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Synthetic Biology Challenges and Progress

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okay thank you very much thank you victor and also thank the organizers for inviting me to give a talk at this really beautiful setting and a really interesting idea of having discussion rather than presentation but let me carry on anyway so i'm poor fremont under co-director of imperial college synthetic biology hub I have a great interest in how synthetic biology could be translated into useful applications but also of course in the spirit of this meeting synthetic biology is an approach to understanding biology and I think that's what I think Francois was mentioning earlier on so my lab works on a number of different areas i'm not going to be presenting data from all of these areas but i'm going to tell you a little bit about our work on cell free platforms and also some work that we've been doing more recently on pathway engineering in the spirit of what Victor dead in 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 quo and I'll just read 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 the biologists still in the descriptor stage then they attempt to resolve these phenomena to determine the cause of their production and as 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 is a really interesting one because biology has only reached the first two stages of of description if you like in phenomena and in light he said the idea but it was it's a bold one it yet it is no novelty it has a cropped up in 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 like and talking about synthetic biology so this is the Provost you notices who I'm afraid I can't actually pronounce the name so I apologize but this is a famous sort of quote but this is Vaclav Wolski I think that's his there and he's a Polish two nurses who came up with the idea of 1974 about 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 limitation to building new better control circuits synthetic organisms knew better mouse I'm not concerned that we have run out of exciting novel 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 asilomar and the introduction of genetic engineering and cloning so this was a really interesting insight that he had where the field would go but then what is all the fuss about society and regulators and European Union's and this is some of the the other literature which we can't ignore and that is the the the public literature this is what people read every day and I grant you that some of this is from from the UK press which has got a particularly bad reputation but things like scientist acute are playing God after creating an artificial life by making designer microbes from scratch but could they wipe out humanity thank goodness they haven't yet but anyway are reviving extinct expends of the earth extreme genetic engineering in your ice cream it's a very evocative 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 a parent at home so this is creating quite a lot of fuss and I think we need to be aware of outside the this wonderful place there are a lot of people there have got great concerns about synthetic biology however so what
is all the fuss about so synthetic biology I think there are many definitions and this is one definition which tries to capture what 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 I think it really does capture very nicely what synthetic biology is it's the application of science technology and engineering for Silla Tatum accelerate design manufacture or 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 synthetic biology that name synthetic biology 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 I German synthetic biology which I think there are teams from France and all over Europe and all over the world actually shown 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 is a huge growth there with almost 15,000 young people around the world have been through I gem
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 modeling and all the other what you call more hard hard sciences into the construction of biological systems and sales of a genetic and protein level I think that vision is very persuasive to a lot of people so if
we consider I mean Victor's already indicated this but if we look at some of the very basics of engineering systems clearly robustness and stability arcade for engineered systems and these are often achieved by the sort of four premises where one has systems control one has redundancy obviously an engineered systems one also has sort of a modern 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 at one pathway doesn't work often another pathway will kick in we also tend to have modular design these evolutionary robust modules that get passed from species to species as a functionality that's been solved and then it will be evolved or inherited by other species that 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 and life state if you like so I suppose then our hypothesis might be are these features intrinsic to all complex systems whether they're natural and artificial and i think one aspect of systemic engineering if you like for biology will will clearly test that hypothesis so i suppose the question is can we learn about biology through design and construction so you know 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 on molecular and cell biology at all different resolution skills new technologies to synthesize and assemble DNA chemically synthesize new computational tools to design and model and obviously systems biology modeling and application comes into play here however I think it's important to realize that and I think everyone in this biologist in the room should know and hopefully everyone knows that you know there's some real challenges for engineering 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 system depending on their own arman non predictive stochastic behavior which is part of the volution process if you like self-assembly and emerging properties nonlinear dynamical processes and multiscale 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 and then 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 signaling protein called mtor which is a pi3 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 you know this is a beautiful of paper by the way showing the interconnectivity 'he's within a mammalian