 Okay, so maybe I'll start the So to bring it back then to what we've been doing so far. Let's see. We're in I Think we're at our fifth lecture now Is that right something like that And so far we've been we've been focused on this this decade Largely focused on this decade from 1958 to 1968 This marks as I say a watershed Period in in the study of quantitative bacterial physiology. So we'll wrap that up I mean in the next moment or two and then we'll turn our focus to more recent development. So when I say recent, I mean I'm very literally be even within the last ten years And so to bring you way back in the first lecture we talked about Minode's review of 1949 and two two Pieces of information that came out of that that I'd like to a manual is his his Minode kinetics for the growth rate. So if you add the concentration of some growth limiting substrate The growth rate looked approximately like this hyperbolic relationship if we grew the the cells in batch growth And you could characterize a growth rate in terms of two parameters then the maximal growth rate when the substrate is is saturating and the affinity if you like of the the Bacterium for the substrate Okay, and then the another part of that review that we talked about which I think is going to be important today Is this growth yield and so here he was looking at the not the concentration, but the the literal amount of Growth limiting substance it was in the test tube to begin with So he would change that and then measure the amount of cells that grew out of that substrate And there was over a large range and over many different nutrients you get a linear relationship that passes almost through zero And this was called the growth yield. So this was how much bacteria you got from how much nutrient it's like the efficiency of conversion if you like All right, we left behind mola. We went to our side. We left behind my node we went to mola and We saw these empirical relationships that they found for the growth rate dependence of the macromolecular composition of bacteria and here we focused on three state variables while for if you count the growth rate the RNA per cell the DNA the DNA per cell the mass per cell and they all had their characteristic growth dependence They were all monotonically increasing with growth rate changed the way that they changed it Which was by changing the nutrient quality the cells were growing in and then we spent Probably three or four lectures Going more deeply into the origins of these now We didn't go deep enough that we talked about molecular origins and that would take something like 40 or 50 years And that's still ongoing But for our purposes what I want to focus on in this course is descriptions of this level and this level All right, so we have these these rule-based phenomenological Results and we asked what could be rash? What could how could we rationalize these these behaviors? And that led to homestead and Cooper's elegant experiments looking at multi-fork replication of the DNA Which then explained as DNA per cell and to some extent the mass per cell Because remember this mass per cell growth dependence can be can be sort of Rationalized by looking at the time it takes for DNA rate to increase to a new rate upon switching to faster medium and the cell numbers to also Change to this new rate and there's a difference of about 65 minutes there Which is almost exactly the amount of time it takes to initiate chromosome replication Duplicate the chromosome separate and then divide All right, and so here we have With with the homestead or Cooper We have at least at this level of mechanism and explanation for these two curves and then early on in this lecture series We talked about this RNA per cell being rationalized by So what we really talked about was the ratio of this line to this line being rationalized in terms of the the protein Production the protein production is driven by ribosomes and most of the cellular RNA is Ribosome affiliated RNA All right, that was a whirlwind recap of what we've done so far But I put this up here because I want to come back to it when we talk about the the modern developments blessing So let me pause there Are there any questions about what we've done so far? I mean it can be anything from Mano to to what we did yesterday plus you again any questions All right, so let's let's Journey into the modern era, so this is now modern Bacterial physiology and for almost 40 years this type of experimentation Became more or less extinct and the reason for that I mean it's there are many reasons of course, but some of the main reasons are because People became more and more interested in in molecular mechanisms So Mano for example one is Nobel Prize ran this time for showing how How protein regulates its own expression or the expression of other proteins in the cell by binding the DNA and shutting off transcription? People became very excited about this and then started exploring the consequences and the Networks that emerge from that sort of picture and that's been ongoing for say 60 years the other reason I One reason is that Mano passed away in the in the early 70s and I mean quite unexpectedly and so that arrested to some extent some of the few Physiologists that were still interested in these types of questions and then finally there was a huge push in the early 1970s to Solve cancer. This is Nixon's big push to cure cancer And so people turned away from bacteria largely, but that's changing in there And that change is being carried out for the most part by physicists who are going into biology. And so I think that's Potentially