 Thank you very much. Thank you, Victor. Also, thank you organisers for inviting me to give a talk at this really beautiful setting and a really interesting idea of having discussion rather than presentation. Let me carry on anyway. I'm Paul Freymont, under codirect of the Imperial College synthetic biology hub. I have a great interest in how synthetic biology could be translated into useful o'r cyfn dangos, ac felly, o ran ddeunydd y Sfyrrit o'r mewn mynd i synteid ybylaloeth o'r cyfnod y tro a chael gwmpade blaen, ac dywedodd ym mynd i ffrasilau i'r cyfrannu Eoron. Mae labd tyddo'r cyfnod o'r cyfrannu allanol, ti'n bwysig i'r ddateru'r cyfrannu o'r cyfrannu, ac yn gallu eu gwirlo'n ysbryd ar ei ddweud o'r cyfrannu lleol gweld. A os ydydd yn ddweudau maes cyflwyntau ar gyfer o'r cyfrannu tyrddfodol ym Llywodraeth a Victor did a Victor gave a fantastic introduction to some of the nuances, if you like, of synthetic biology and how we might take it forward, harnessing biology explicitly. Okay, so I mean Victor mentioned a little bit about quotes and so I thought I'd just quickly put in a couple of quotes. I found this article in the British Medical Journal in 1910 and it's a sort of quote and I'll just read it a little bit. It says, all natural sciences follow the same process of evolution. They begin by the observation and classification of natural objects and phenomena and that's the descriptive stage and I do feel often that biology is still in the descriptive stage. Then they attempt to resolve these phenomena to determine the cause of their production and thus they become analytical. And I think what this gentleman was saying at the time was that the idea of biological synthesis, the idea of going synthetic is a really interesting one because biology has only reached the first two stages of description if you like and phenomena. And the night he said the idea of biological synthesis is a bold one and yet it is no novelty. It has a crop top in the imaginative literature of all ages but considered as a scientific possibility, its conception is a very recent date. So that was written in 1910. So if you wind forward this is a very large quote but it's again talking about synthetic biology so this is the Polish geneticist who I'm afraid I can't actually pronounce the name so I apologise. This is a famous sort of quote but this is Wachlaw Szewalski I think. That's his there and he's a Polish geneticist who came up with the idea in 1974 truly about synthesis and synthetic biology because he says here we will then devise new control elements, add these new modules to existing genomes and build up whole new genomes. I mean this is essentially what we're doing now and this was in 1974. And then he said this would be a field with the unlimited expansion potential and hardly any limitations to building new better control circuits, synthetic organisms, new better mouse. I'm not concerned that we have run out of exciting novel ideas in the synthetic biology in general. So he was very, very far ahead of his time. This was only a few years after Asilomor and the introduction of genetic engineering and cloning. This was a really interesting insight that he had of where the field would go. But then what is all of us about society and regulators and European unions? And this is some of the other literature which we can't ignore and that is the public literature. This is what people read every day and I grant you that some of this is from the UK press which has got a particularly bad reputation. But things like, scientists are going to play in God after creating artificial life by making designer microbes from scratch. But could they wipe out humanity? Thank goodness they haven't yet. But anyway, reviving extinct species. This is from Friends of the Earth. Extreme genetic engineering in your ice cream. It's a very provocative picture if you like of a pipette and a lovely beautiful ice cream. And then this is probably one that really was not very helpful which is brewing bad, scientists find ways to cook up heroin at home. So this is creating quite a lot of fuss and I think we need to be aware of outside this wonderful place. There are a lot of people there that have got great concerns about synthetibology. However, so what is all of us about? So synthetibology I think there are many definitions and this is one definition which tries to capture what Victor was saying about the idea of building, designing and constructing and redesigning biological systems and there are many reports. This is the most recent European operational definition and there are some people I think in this room were involved in this and I think it really does capture very nicely what synthetibology is. It's the application of science, technology and engineering to facilitate and accelerate design, manufacture and modification of genetic materials and living organisms. So we have definitions so that's kind of where Victor came from. Now just to put it in context this is the number of publications in synthetibology that name synthetibology as part of the field. This is since 2010. You can see it's rapidly rising. There are over 47,000 papers. This is the growth of the student competition called IGEM in synthetibology which I think there are teams from France and all over Europe and all over the world actually showing on this. So these are all young, excited, enthusiastic researchers who are spending their time over the summer designing and building new biological systems. So you can see there's a huge growth there with almost 15,000 young people around the world have been through IGEM. And so, you know, it has this powerful vision if you like for merging engineering