system that that does provide a huge challenge if one wants to start engineering a part of that system or reaper turb that system this is also a major protein involved in in cancer so as 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 that's what synthetic biology is trying to do is trying to build things in a sort of more systematic engineering process so using things like modularization so interchangeable parts interchangeable modules using things like standardization can we standardize measurements tools or processes and then using this idea of abstraction which engineer's you is very successfully to try and sort of D convolute complexity in some ways to try and sort of allow people to cope with complexity unsystematic design aims to achieve ultimately robustus and reproducibility but as I said these are huge images and biological systems so so with this is already shown by vector and I think just re-emphasize it's a conceptual framework is not a literal framework and it allows ones 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 put in these parts together in human defined ways just a simple transcription module promote arrive somebody sequence a protein and a terminator that's a module than one could consider that module to be exemplified and analyzed and whatever and characterized that 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 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 modeling and sorry metrology data analysis and modeling and obviously learning about that process and these are key elements of this design cycle and the design cycle again it's a framework it's not meant to be a literal thing it doesn't mean if you can build by those consistence 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 challenges and I'm sure this is going to cause a lot of discussion over the next few days is you know can we build new bar legal systems with standardized DNA parts and already we are building registries and repositories of parts with nomenclature that people can use and analyze both digitally and 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 incredibly useful bringing people together from all over Europe to look at standards and I guess this is just a very simple standard the the first sort of thread standard by Joseph with word 1841 you could imagine how much impact the introduction of a standard and the screwin and not 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 sort of standards if you like the government's 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 standardize 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 and that is one of the feel 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 realize that biological research unlike my engineering colleagues research or even chemistry and physics research is often irreproducible am I like my colleagues and physics and engineering find it extraordinary that biologists actually live with this irreducibility and can cope with it but we do and this is part of our you know 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 dated can suffer for me reproducibility now I think the reason for that there's more a lack of technical standards are more lack of sort of people doing the same thing constantly using the same process as 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 has been possibly physical standards DNA standards possibly functional standards you know standard measurement conditions standard culture conditions standard strains ie i'm using the same strain as victors 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 I think these are really important now i do want to point
out that synthetic biologists do think that you know we all think we're really new boys on the block and you know let's this is a very good cartoon I don't think it'll work let's do something different Sony's smarter something cooler and those kind of attributes do fit quite nicely with the synthetic ecology 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 could measure everything not quite everything but pretty much everything so in a biological system you could measure you know 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 of in biological systems you can measure noise spatial localization protein-protein interaction networks relative networks trying to bridge 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 to really improve our sort of design robustness or 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 and that evolved you 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 standardized measurements and the whole kind of really meticulous measurement of your system and what's going wrong was working followed with this a very persuasive technology Chris forecast genome editing screens again using the optimization of biology as a way to to understand design if you like cycle and then of course working on how do we characterize what what is a sort of do we have standardized rains will we ever have sound that I strains can we work towards some sort of standardized hosts strange so these foundational technologies the idea is that they would fill in two 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 were confidential tools therapeutics novel drug delivery systems agriscience find speciality chemicals biomanufacturing commodity chemicals and biomaterials are to mention but if you now 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 ology 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 sciences but open source and descaling it was quite a lot of work on that there is a growing interest and excitement in the idea of creating art alternative biological systems using exobiology XNA artificial cells and cell-free systems this idea of building cells from the bottom up and I think this is area which is actually very very interesting and there is some kind of interesting integration of cell-free systems and the kind of alternative biosystems and what i would call more than mainstream synthetic biology so that leads me nicely on to work that we've been doing on cell free systems and i'm going to now just switch gear a little bit toe up 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 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 transfer 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 current to 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 HP generating pathway that exists in cells and also the TCA cycle is existing within sultry x rays but you can provide new energy sources so one common energy sources 3-phosphoglycerate by the people are working on cheaper energy sources it's clear within Selfridge 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 ATP regenerating system you also need to add amino acids and other essential 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 rican singer in a test tube if you like that's the sort of you know not only is it pure it's sort of your 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 retuning allowed but it is a beautiful system nonetheless so what are
