My name is Kristala Prather and I'm an associate professor of chemical engineering at MIT. In the first part of my presentation, I gave an overview of metabolic engineering and synthetic biology and now I'd like to talk more specifically about work being done in my lab towards expanding the capacity of biology for chemistry, or as I've titled it here, teaching an old bacterium new tricks. So, in my introduction, I gave this maze as an example of how we think about metabolic engineering, where you have the example here of a mouse Wemberly that's lost its pet rabbit Petal and there's a maze of possibilities of how the mouse might get to the rabbit it's looking for. And our goal is to be able to block off, or to obstruct, those pathways which are not going to be productive, or to stimulate, in this case, the mouse to run faster, or in biological terms, to increase the rate at which material will flow through our maze so that we get to the product that we're interested in more quickly. Now, there's another way that we actually think about engineering pathways that also looks at this maze analogy. In this case, our goal is completely different. Now, we actually want to blow the maze up so that rather than forcing the mouse to run from one to the other through all these obstacles, we have a more direct way to get from point A to point B. And that's actually the focus of much of the work that goes on in my lab. When we started thinking about this problem, that is how do we actually get biology to do more chemistry, the question was, well, what kind of targets could we look at? What are good molecules to look at that might be produced by biology? And in 2004, the US Department of Energy put together a report called "Top Value Added Chemicals from Biomass" where they actually sought to answer that very question. That is to say, if you're using biology, or biomass, as the input for chemicals, what are the right molecules that you'd want to produce. And they came up with a list which is actually called the "top 10" list in the literature of building block molecules. Now, I always find this interesting because it turns out the top ten list actually has 12 lines and a few of these lines have more than one molecule, but nevertheless, it's called the top ten list because that sounds a lot better than the top 14 or 15 list. If we look at this list, we see things for example like glutamic acid, and that's an amino acid. We see aspartic acid, which is also an amino acid, and those were compounds that we weren't really interested in working on because our challenge was to find a pathway that either didn't exist or one that was really, really complicated that we could, again, blow up our maze, if we use that analogy, in order to get to the compound that we're interested in. So, when we looked at this list, we eliminated compounds like that. We also eliminated compounds like glycerol, which it turns out is actually relatively cheap today, but wasn't when this report was first produced. So, once we went through this process, of saying, well here are things that we're not intellectually interested in, and here are compounds that we don't think really give us the value that we want, we began to focus on a couple of different compounds, one of which is glucaric acid, that's shown here, and I'd like to talk to you today about our work that we've done to be able to produce this compound in a microbe, namely, E. coli. Glucaric acid, as it's shown again here, is a structure that has 6 carbons, so it's actually pretty similar to glucose in how it's arranged, and it is actually a natural product and I mentioned natural products in the first half of my talk as being compounds that are naturally produced by nature. It turns out this is a compound that's produced in fruits and vegetables and also in mammals, but there's no known microbial pathway for it, meaning that if we look at the simplest organisms, the ones that are easiest to think about putting into a factory, there are no microbes like that where we know that glucaric acid is produced. This compound has been studied for therapeutic purposes either as an agent to reduce cholesterol, or even possibly to fight cancer, but we've actually been more interested in its properties as a monomer for different kinds of materials, or as detergents. And the final bullet point on this slide just emphasizes the fact that we actually know how to make this compound chemically, from glucose, but it turns out that that process, the way it exists now, is pretty messy, it requires a lot of harsh materials, and so it's both not economical and not environmentally friendly. So, we set out to come up with a way to make glucaric acid using biology. This is actually what the natural pathway looks like and hopefully you can see a lot of arrows here that might make you a little bit squeamish if you were going to graduate school and your advisor said, you've got to get this whole thing to work in E. coli. Just to give you a quick overview of this pathway, the compound we're interested in, glucaric acid, is in this box at the top. I mentioned that this is something that could come from glucose and we actually will use glucose as our starting compound as well, and glucose is on this figure. I'll give you a second to look and see if you can find it, because it turns out there are quite a bit of arrows here, but if you look very closely along the left-hand side, then you can see glucose right here. You can also see that all these arrows are going back and forth, you have this interaction with the pentose phosphate pathway, you have another sugar, galactose, which is one input, and you actually have an additional output, which is ascorbic acid. This is a mess, and this is not something that we would really want to think about putting into E. coli. So, our challenge was to figure out, is there a different way for us to get from glucose to the molecule that we're interested in, that would be much simpler, that would