design and practice and all of the associated tools involved in that including obviously mathematics, computational modelling and all the other what you call more hard sciences into the construction of biological systems and cells at the genetic and protein level. I think that vision is very persuasive to a lot of people. So if we consider, I mean Victor has already indicated this but if we look at some of the very basics of engineering systems clearly robustness and stability are key for engineered systems and these are often achieved by these sort of four premises where one has systems control, one has redundancy obviously in engineered systems, one also has sort of an idea of modular design and also one has this idea of structural stability within the system you're designing. Now the question is, you know, how do we put that into context of biology? So we think about that. We can think well systems control, we have quite a bit of information and understanding about how biological systems regulate themselves. So we have control circuits, we have feed forward, feedback loops, we have control networks, we have interaction networks. We also have redundancy. We have multiple genes that can carry out similar functions. We also have multiple regulatory pathways. If one pathway doesn't work often another pathway will kick in. We also tend to have modular design, these evolutionally robust modules that get passed from species to species. There's a functionality that's been solved and then it will be evolved or inherited by other species. It does happen in biology. And then we often have good structural stability, homeostasis. I mean cells are incredibly good at regulating their internal processes and life state if you like. So I suppose a hypothesis might be are these features intrinsic to all complex systems, whether they're natural and artificial. And I think one aspect of systematic engineering if you like for biology will clearly test that hypothesis. So I suppose the question is can we learn about biology through design and construction. So biological systems can be considered as modular I think. Functions primarily encoded in DNA, large knowledge of genome databases, large diversity of parts if you like, increase understanding of molecular and cell biology at all different resolution scales. New technologies to synthesize and assemble DNA, chemically synthesize. New computational tools to design and model and obviously systems, biology, modelling and application comes into play here. However I think it's important to realize that, and I think everyone in this biologist in their room should know and hopefully everyone knows that, there's some real challenges for engineering and biology. And one is context dependency. So the idea that genes will function similarly depending on where they are within the genome is not correct. Evolution, adaptation and natural selection, these are very strong processes. This is that will change biological systems depending on their environment. Non-predictive stochastic behavior which is part of the evolutionary process if you like. Self-assembly and emerging properties, non-linear dynamical processes and multi-scale interactions. These are massive, massive challenges and if you really boil it down, I mean living cells are essentially constrained volumes and very high concentrations of biochemical components. I mean that's it. And so therefore, you know, biology is not plug and play. You cannot take one component, put it in the context and assume it will predictably function as you predict this is not true and it really poses problems. And illustrated here is just a sort of network map for a really important eukaryotic mammalian signalling protein called mTOR, which is a piatory kinase, which has functions in many different aspects of a mammalian cell including growth, including all sorts of functions within the cell itself. And I think this is a beautiful paper by the way showing the interconnectivities within a mammalian system that does provide a huge challenge if one wants to start engineering a part of that system or reperturb that system. This is also a major protein involved in cancer. So, as Victor said, one approach may be to overcome this kind of, you know, almost overwhelming sense of complexity and bewilderment might be to try and develop some sort of systematic design process and I think that's what synthetic biology is trying to do. It's trying to build things in a sort of more systematic engineering process. So using things like modularisation, so interchangeable parts, interchangeable modules, using things like standardisation, can we standardise measurements, tools or processes and then using this idea of abstraction which engineers use very successfully to try and sort of deconvolute complexity in some ways, to try and sort of allow people to cope with complexity. And systematic design aims to achieve ultimately robustness and reproducibility, but as I said, these are huge challenges in biological systems. So, this is already shown by Victor and I think just to re-emphasise it's a conceptual framework. It's not a literal framework and it allows one to start thinking about biology at the genetic level as essentially functional genetic elements and therefore by building, you know, repositories and understanding of these parts and putting these parts together in human defined ways, just a simple transcription module, promoter arrives on body sequence, a protein and a terminator, that's a module and one could consider that module to be exemplified and analysed and whatever and characterised and that module could become part of this idea of going from parts to devices and