the advantages of free system so you can do a transcription translation you can do DNA circuit prototyping you can use them for biosensors environmental tests 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 liters as shown by Sutra biopharmaceuticals but it is probably best if you're thinking about producing products 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 with insole free system so as a 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 the quantitation of a promoters or rather somebunny sequences or whatever you know to do this using standard molecular biology 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 said 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 act to you know the legations or transformations and liquid cultures the growing the measurement it's a very tedious process so the idea was if we want to do it synthetic policy is going to become this kind of engineering field you want to have thousands of parts characterized to some level of quantitation so that you can inform the various modeling 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 promoters bunch of ribosome binding sequences we hooked them all up to a gfp reporter and we did a very very simple experiment we measured the gfp quantity if you like a production in a in vivo in a steady state expression system mid-log and then using bl21 de3 using m9 mineral media 30 degrees we took the same self free extract from bl21 d 3 30 degrees but obviously it's completely different reaction and we measured the production of GOP 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 of relative production of grp between in vivo and in vitro was similar we were quite surprised / significant error there are some sort of largest Arab eyes but the relative strengths of some of the promoters shown over here you can see that you know in vivo is in the gray and in vitro is in the white and you're getting kind of sort of nice relatively good correlations between in vivo in vitro and it was the first time which we I was unexpected and we also did something useful from rotors and we got similar data and we published it 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 so if you like or not proposition sorry proposition that said that for simple DNA regulatory parts the ones that have been studied they showed similar kind of functionality similar quantitative behaviors in vitro and in vivo which is 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've found some really quite extraordinarily strong library this an in vitro this is an in vivo screen and we found a saurian in vitro screen and we found some really really strong promoters and then 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 promoted down here and this is the promoter we found it's a really unexpected observation as we were again this descriptive Metro biology as we went through all of these different sequences we found this extraordinarily high promoter and I think what was interesting about it as shown here just shown here when we went to look in vivo
we could not replicate at all that promoter strength it looks like it's the same promoter strength as the Kelly promoter down here in vivo and you know they're all so surreal we're exploring why that is subsequent to that Zach son and others came out and said well actually there 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 you know 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 this very very high producer this promoter which is essentially two based changes which is quite extraordinary and we need to work out why that is so then we're
making cell-free extracts from different cells we're going to explore cell-free extracts as a platform we're going to try and compare them from ecoli
different strains of E coli so this is mg this is rosette bl21 s looking to see if we can learn about any of the sort of phenotypic functionalities of cell-free extracts this is now bacillus subtilis
which we've managed to get optimized and we're going to be looking at the syllabus as well and then this is that's
the optimization bacillus up close and
then we're going to be looking at bacillus megaterium which is a very interesting organism has been thought of as a very important organism potentially and for an industrial production setting and we're doing it's in collaboration with colleagues at the branch vague Technical University and so we've made a
cell-free extract from bacillus megaterium and we get an extremely good production or proteins within the bacillus megaterium now in the context
of that approach we're also developing some real time machine 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 of modeling way and we are we are getting very nice data shown you get very nice bus and decays of messenger RNA we're
also looking at trying to do very very high throughput analysis so this is on our eco liquid handler so this is 108 conditions in triplicate three times DNA six times repressing of juicers 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 basin statistical inference model it's around trying to do trying to map out the parameters that we don't know at all within our system what they might be and the modern parameters are interested in is polymerase binding master RNA synthesis machine only degradation and then GOP senators GOP mature age this is Jerry McDonald 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