have much, much less of this maze-like effect. One of the nice things about having a molecule, however, that is a natural product, is that we could go to the databases and say, is glucaric acid there? That is, in known metabolism, is there an example where glucaric acid has been found to be associated with biology. And in fact, what we found is that glucaric acid could be produced from a compound called glucaronic acid and it can produced using an enzyme called uronate dehydrogenase that's actually found in a bacterium called Pseudomonas syringae. But that was sort of the end of the story as far as Pseudomonas was concerned. With our glucaronic acid, now, we could go back to the databases again and ask the same question, that is, do we see glucaronic acid being produced by nature, and in fact what we could find is that glucaronic acid could be produced from a compound called myo-inositol with an enzyme called myo-inositol oxygenase. And that enzyme is found in a number of sources, a number of mammalian sources, and fungal sources, and we actually chose the variant from mouse because it was one that had been shown to work well when it was expressed in E. coli. But that was really the end of the story as far as mammalian biology was concerned, but if we said where else does myo-inositol show up in metabolism, we could actually find a linkage directly from myo-inositol, or to myo-inositol, from glucose, and that was work done by John Frost's lab at Michigan State, where he showed that you could use glucose as the input, you would go through glucose-6-phosphate, and then you would have just a single recombinant enzyme, that is, a yeast myo-inositol-1-phosphate synthase that would produce myo-inositol-1-phosphate and that, in E. coli, was naturally dephosphorylated in order to give the myo-inositol compound that we're interested in. So, now, rather than having this very complex network of 11 or 12 steps, we really only need 3 different enzymes to be expressed in E. coli, although from three very different sources, in order to get the compound that we're interested in. And so we could take advantage of that to actually have the first gene directly PCR-amplified because we knew that would work in E. coli from John Frost's work. The second gene we could take advantage of this DNA synthesis that I talked about in the first part to be able to have this version of the gene synthesized but synthesized in a way that E. coli would be able to produce it more easily than the natural sequence of DNA that would come from mouse, and then we actually had to do a little bit of work to figure out what was the sequence of DNA, or the gene, encoding from the uronate dehydrogenase in bacteria. But once we were able to do that, we now had all three of the genes that we needed to put into E. coli to see whether or not it could make glucaric acid. So, when we co-expressed all three of these genes, what we found was exactly what we hoped to find. And that is that we got glucaric acid being produced. And the figure that's shown here shows the titer, or the concentration, in grams per liter, of glucaric acid that we can measure in the culture medium. So, this is actually spit out by the cell into the surrounding medium. And I have two different bars that are shown here, one that has 0.1 millimolar IPTG and one that has 0.05 millimolar IPTG. I want to take just a second and explain what that really means. IPTG in this case is what we'd call an inducer; that means that it's something that we add to the culture that tells the cells you should start making the proteins, or the enzymes, that we're interested in. And what's shown now is the result on this slide, as something that we see a lot of times, which is that if we have a somewhat higher concentration of our inducer, where we're making more protein, you see that we actually have less of the product than if we have a lower concentration of our inducer. And that's really a core principle of metabolic engineering, which is that changes that we make to the cell have these very broad systems-wide effects that we don't always understand. And so every time we seek to engineer an organism to make a compound we're interested in, we have to go through this trial and error process of trying to identify what really are the best conditions to make the compound that we're interested in. The second thing that I want to point out is that we see, besides glucaric acid being produced, we also find that we have myo-inositol, which is accumulating, meaning we can measure that in the culture medium. And the fact that that myo-inositol is there, it lets us know that the enzyme which is converting myo-inositol to glucaronic acid is a limitation in the system. That is, it's not working the way its supposed to work, such that all the myo-inositol that's produced is converted to glucaronic acid, and then onto to glucaric acid. I always think at this point, there must be a joke in here somewhere. We have a yeast, a mouse and a bacterium and they all go into a bar and I'm not really sure what the end result is here, but we know that glucaric acid comes out somewhere. Unfortunately, it's not quite that easy and we have a lot of challenges that we have to try to address in trying to actually get the cells to make a lot more of this product that we're interested in. The first of those challenges actually comes into place when we actually look at the fact that we have this myo-inositol accumulating, as I pointed out in the first graph, that showed glucaric acid being produced. And in this case now, if we take a closer look at this enzyme, all we're focused on is this one reaction. We can see that this MIOX gene, the myo-inositol oxygenase, takes myo-inositol as its input. It also uses molecular oxygen and the product that's produced now is glucaronic acid. And so we know that the cells are not actually doing this reaction, that is, converted myo-inositol to glucaronic acid, at a fast enough rate to consume of all