into systems and I think this idea of abstraction hierarchy is actually a very powerful conceptual framework that allows one to start addressing this huge complexity that we're trying to deal with. This then leads on to this very slightly simplistic if you like but very effective design cycle where one can start doing systematic design, building, testing, learning and of course the key aspect here is metrology, modelling and sorry, metrology, data analysis and modelling and obviously learning about that process and these are key elements of this design cycle and the design cycle again is a framework. It's not meant to be a literal thing, it doesn't mean that you can build biological systems without doing this but I think if you want to develop a systematic framework and learn about how you build biological systems this idea of going through the systematic process is extraordinarily powerful and very useful. So I guess the big challenge is and I'm sure this is going to cause a lot of discussion over the next few days is can we build new biological systems with standardised DNA parts and already we are building repositories of parts with nomenclature that people can use and analyse both digitally and functionally. Now what about standards? So Victor led a beautiful project actually called ST Flow a four-year European Union project on standards in synthetic biology which is incredibly useful bringing people together from all over Europe to look at standards and I guess this is just a very simple standard this is the first sort of thread standard by Joseph Whitworth in 1841 how much impact the introduction of a standard and a screw and a nut had on the world at that time it was a hugely important development and there are many other standards now I won't go through all these these are sort of the standards if you like the governments and consumers and businesses look for but I think one key aspect is this idea of interoperability and I think standards are directly linked to measurement I think we need to understand that you know can we standardise the construction of living matter and this is a very big question and I'm sure we can spend the rest of the week talking about that just that one question that is a huge question and that is one of the if you like challenges and approaches that synthetic biology is trying to address now the reason that we think that the systematic approach might be beneficial is because I think people realise that biological research unlike my engineering colleagues research or even chemistry and physics research is often irreproducible and my colleagues in physics and engineering find it extraordinary that biologists actually live with this irreproducibility and can cope with it but we do and this is part of our descriptive storytelling if you like which we do very very successfully not all but certainly we do quite a lot of that and I think it's clear that biological data can suffer from irreproducibility now I think the reason for that is more a lack of technical standards and more a lack of people doing the same thing constantly using the same processes using the same measurement tools using the same strains and learning about the variability within systems so from the standards consortium and our own thinking I think you can think of standards as being possibly physical standards DNA standards possibly functional standards standard measurement conditions standard culture conditions standard strains ie I'm using the same strain as Victor's using in Spain and we use in London and sharing data standard strains and then of course standards within digital information so that we can share all of this information digitally and I think these are really important now I do want to point out that synthetic biologists do think that we are really the new boys on the block and this is a very good cartoon I don't think it will work let's do something different something smarter something cooler and those kind of attributes do fit quite nicely with the synthetic biology so I think we need to think about systems biology and the systems biology community have been going through exactly the same thinking that we are now approaching and I think there is some overlap here that we need to bring into play and try and integrate both systems biology thinking and synthetic biology thinking so what do we measure if you like what would you measure in a biological system there are many different things we can measure and no one really fully understands what we measure everything not quite everything but pretty much everything so in a biological system you could measure RNA transcripts quantitatively proteins quantitatively you can measure metabolites lipids glycans you can get handles on post translational modifications functional states are complicated you know epigenetic states growth state noise within biological systems you can measure noise spatial localization protein protein interaction networks between genetic space protein space and metabolite space these are complicated areas that one can try and develop models and try and develop understanding so we can measure quite a lot using the omics technologies that we have now but no one clearly knows yet I don't think what we need to measure to really improve our sort of design robustness our design cycle so this is where you kind of get this sort of synthetic biology field going there's this idea of a whole bunch of foundational technologies you could reduce synthetic biology down to that if you like a whole bunch of synthetic biology technologies which are things like design tools you know to build new genetic circuits the synthesis and assembly of DNA the parts