are 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 synthetic biology but they're also very good testbed to start and develop slightly simplified models in a non-living system but having all the central sort of sort of broken down parts of life if like in terms of metabolism so we've been developing a whole extract model here this is James's work doing experiments metabolome is 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 selects rights to see if this breaks down depending on the particular extract but it's so 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 actual sin sin sin sin actuation export communications with the outside regulation computation within the cell signaling and metabolism and I think I think one of the challenges and one of the exciting challenges I think cell free and synthetic biology per se can can can offer is to try and develop sort of these subsystems so you know sort of to build these subsystems and see if they can work and activity 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 the goals that we're going to be moving into and collaboration okay so finally I'm not sure how long I've got left 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 could be used as metabolic factories and that's been around a long time it's been around so long that we forget that industrial biotechnology has been with us for you know 5,000 years maybe or a few thousand years or whatever since we started making wine I guess but anyway it's 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 biotechnology you can see that all of these products that we take kind of for granted there are components within 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 going to all going to have to live in and a clearly industrial biotechnology has an immersive lee important role on synthetic biology to provide the components and chemical entities that we all need for all of these everyday life systems or we or we 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 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 cuz it's okay doing stuff in a lab so the question is can say they will accelerate the construction and prototyping or 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 bio mattox clearly flux modeling combinatorial pathway assembly metrology in vitro in 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 commentary also because this is based on Golden Gate and it's a 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 busy slide is extremely powerful technology it's been used around a lot it's a fantastically systematic way of assembly multiple 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 variance in the systems to allow you when you're doing comment or 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 gene so hopefully 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 of 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 us
something called raspberry ketone I really like raspberries as and there is this sort of product here that comes out of raspberries which is essentially gives you the 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 as a simple prototyping test bed so here we
do our comet or 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 for enzyme pathway here and it's a other enzyme involved here it's it starts at tyrosine and it goes to the raspberry ketone through a series of enzymatic conversions you can also come in from a chemical 4-hydroxyphenyl dahod you need a chemical process to produce this hydroxy bones are assets own comp component which can then go to form the raspberry ketone so it's a it's a sort of it's a biochemical pathway enzymatically catalyze produces a natural component so we were just using it as a test bed so
the first thing we do 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 published so these are the four enzymes we purified we then increase tyrosine product this is the substrate concentration and then we look to see what we produce over time and in terms of the concentration of the products and you can see here that just from this very simple pure cissa tea this is the enzymes themselves all together put a substrate get our product you can do is very simple mykelti's mint and modeling all sort of modeling on this and you find that actually there is some sort of product inhibition in this pathway where you get is for q8 accumulating where the product as a function of increased tyrosine concentration so very simple observation but a very important observation if you're trying to build a pathway in vivo we then did some
screening with different rubs and binary sequences using our Golden Gate eco flex system and again in vivo and these are just a series of ropes and bonnie sines and different promoters on different genes we were finding different outputs from the production process so so combinatorially we were finding actually the product is in red and that's the product we're 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're interested in trying to develop a more entry or 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're getting product getting caught up we're getting product bishan we're getting all sorts of interesting and unexpected outcomes that we didn't know then we decided to try
and do some structure biology and we built this model into a crystal structure where we're trying to change the requirement for NADPH to be NADH so we've now got a very nice mutant here which it can use nada sh instead of nadph is one of the components in the pathway so I guess from this engineering
kind of like pathway engineering approach i think what what what I what we beginning to realize is that the the landscape of data space that you need to explore in a for enzyme pathway to try and optimize 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 thought thinking about maybe applying weights if you like which is kind of what we're 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 ok 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 different enzymes are going to purdue but that clearly it's not a case and cut and they're all 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 ton of sort of more engineering type of field i think we need to develop technical standards we need to have shearing apart 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 ninety percent of the time if not more they don't work and we don't under it and so we need to start thinking about failure and sharing that I'm 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 rushed through a lot of stuff there but I just hope to give you a flavor of some of the things we're doing in the lab and and how cell-free is producing it being a really extremely powerful technology and the work I described as Richard curl with Jerry McDonald Simon more and we're funded and thank you very much for your attention