it. So, if we study that enzyme by itself, the experiment we did in this case was to look at cells producing just this enzyme, so it doesn't have the first enzyme, which gives us myo-inositol, it doesn't have the third enzyme, which actually takes that glucaronic acid and converts it to glucaric acid. Instead, we're looking at this in isolation, and we looked at two different conditions: one where we actually have myo-inositol present in the culture medium as we're growing up the cells and making the protein, and one where it's missing. And the only difference now is that at a point where we measure the activity of the cells, we actually have some cells that saw substrate, that is the myo-inositol, and some that didn't, but at the same time, when we would go to analyze them, we take the cells away, so now there's no myo-inositol, we break open the cells and release the protein and we expose those cells to the same concentration of the substrate. And in doing that and measuring the activity, what we find is that for the cells that were able to previously see the substrate, the activity of that protein is about an order of magnitude higher than the cells that only saw substrate for the first time after the protein had actually been produced. Well, so this actually raised an interesting question for us. And we thought about we actually solve this problem, and I can tell you the answer is not toss in a lot of myo-inositol, because that's actually cheating. What we want to do is start from glucose, which is going to be a more cheaply available substrate, and make the product that we're interested in. But now we can think about this as engineers and say, well, what information do we have that actually gives us some guidance on how we might actually be able to sole this problem, even if we don't exactly understand the underlying reasons for the phenomenon that we see. And so the first thing that we thought is, ok, what we want then is for that first enzyme, the INO1, to make a lot of the myo-inositol, and then that would be really good because that's what we need for the second enzyme to be effective. The only problem with that is that it sounds really good to say that, but as we've worked on that, that turned out to be a lot easier said than done. At the same time as we were looking at this, we actually came up with another idea. In this case, the idea came from a collaborator, John Dueber, in SynBERC, which is the Synthetic Biology Engineering Research Center, and John's work was looking at something called enzyme colocalization, where the goal here was to be able to take enzymes that normally might be freely disbursed throughout the cell, with no reason for them to be together, and to cause a way for those enzymes to be physically located next to each other. In fact, what happens in this case is that the enzymes, shown here now as MIOX and INO1, are actually exposed, or they have covalently attached to them these tags, and those tags fold into a certain 3-dimensional conformation that can then be recognized by a different piece of a protein. That piece of a protein can then be put into something that we call a scaffold, and if you now have the scaffold in the cell, and you have these enzymes that are tagged with pieces that will recognize that scaffold, that actually causes two enzymes to become located close to each other within the cell. So, our idea here was very simple, that if we couldn't actually change the activity of the enzyme and the way that we could get the upstream enzyme to make much more product, if we actually reduced the distance between the two enzymes, that would give us a higher local concentration of myo-inositol, and maybe if that local concentration was higher, that would give us the higher activity that we had seen before, and that would actually give us higher yields and productivities. And the first way that we tested this was exactly as its diagrammed on this slide, where we actually had just these two enzymes being recruited to the scaffold, in a one to one ratio, and in doing that, we actually got an increase of about a factor of 3 in the amount of glucaric acid that we were producing. Now, as all good scientists, we have to ask ourselves, is this working the way that we want it work? And I'll remind you that our theory here was that what we would get was not just more glucaric acid, but that that would happen because we would have a higher activity of MIOX, that is, we would have better activation, and that would result in this faster conversion that would give us more of the product that we're interested in. So, we actually needed to test that theory, that is, to measure the activity of this MIOX protein and find out whether or not it actually had higher activity, as we supposed that it might. What's shown now in the upper left-hand corner is the data for the product, or the glucaric acid titer, where the lighter bars here are, well, on the left hand side, I should say, without scaffold, and then on the right hand side, with scaffold. And you can see again, these are two different conditions in terms of how much of this IPTG we use to induce the expression of the proteins. And in the first case now, of these lighter bars, there's no real difference between not having scaffold and having scaffold, on the amount of product that's being produced, and if we actually look at the activity of the protein, there's also no significant difference between the protein activity here and the protein activity in this case as well. However, in our best case, where we actually had an increase of 3-fold in the amount of glucaric acid being produced, that's the darker bar in this case, we can look at the specific activity of the protein and we see about a 30% improvement in the activity of this protein relative to when the scaffolds aren't present. And the p-value is here just to show you that that difference is actually significant. So, now we've actually verified that we have not just higher production