and device characterization and the standardised measurements and the whole kind of really meticulous measurements of your system and what's going wrong what's working followed with this very persuasive technology crisper cast editing screens again using the optimization of biology as a way to understand the design if you like cycle and then of course working on how do we characterise what is a sort of do we have standardised strains will we ever have standardised strains can we work towards some sort of standardised host strain so these foundational technologies the idea is that they would fill in to different applications and of course the applications that people are very interested in now are shown on here these are not by no means complete but there are a lot of work on foundational tools, therapeutics, novel drug delivery systems, agri-science, fine speciality chemicals biomanufacturing, commodity chemicals and biomaterials to mention but a few now of course this has been the area of industrial biotechnology for many years so synthetic biology is going to try and provide a new tool kit if you like to address some of those issues so what are the current research trends so when I look through all that literature I showed you earlier these are the kind of things that were coming out from the current synthetic biology literature there is quite a lot of people working on refactoring and redesigning genome editing, genome construction automation standards and tools and then some sort of quite a bit of literature also in some of the social science but open source and descaling and there's quite a lot of work on that there is a growing interest and excitement in the idea of creating alternative biological systems using exobiology, X&A artificial cells and cell free systems this idea of building cells from the bottom up and I think this is an area which is actually very very interesting and there is some kind of interesting integration of cell free systems and the kind of alternative biological systems and what I would call more the mainstream synthetic biology so that leads me nicely on to work on cell free systems and I'm going to now just switch gear a little bit to what we've been doing on cell free systems so cell free systems are really interesting because they are essentially the cell extract with the membrane peeled off and all of the ingredients within the cell extracted the genomic DNA removed and essentially it contains ribosomes some membrane vesicles and some cellular proteins so it's a crude sort of lysate if you like from a cell but it has the great ability to be able to translate and transcribe DNA within as a biochemical reaction and assay so it takes out the life of a cell if you like but uses all the ingredients within the cell to carry out reactions so this is a sort of scaled down version if you like so what's interesting about cell free systems is that you can use part of the glycolytic pathway which is the ATP generating pathway that exists in cells and also the TCA cycle is existing within cell free extracts but you can provide new energy sources so one common energy source is three phosphoglycerate but the people are working on cheaper energy sources it's clear within cell free systems you have components of oxidative phosphorylation so you do have the ability to create ATP within the system although you do need to add an ATP regenerating system you also need to add amino acids and other cofactors to allow the system to kick off but the point is that in within that system you can get transcription and translation working quite reproducibly and robustly now there is an alternative system which is the pure system which was a beautiful system essentially first published by some Japanese colleagues that went to the effort of purifying all of the machinery of transcription and translation and then reconsignat in a test tube if you like that's the sort of not only is it pure it's sort of beautiful science if you like of reconstructing the basic components that would allow transcription translation to occur in a test tube the disadvantage of the pure system is it's extremely expensive and awfully difficult to get running, routinely in the lab but it is a beautiful system nonetheless so what are the advantages of cell free systems so you can do transcription translation you can do DNA circuit prototyping you can use them for biosensors environmental testing we've got some projects on that you can actually do enzyme pathways for fine chemical and drug production you can make recombinant proteins and you can do toxic pathways it is scalable, you could scale it up to a thousand litres as shown by sutro biopharmaceuticals but it is probably best if you're thinking about producing products that are high value low volume products now the metabolism is simple and cheap and it's easy to modify so you can do all sorts of interesting things within cell free systems so as a test bed it's a very useful system to operate and in the context of what I described earlier in synthetic biology if you were prototyping parts a few years ago we had this idea well if you've got all these parts you want to measure the functions or the quantitation of a promoters or ribosome binding sequences or whatever to do this using standard molecular biology it's sort of a long process and we wanted to speed that up and see if we could explore whether the information we got from in vitro systems was very similar to the information we got from in vivo systems so we set about doing that as if we could use it as a prototyping and this is the idea of taking parts doing all of the molecular biology the ligations, the transformations the liquid