why Mateo asked me to talk at this school Well, I'll tell you some of that and then you tell me if it's interesting Well, it doesn't matter actually even you can't tell me if it's interesting But more I want to know if you have any questions. So let's talk about the modern biology and I'll tell you what's changed in the 50 years since since Monod or and Moa so not much In this area for about say 50 years Well, I say 40 years It's not to say that everyone stopped there were isolated pockets But it didn't dominate the thinking of the biological community as it once did The mindset of the the say the spirit of the people changed So that that changed That is starting to change and to to Developments at least the last ten years are the following. So two big changes big changes are One an appreciation for constraints in the in cellular growth if you like better to say a quantification resource allocation constraints Resource and I'll tell you what I mean by that in a second in bacterial growth and Technological developments that have allowed us to visualize and record with high fidelity single cell growth and so If you like this is a legacy of Moa and this is a legacy of Helmsteader and Cooper. So single cell technology, let's say okay, and this is so this carries on Moa's like or Moa's legacy and this is Helmsteader's legacy So we may talk a little bit about this. It's it's asking more detailed questions about the cell cycle and the dependence of cell cycle on different methods of growth and inhibition But I'll focus on this. This is the Material that I know best And so this is then asking about this type of phenomenological picture But in different scenarios in a different context of growth okay, so what we could do is And what has been done is to repeat Helmsteader's Analysis on a single strain under different conditions of growth repeat Mola's on the same strain different conditions of growth What I mean is different nutrient environments And then go deeper and deeper and deeper and say okay Well, how many ribosomes precisely are there how many RNA polymerases precisely are there and things like this and that that has been done So in the intervening decades have much more detail But no sort of changes in on what made in the big picture if you like We've collected Many details the big picture remains now now I want to contrast all of this work With with something that we haven't really spoken about And that is how we change the doubling rate So in all of what we've talked about In all we've discussed The growth rate has been changed or modulated by changing the quality of the growth environment by changing the sugar source by giving it different Nutrients whatever it might be right growth rate modulated by Nutrient quality and I'll contrast that with other modes of growth inhibition in a second So this is a course. It's just you change the chemical recipe of what you're feeding them their diet if you like But now we can ask questions like what if we want to see how bacterial physiology alters when we treat them with antibiotics for example Or if you're more physically oriented you might be thinking what if we change the pressure or the osmolarity or what if we change the temperature All of these are natural buttons to twist as a physicist or as an engineer or as a process design engineer But for the biologists at this time the most natural buttons to change were carbon source nitrogen source enrichment for amino acids and so on Now if we do want to change to different growth strategies What are we gonna do right are we gonna? Repeat then all of this detailed measurements of the intervening decades of 30 years worth of experiments to count How many ribosomes there are at this temperature and this pressure and so on I mean that's one way to do it and that would be a brute force sort of Let's say statistical mechanics approach But what I want to talk about here is more thermodynamics phenomenological approach So what I'm going to outline are some other modes of growth change and then let's talk about what I'm talking about Okay, so We can imagine you can imagine Let me put a butt here Imagine many ways To modulate growth we can maintain exponential growth, but I hear I'm talking about changing how fast they double and so we can think of for example physical perturbations and so these might be say temperature pressure Osmolarity osmotic pressure and there are many Biophysics labs across the world that are looking at exactly these types of perturbations to grow You can imagine chemical perturbations So these would be say abiotics other inhibitors of growth and so that's sort of redundant, but What I mean here are clinical antibiotics or other types of growth inhibitors that you wouldn't necessarily give to a human or an animal Or you can think of biological and so let me write this up and then let's talk about all this and And so for example You may be using your bacterium as a factory for developing some bioproduct that you're interested in Insulin was one of the first so we manufactured we didn't but some it has been manufactured a Bacterium E. Coli that produces insulin and they can't possibly use the insulin But we just feed it glucose it makes insulin we grind up the E. Coli Purify the insulin and give it to humans We use E. Coli to biomanufacture a very at that time expensive protein Okay, and so this is protein Production or bioproduct manufacture. Of course if it's making something it doesn't need it's going to do grow more slowly And other things you could have Toxic proteins and we'll talk about that Probably not today, but next week and