of the product that we're interested in, but we're getting that higher production by the mechanism that we had supposed would actually happen. Now, one of the nice things about these scaffolds is that what it allows you to do is to explore different stoichiometries. What I mean by that is you don't just have to have one of one protein and one of a second protein coming together, but you can actually, in that scaffold, dial in the stoichiometry by specifying the number of binding domains that you have for each particular protein. So, this is an example of a different scaffold, where you can see two binding domains for one of the proteins, four binding domains for another protein and a single binding domain for the last protein. And if we put that together, what it actually means is that we have, in this case, four copies of the first gene, the INO1 enzyme, that is, two copies of the second enzyme, and only one copy of that third enzyme. This actually allows us to look at a wide variety of different configurations as well as look at varying the amount of the scaffold that we have and the amount of the enzyme that we have, to look at the effect of that on the productivity. And the result of that exercise is shown here, where each of those dots is the average of a triplicate experiment where we have the same amount of enzyme being produced in all cases, but we're looking at a wide variety of scaffold induction levels and also looking at a very wide configuration of different scaffolds themselves, meaning different numbers of binding domains for these enzymes that we're interested in. What we see if that we actually are able to change the activity of this enzyme over a factor of about 7-fold and that actually results in a change in the amount of glucaric acid that we have in a factor of about 5-fold. So, we really have shown that we can use, in this case what's called a synthetic biology device, that is, these protein-protein co-localization mechanisms, to be able to solve a problem with an engineering approach, even if we still don't understand exactly what is it that leads to these differences that we see in the activity of the protein. Now, I want to remind you again of this maze analogy that we had before of a protein, or rather a compound, coming into a maze and having a number of different places that it could go. And I showed a very simple diagram before of the maze having four different entry points. Well, the reality is that this is really what the maze looks like inside the cell, where each of the individual dots in this figure represents a particular chemical, and each of the lines between those dots represents an enzyme that can convert that chemical into something else. So, that means that the networks that we're really talking about are very, very large mazes, not these very simplified ones that I showed you. And if our goal is to have glucose, for example, as a starting molecule, work its way through this maze, and end up with a final compound that we're interested in, we can often have by-products that are being produced. And ideally what we'd like is to, again, knock-out those unproductive routes, which are going to lead to byproduct formation, but the question becomes, what if your byproduct is actually growth? And growth in this case also means the ability to make the enzymes that you need in order to catalyze all these chemical reactions that are going to give you conversion of your starting substrate, glucose, down to your final product, glucaric acid. In this case now, we don't have the option of simply knocking out or deleting growth, because now we're not actually going to make the enzymes that we need and this means that we have to have a different way of solving this problem, or a different approach to dealing with the byproduct that we have. So, what we can do in this case is again, take advantage of these principles of synthetic biology, which are based on design, to think about a control system. In particular what we want is dynamic control of these activities. We like to have our initial condition be fast growth, or growth being favored, such that we actually make not just the cells, but again the proteins that we need, that are going to give us the enzymes that give us the chemical reactions that we need to make the product that we're interested in. And then we want to trigger a switch to a production phase where we say, stop growing now, and instead of growing, use all of that glucose to make the molecule that we want you to make. I can represent that diagrammatically like this, where if we have our competing activity, initially, when the input is low, that activity will be high, and at some point, I'm going to now add an input that causes the competing activity to be low. You can see that now, specifically, in what we're interested in, which is growth versus production, which is that we want growth to actually start high, and then after awhile, we want growth to go down, and instead we want the production here to actually start to go up. This is actually something called a genetic inverter. It's an inverter because when the input is low, the output is high. When the input is high, the output is low. And there is actually a precedent for this in nature, namely in secondary metabolite production. Now, for secondary metabolites, these are natural products where growth first is favored, and then the cell will naturally make this switch such that you then will have the metabolites being produced later. So, how do we actually make this process happen when we're talking about having a switch for growth where ideally what we're doing is having the cells use glucose for growth initially, and then change that in order to use glucose for product formation at some point after which we apply our trigger. If we look at how glucose is normally used in our cells, it comes in in what's called the PTS system, and that PTS system brings in glucose as glucose-6-phosphate. And it has two different