cultures, the growing, the measurement it's a very tedious process so the idea was if we want to do a synthetic biology is going to become this kind of engineering field you want to have thousands of parts characterised to some level of quantitation so that you can inform the various modelling aspects of the field so we decided a few years ago James Chapel PhD student to look at that in detail so we took a bunch of parts, bunch of promoters bunch of ribosome binding sequences and we hooked them all up to a GFP reporter and we did a very very simple experiment we measured the GFP if you like production in a in vivo in a steady state expression system midlog and then using BL21D3 using M9 minimal media 30 degrees we took the same cell free extract from BL21s D3 30 degrees but obviously it's a completely different reaction and we measured the production of GFP from the same parts in the same context in both systems now to our surprise we found on this side that the measurements of GFP the relative measurements the relative production of GFP between in vivo and in vitro were similar we were quite surprised obviously there are some largest error bars but the relative strengths of some of the promoters shown over here you can see that in vivo is in the grey and in vitro is in the white and you're getting kind of nice relatively good correlations between in vivo and vitro and it was the first time which I was unexpected and we also did some reduced promoters and we got similar data and we published this so I won't spend too much time at the same time a whole bunch of other papers came out as well and there was this sort of acceptance if you like or not proposition that said that for simple DNA regulatory parts the ones that have been studied they showed similar kind of functionality similar quantitative behaviours in vitro and in vivo which was quite surprising however as all biology shows you this is not the case so now this is a library screen of promoters we've been doing recently and we found some really quite extraordinarily strong library this is an in vitro this is an in vivo screen sorry in vitro screen and we found some really really strong promoters there's no need to look at the data but it was an extremely strong promoter and it turned out that and here it is here this is the normal Kelly promoter down here and this is the promoter we found it's a really unexpected observation as we were again this descriptive nature of biology as we went through all of these different sequences we found this extraordinarily high promoter and I think what was interesting about it was shown here just shown here when we went to look in vivo we could not replicate a tool that promoter strength it looks like it's the same promoter strength as the Kelly promoter down here in vivo and you know there are all sorts of reasons we're exploring why that is subsequent to that Zach San and others came up and said well actually this does break down so this idea of in vivo in vitro does break down so I guess the way I could pitch that would be well could we use this in vitro in vivo kind of comparison as a way to tell us about context dependency and I think that's something we're going to explore with this very very high producer this promoter which is essentially two base changes which is quite extraordinary and we need to work out why that is so then we're making self-re-extract from different cells we're going to explore self-re-extract as a platform we're going to try and compare them from E. coli different strains of E. coli so this is Mg this is Rosetta BL 21s looking to see if we can learn about any of the sort of phenotypic functionalities of self-re-extracts this is now Bacillus subtolus which we've managed to get optimised and we're going to be looking at Bacillus subtolus as well and then this is, that's the optimisation of Bacillus subtolus and then we're going to be looking at Bacillus megaterium which is a very interesting organism it has been thought of as a very important organism potentially for an industrial production setting and we're doing this in collaboration with colleagues at the Branschweig Technical University and so we've made a self-re-extract from Bacillus megaterium and we're getting extremely good production of proteins within the Bacillus megaterium now in the context of that approach we're also developing some real-time RNA measurements and the idea here is to try and provide quantitative data that would allow you to assess the cell-free system in a more quantitative mathematical way or modelling way and we are getting very nice data showing you get a very nice burst and decays of messenger RNA we're also looking at trying to do very very high throughput analysis so this is on our echo-liquid handler so this is 108 conditions in triplicate three times DNA, six times dosers and we've been developing a model so one of my senior researchers in my lab is a physicist actually originally and he's been developing a mathematical model to try and develop this is the model here it's a Bayesian statistical inference model it's around trying to map out the parameters that we don't know at all within our system what they might be and the modelling parameters that we're interested in is polymerase binding, messenger RNA synthesis messenger RNA degradation and then GFP synthesis, GFP maturation so this is Jane McDonald's work and the idea here is that we can start doing simulations as well as experimental observations and of course here we can start providing the quantitative details that would allow that model to become much more not better but sort of more informed if you like on experimental data and so I think cell-free