, combinatorial chemistry, computational chemistry, all of these were areas that Big Pharma worked on, but now there is the development of focus, I would say, much more on genomics and also developing focus on synthetic biology techniques. And this is all about the realisation of personalised drugs to develop the therapeutic properties of these drugs so that they have low or no side effects. And the vision here is that synthetic biology will aim to allow the optimisation of existing production processes and the design of new processes, and we are very involved in that through our industrial translation centre. So here are some examples of medical applications. A number of these will be discussed this afternoon. OK, the long-term vision, not the long-term vision but a long-term vision, is, for example, the use of biosensors which permanently reside within the body to detect particular types of abnormality, for example arterial disease and cancer. And for another example is the extension of the concept of highly adaptive vaccines and antibiotics so that, as I say here, the vaccine, for example, can rapidly adapt to a particular type of influenza. And we are actually seeing that coming through now, even industrially. So one example of this is arterial disease. When I talk about this and we got a nice row down the middle here, I usually say about 50% of the room are going to die from this. So it's a pretty important thing. You have to work out which side you're on. But here's the arterial plaque. Typically nowadays what we do is to diagnose the arterial plaque. At least one method of diagnosing the arterial plaque, which is used quite a lot, is to use arterial catheters with ultrasound on them so you can image the plaque. But the developing ideas here are can we use synthetic biology biosensors and the possible in situ, what I've said, manufacture of plaque-busting drugs. But as you'll see in a moment, there could be an alternative strategy here. And the alternative strategy is to use nano cages where we build in a biosensor which has a detector and amplifier. And then feeding through to control, which controls the nano cage. So the nano cage is a hollow cage, which you protein, which you can actually control in terms of opening and closing and putting there, for example, a plaque-busting drug. So that's part of the basic strategy. If you extend that, I've almost finished, if you extend that, this brings in the need for biologic, which I'll explain in a moment, and one of the things that we've done over a number of years is to develop a series of different types of logic gates. So here's one example of one of our AND gates. So I'm sure everybody in the room knows that the basis of any computer are logic gates, and here we have biological logic gates. That's been extended more recently in terms of our work into the developments of what's called a half adder, and we're now moving quite well towards more sophisticated, biologically-based computing based on biological logic gates. And so this will lead to various applications, so things like counters calculated in microprocessors in the longer term, but in the near term, more sophisticated biosensors and possibly for intracellular control and signalling. So here's an example of work that we've been working on which is developing these biosensors to be able to detect cosenoma within the liver and using the biosensor to detect cancerous cells as shown here. It turns out that in order to detect the cells, it's not optimal to simply have one channel in the biosensor, so this is where the logic comes in, the potential, and this is potential, not reality, to be able to have a series of inputs which then go through biological circuitry or biologic circuitry to control the release of, in the case of cancer, a psychotoxic drug. And so I think within the medical context, what we are seeing is a fairly rapid development, I would argue, from conventional medicine through to molecular based medicine. So that's a quick introduction to try and give you at least some of the views that I hold in terms of medical applications. So I think we now need to move on to the first speaker in this afternoon's presentations, which is Nico.