routes that it can go into; glycolysis or the pentose-phosphate pathway and that's actually how that glucose is used by the cells for growth. That's how the glucose is eaten, if we want to think about it that way. And that's the process that we want to compete against. Well, glucose-6-phosphate is the original substrate of our glucaric acid pathway, but we didn't really want to deal with quite this complexity to start with, so we decided to start on a simpler scale and see if we could just address the glucose utilization issue and then what we're doing now is to try to work up to the increasing complexity that's required to deal with glucose-6-phosphate specifically. That can be addressed by the fact that there is actually another way that glucose can come into the cell. It can come in through what's called the galP, or galactose permease, and in this case, it comes in as free glucose. That glucose now has to be converted to glucose-6-phosphate with an enzyme called glucokinase that uses ATP. And because now the glucose has to go through that route, it gives us just a single control point for being able to regulate, that is control, how much of the glucose goes into our endogenous metabolism, or growth, versus what goes into the product that we're interested in. So, we can actually have this system now where we knock-out the PTS system, we apply what we describe as a valve to regulate Glk activity, and in doing that, we're able to modulate how much of the glucose is available for endogenous metabolism, that is for growth, versus how much is available for productivity. And I just want to remind you that when we're talking about modulating the protein, that is, how much of the glucokinase that's available, what we're really talking about is controlling how much of the DNA, or how that DNA is being expressed. So, we're actually doing all of our manipulations at the level of DNA synthesis, which comes back to how we think about synthetic biology. So, one way that we can actually test this, rather than immediately going to a process where we have to worry about dynamic control, is to look at what we would call static control of the system. And that is that we can replace the natural glucokinase operon, or production system, which naturally consists of two different promoters that are negatively regulated by this protein called FruR, we can get rid of all of that regulation, that is we can replace that DNA, and instead have a library of different promoters where the binding site for FruR is gone, so the only thing that's regulating how much of this protein is produced is the kind of promoter that we use. And by varying the strength of these promoters, by using different variations here, then we can end up with a library of different expression states and ask the question, does that actually affect how much of a heterologous product we could actually produce. Here's now a little bit of characterization of this library. The first thing that we're looking at in this slide is whether or not we actually do have increases in the mRNA, that is, whether or not changing the promoter strength changed the transcription, and then if that corresponded to increases in the protein being produced. And what's shown in this case now, along the x-axis, is the relative promoter strength, from very low strength, or weak promoters, up to very high strength promoters, and then what's shown on the y-axis, on the left-hand side, is the activity of the protein that we're interested in, glucokinase, and what's shown on the right hand side is the mRNA levels. And you can see now, that activity, which is in the solid circles, does actually go up as we go from low promoter strengths up to high promoter strengths, but it only goes up to a certain point, after which we see it start to decline. The same thing is true for the mRNA, that it actually will go up as we go along this axis here, and it only will go up to a certain point and then it starts to decline as well. These measurements were all done where we use glycerol as a carbon source instead of glucose and that's actually to allow us to decouple growth from measuring the properties of this enzyme just to see if the library is working. And what we actually found when we went to glucose is that when the expression levels were too high here, then these cells no longer grew. So, this cell has high mRNA, but you can see the protein levels are pretty low. And these cells would not grow on glucose. The ones where the protein levels were still pretty high would grow on glucose, except that we did have this gray region here, this stipple region, where we saw the cells could grow, but only very, very poorly. We could then take the cells that we knew were growing well, in this region here, and then ask, can we actually now, in glucose, relate the growth rate to the activity of this protein, which tells us whether or not it really can control how much of the substrate is available for endogenous growth. The result of that experiment is shown on this slide, where again what we have now is expressed in terms of Glk activity where it goes from a very low activity up to our higher activity and then what's shown on the x-axis is the growth rate of the cells. The native promoter is shown right here in this open triangle and the filled squares will tell you that we're able to actually increase the growth rate of the this cell. We can also decrease the growth rate of the cell by changing the glucokinase activity. So, that confirms for us that we actually do have a control point or a specific protein where if we vary the activity of that protein, that actually will tell us, or allow us to control, rather, how the cells are growing. The next question then is, if you can control the growth of the cells, does that actually result in more product being produced. So, in this case we have an example molecule, or a test molecule, gluconate, this can be produced in one single enzymatic step from glucose, and