systems so here's the kind of summary of that if you like so cell-free systems I think are a very useful testbed to explore part of the design cycle of synthetibology but they're also a very good testbed to start and develop slightly simplified models in a non-living system but having all the central sort of broken down parts of life in terms of metabolism so we've been developing a whole extra model here this is James's work doing experiments metabolomics trying to infill this model and also doing proteomics as well to build up a cell-free kind of scenario and then we'll be comparing that with our different cell extracts to see if this breaks down depending on the particular extract but it's a yeah okay so I mean that leads on to the obvious question I think which a lot of people are interested in which is could we build a cell from the bottom up using sort of subsystems if you like maybe using cell-free systems and if you break down a cell I think this is the idea of modularity functional modularity and here you can see if you break down a cell a very simple bacterial cell there are discrete components that you can think about so there are actuation sensing so sensing actuation export communications with the outside regulation computation within the cell signalling and metabolism and I think one of the challenges and one of the exciting challenges I think cell-free and synthetic biology per se can offer is to try and develop these subsystems to build these subsystems and see if they can work now clearly subsystems that involve a membrane compartment will be difficult but we can certainly start looking at regulation and computation or even some metabolic subsystems within the cell-free system and that's one of the goals that we're going to be moving into in collaboration okay so finally I'm not sure how long I've got left if you have 50 minutes okay so finally I just want to go into some work we've been doing on pathway engineering here now I think everyone realizes that cells can be used as metabolic factories and that's been around a long time it's been around so long that we forget that industrial biotinology has been with us for 5000 years maybe or a few thousand years or whatever since we started making wine I guess it's a very very sophisticated industry that has been using cellular systems to produce and manufacture components and some really major pharmaceutical components as well so I guess when you look at industrial biotinology you can see that all of these products that we take kind of for granted there are components in these products that are being or can be manufactured using biological systems so it's very apt that we just had the climate change big convention in Paris just the other day everyone's very excited about moving to this non petroleum based world that we're all going to have to live in and a clearly industrial biotinology has a massively important role and synthetic biology to provide the components and chemical entities that we all need for all of these everyday life systems or we adapt our lives to not live with them which is going to be difficult so of course scale up is a huge problem in industrial biotech and it still continues to be and I think that is one of the big challenges I think that synthetic biology is going to have to try and address because it's okay doing stuff in the lab so the question is can synthetic biology accelerate the construction and prototyping of synthetic pathways for the production of products if you like and these are the kind of areas I think that are important for pathway engineering so we have kinematics clearly, flux modelling combinatorial pathway assembly, metrology in vitrin vivo and chassis host cells and then we need to go through this testing and prototyping so we've been doing some work on a new kind of golden gate based E. coli kit for part assembly which we're just about to publish and the idea here is that we can accelerate the production of different pathways, different combinatorial circuits this is based on golden gate the sort of plasma kit that we can use for many different range of applications and we put in all sorts of variants into the system and the golden gate assembly strategy, a busy slide is extremely powerful technology it's been used around a lot it's a fantastically systematic way of assembling multiple components there are other methodologies for assembly but I think golden gate gives you quite a lot of combinatorial variations so we've made a golden gate kit for E. coli where we can start assembling these modules which can then be assembled into pathways and into greater modules shown here we've also put in some variants in the system to allow you when you're doing combinatorial assembly that you can keep some of the components constant and then just assemble certain parts of the system which I think could be very useful we put in various other things like protein purification tags and all sorts of other things so we're hoping to submit that to our genes so that everyone will be able to access that and find it useful like we found it useful so we've been thinking about products and clearly there are a lot of interesting products that people are manufacturing and making very very complex products but we wanted to develop more of the platform technology to allow us to see how would we make a pathway so we chose something called Raspberry Keytone I really like raspberries and there is this sort of product here that comes out of raspberries that essentially gives you the sort of essence of what raspberries are now obviously there are various economic factors but we just wanted to look at this pathway we're not really interested in