again, the competing reaction here is glucose-6-phosphate, which is actually going to be produced from glucokinase. What's shown now here is 5-KG, this is 5-ketogluconate, which is just a spontaneous product that we actually get in very, very small amounts, but we want to account for that by making sure that we look at the sum of both of these products, to give us a sense of how much of the flux is coming through this side versus this side of our pathway. And now what's shown in this slide is actually the result of that experiment, where what's shown now is the Glk activity, that is from, lower to higher amounts of that protein, which is controlling how much glucose goes into endogenous metabolism and what's shown on the y-axis is the molar yield, and this is really how much of the glucose that we start with goes into the compound that we're interested in, versus goes into other byproducts, or into cellular growth. And we see this very nice relationship where, when the activity is very low, then we can see that we have a moderate amount of the yield, in this case, that is the product that we're interested in. As we increase the activity, then we get a slight bump, but as the activity goes higher and higher, what we actually find is that we are decreasing the yield, which basically tells us that as we get, now, to the point where we're making more and more of this glucokinase, we have more of the glucose going into growth and less of it going into the product that we're interested in. So, that actually gave us the validation that we needed that the system design that we had envisioned, one in which we could control the activity of this enzyme, was going to be useful. What I haven't told you so far is that these cells here, although they had the highest yield, did not have the highest concentration. The concentration wasn't very different from the cultures that surrounded it as far as yield was concerned, and they also didn't grow very well. So, that just meant that the cells overall were not happy, and that our original design of having them have a state where they grow very well first, would probably work better in terms of giving us the maximum yield possible. So, the system that we wanted to design here, again, is an inverter, and the way that this will work is that we have this protein, now as an example, GFP, which is being produced by a promoter which is regulated by the lacI protein, or the lacI operator. When lacI is not present, GFP is turned on. We then have lacI, however, under the control of something called the tet promoter, and the tet promoter is responsive to a small molecule such that when you add aTc, this small molecule, it would turn on the expression of lacI and that would turn off our GFP. Now, that you can see by looking at the graph; the first point that we actually have is the fact that in the absence of any aTc, then we have a very high fluorescence, which means that the whole system is on. If we then move to a point where we add aTc, what you'll find in that case is that you can see the GFP levels start to go down as a function of how much aTc we add, and at the point where we've added 100 ng/ml of aTc, we have very little GPF being produced. We can show that the mechanism of this is working the way we intend it to work by adding an additional protein called IPTG, and what IPTG actually does is to interfere with this lacI binding such that you can recover some of the GPF expression and that's actually shown in the last two points of this graph here, that show that GFP can go back up. So, now we know that our system, our basic inverter is working, and what we have to do in this case now is to integrate that into our cell, that is to change now Glk activity so that it responds in this same way. And what's shown now in this slide is the result of having done exactly that. So, here's now the construct of our inverter, where again, this is really just how the DNA is being constructed, and we're using that to control how Glk is being produced and we can look at the same two properties that we looked at before, which is, is the mRNA changing, that is, is the DNA to mRNA, that transcription process, is that being regulated the way we want it to, and does that correspondingly result in differences in the Glk activity? And the mRNA levels are actually shown at the bottom, where you can see that as we increase the amount of aTc, we actually do see that we start initially with high levels of mRNA, and then those levels of mRNA eventually come down. The top graph here actually shows the response of Glk, where it also starts very high, and then it also will come down to a very, very low level. This is again a characterization in glycerol, where we don't have glucose present, so we're only able to see the response of the cells to Glk when it doesn't really need Glk and that actually tells us, is the system really working. Now, we also want to know that it's actually dynamic. So, the way we tested our static system before was just to change the promoters that were encoding for Glk and then to ask, does that actually give us differences? We now want to know if we have a switch. If we start off with it on and then add this inducer so that we turn it off, that is, we invert the response, do we actually get what we're interested in. And the top graph that's shown here is the response of what happens to the cell growth as we actually add our inducer, where the top part of this is now uninduced, that means that we're not adding anything chemically, and we see that the cells are continuing to grow. If we compare that now to the second line here, where initially they both started off at the same point, we add our inducer, we can see that the cells where we now have turned the gene off by activating our inverter, are growing to a lower point. We can also see a control plot in this very bottom here, which is what happens if we add inducer from the very beginning. That actually means that it turns off gene expression so low that those cells never grow. You can see that the OD stays flat and pretty much close to zero the whole time. So, we know that again, the response we're looking for, growth, is changing the way we want it to, and just very briefly, what's shown in these bottom slides is that the growth rate again is changing, this is now relative OD between those two. The activity is also changing, it's decreasing, and the mRNA levels are going down as well. Ok, so now we know the system is working exactly the way we want it to work, it was designed in a certain way, we seem to have the output that we're interested in from the design perspective of growth. The question now is, does it actually give us the productivity enhancements that we were looking for. And we're now again looking at the same system as before, where our goal is to make this compound gluconate, and the only difference now on this slide is that I've added now this product acetate which is a byproduct of metabolism, and is a representation of how much glucose flux is actually going down into endogenous metabolism. And if you look at now the charts here on the right hand side, the top one gives us the titers, or the concentrations, and it shows that more glucose is being consumed, that's what in this white bar here, is how much glucose is consumed. More of that is consumed when the inverter is on; the gray bar is how much product is being produced. We make substantially more product being produced here as well, and then these smaller bars here, the lightest kind of dark gray and the very, very black bar, give us an indication of some of the minor byproducts. And that's actually represented more easily in the bottom graph here, where again, I'm showing the yield, that is how much of what goes in as glucose is being converted to the glucaric acid product that we're interested in, sorry, in this case the gluconate, or the gluconic acid product that we're interested in. And the open white bars here give us the yield measurements and in this case we've actually increased our yield from about 0.7, and this was actually higher than what we had seen with the other system, which tells us the cells are happier now, and our yield in this case goes up to about 0.8, or a little bit higher than 0.8, so we have about a twenty percent increase in the yield. The grey bars that are shown here is this acetate by-product, and you can see an even larger reduction in the waste going to acetate. So, we have again a twenty percent increase in the yield here, but we also have almost a fifty percent decrease in waste, that is this acetate waste. Now, the last thing that we wanted to look at was the timing of the induction because we do know that based on exactly when we add this inducer to turn off Glk expression, we could have the cell growth go way, way down, I showed you that as a control plot, or if we wait too late, then the cell is not actually able to respond because it's going to stop being very active. So, what we're looking at here now is the OD, or that is the growth, at which we induce, starting from very early induction times, up to later induction times, and then what's shown on the y-axis is the yield relative to an uninduced culture. And we have two different yields that we're looking at, one is the yield of product, and that's shown in the top here, with the squares, and the second is the acetate yield, or, again, a measure of waste that we have here. What we find in this case is that the yield improvements are actually best when we induce earlier. That means give the cells a little bit of time grow, but don't let them grow too far, and we can see in our best case about a 70% reduction in waste and a 20% increase in product being produced. Let me summarize the story that I've given you about glucaric acid. I started by talking about how we could come up with a new pathway to be able to make this compound that was still a natural product, but whose natural pathway was too cumbersome from being, to be produced in E. coli. What we used in this case is part selection, or bio prospecting, to find the enzymes that we could move from one source into another source, and we're able to do this because once we know the DNA that encodes for those enzymes, we can synthesize that DNA, and easily move it around between organisms. And the second thing I showed you was this example of a synthetic biology device, that was the protein-protein colocalization study, which gave us increases in productivity. And those protein-protein colocalization devices, or the scaffolds, have been shown to be useful in other projects as well, so that we know that they are reusable and modular in a way that makes them very useful for thinking about how do we actually engineer the metabolism of cells to make the products that we're interested in. And the last part that I showed you was an example of how we might engineer the host, or chassis in the language of synthetic biology to give us further improvements both in the titers, that is the concentrations that we're interested in, and in the flux, or the yield of the product that we want, such that we get more of the substrate that we start with going into more of the product that we're interested in. I'd actually like to end this whole iBio seminar by acknowledging the folks that did the work. I won't go through all the names, but you can see them highlighted here in red, as students who are both currently in the group working on these projects, as well as former students and postdocs in the groups. I've recognized John Dueber as a collaborator, he is still at the University of California at Berkeley, and this work was primarily funded by the National Science Foundation through SynBERC and through the Office of Naval Research through the young investigator program with the last part of it being funded primarily by the National Science Foundation through the career program. I hope you've enjoyed the iBio seminar and thank you very much.