that we're more interested in using the pathway it's a simple prototype and testbed so here we do our combinatorial DNA assembly we do the cell-free prototyping we do high throughput LCMS and then we bring in structural biology and other aspects into the process so here's the pathway it's quite a simple 5 enzyme pathway sorry 4 enzyme pathway here and there's another enzyme involved here it starts at tyrosine and it goes to the raspberry keytone through a series of enzymatic conversions you can also come in from a chemical 4-hydroxybenzaldehyde you can do a chemical process to produce this hydroxybenzal acetone component which can then go to form the raspberry keytone so it's a biochemical pathway enzymanically catalyzed produces a natural component so we were just using it as a testbed so the first thing we did is we purified all the enzymes and we did very pure in vitro biochemistry so here are all the enzymes here and all this has worked on publish so these are the 4 enzymes we purified we then increased tyrosine this is the substrate concentration and then we looked to see what we produced over time and in terms of the concentration of the products and you can see here that just from this very simple pure this is the enzymes themselves all together put in substrate, get out product you can do this very simple McKellies Menton modelling all the sort of modelling on this and you find that actually there is some sort of product inhibition in this pathway where you get this 4 cumulative accumulating where the product as a function of increased tyrosine concentration a simple observation but a very important observation if you are trying to build a pathway in vivo we then did some screening with different ribosom binasequences using our golden gate eco flex system and again in vivo and these are just a series of ribosom binasequences and different promoters on different genes we were finding different outputs from the production process so combinatorially we were finding actually the products in red and that is the product we are trying to increase the yield of clearly and we were finding all sorts of interesting correlations in here between ribosome strength and different intermediate products and different products and clearly we are interested in trying to develop a more intrinsic understanding of that in terms of a model but in general terms what we find is that particular combinations of promoters are producing different sort of points within the pathway where we are getting product getting caught up we are getting product inhibition we are getting all sorts of interesting and unexpected outcomes that we didn't know then we decided to try and do some structural biology and we built this model into a crystal structure where we are trying to change the requirement for NADPH to be NADH so we have now got a very nice mutant here which can use NADH instead of NADPH as one of the components in the pathway so I guess from this engineering pathway engineering approach I think what we are beginning to realise is that the landscape of data space that you need to explore in a four enzyme pathway to try and optimise the product production if that's your target function is actually a very very large space indeed and there are many many different nuances and unexpected consequences of changing various parameters which I think Victor alluded to in his very nice introductory slide and clearly you could start thinking about maybe applying weights if you like which is kind of what we are trying to do here and so having a mathematical formulation around where to go next, what to explore, what to change in this kind of design would be extremely useful okay so I think from our initial sort of unpublished data so far refactoring pathways requires I think multiple approaches promoter strengths we find are often inversely correlated to the production so you think if I have high production of different enzymes I am going to produce but that clearly is not the case and there is clearly a lot of unknowns and I think that's been well understood in the literature cell free assays I think have been helping us to make decision points along that reaction pathway and if you want to just to finish up now on the challenges moving forward for the field and this is more of a discussion point I think if we are going to become a sort of more engineering type of field I think we need to develop technical standards we need to have sharing of parts, we need to have parts that are shared between multiple labs, multiple groups ever in the world, openly, easily so that we can learn from all of the information that we need to make this process become much more systematic and predictable we need to share I think detailed data on fails and successes so the great thing about biology and I'm not sure this is true in other sciences but certainly in biology we never share our failures and to be honest most time in biological experiments don't work probably 90% of the time if not more they don't work and we ignore it and so we need to start thinking about failure and sharing that and looking to see what works what doesn't work and we clearly need to integrate systems biology thinking and approaches to try and really harness what's already been done in systems biology into synthetic biology and those are just some of my own thoughts so I've rushed through a lot of stuff there but I just hope to give you a flavour of some of the things we're doing in the lab and how cell free is producing to be a really extremely powerful technology and the work I've described is Richard Kerl with James McDonald, Simon Moore and we're funded and thank you very much for your attention.