so these are I've just I mean these are fairly arbitrary distinctions But I wanted to to convey to you that changing the carbon source is not the only way to change the growth of these bacteria and of course you knew that but The kind of the variety is astounding because within each of these is you know Hundreds of degrees of freedom if you like and what if you want to do simultaneously two or three Would you then go in and categorize chemically the state of the bacterium in all these different growth rates? It's almost unimaginable. I mean it's possible that we come up with throughput technologies that allow us to do that But then how do we make sense of this big data that accumulates if you like and so Counterpoint to all of this or another way to look at this is then to ask can we generalize these types of relationships to Arbitrary perturbations and if we do do that what do we learn about the growth of the bacterium? I says I Mean I don't want to strain this analogy too much, but it's like Carmel asking what's the efficiency them the best efficiency of a steam engine? Because I mean there's no use trying to build one that's better than that And so here what we're going to ask is what's the maximum rate of growth for these bacteria under all these types of? constraints or or inhibitions of their growth All right, so that's the preamble that sets up the rest of the course, but let's let me pause here Is it is it clear when I'm talking about when I talk about different ways to modulate growth? So there's there's also aside from this the question of whether you want to grow yourselves You know, maybe you don't want them growing exponentially. Maybe you want to you know They saturate out you take them out of your bioreactor and grind them up That's a whole different engineering question We're still going to be talking about balanced exponential growth But we're going to be changing that slope on a log linear scale. We'll be changing that exponential growth rate through different methods Not just by nutrient change. So that's the that's a scenario any questions about the scenario So for this course, I'm going to leave. I'm not going to discuss these Right. Why is it because I don't have any data on them? I Will talk about these for the most part and and I'll start here with chemical inhibitions and antibiotics Okay, and then we'll go on to some of these biological ones which tie together this phenomenological picture that I'm about to talk about so before we We start talking about what's happened lately Let me bring you back to the night heart manic magasinic stuff. We talked about in the second lecture. Maybe even the first lecture so let's talk about constraints on protein synthesis and so we had this idea that bit of protein synthesis is is linearly proportional to the number of of active ribosomes in the cell this was something that Magasinic and night heart inferred from their linear relationship between the RNA to protein ratio I'm going to remind you all this But now we know mechanistically that this is true We've been able to reconstitute protein synthesis in the test tube that was done quite early on the early 70s And is now sort of routinely done And so you can just add energy ribosomes and some substrate and the ribosomes will turn out proteins It's astounding. So we know exactly what you need And this is is turns out to be true. So the road through this is proportional to the number active Ribosomes Okay, so the first underline is what's going to give me the equation that I'm about to write up But the double underline is something we didn't talk about when we discussed night heart and magasinics work Which is it not all ribosomes necessarily need to be working? Okay, so what I mean by this is it Suppose you have the mass of protein In a given volume or in a test tube or whatever here. I'm not thinking of it as a per cell measure I'm just thinking of it as a Chemical measure take out a milliliter measure how much protein is there and in that milliliter in terms of its milligrams And then see how rapidly this increases. We know in balance growth. It's this It's going to increase exponentially And then from that night heart of magasinic We had that it was proportional to the number of ribosomes in here I'm thinking of an inactive fraction that I'm going to So these are inactive Now a person could imagine this two different ways one could imagine that all Ribosomes are active, but their rate of translation changes with growth rate Or you could imagine that there's some small population that is not Translating not actively engaged in protein synthesis, but those that are translated some maximal rate The the truth is is a combination of both and we may get that we may get to that this afternoon If not, it's in the lecture notes, but but it's not it's a minor point The reason that I write it this way is because I know that this Relationship between the growth rate and the ribosome abundance is linear With a with a at non-zero offset. So let me show you what I mean by that Okay, let me pause any questions about this This expression so when we were talking about it the beginning of the week we had This claim by night had some night heart of magasinic that was then Used to rationalize the linear relationship between the RNA to protein ratio Because this number of ribosomes was proportional to the abundance of RNA in the cell That was their argument I'm modifying it somewhat by allowing Some growth independent offset I'll show you how that comes out in a second so then in terms of RNA We had that the number of ribosomes is proportional to the total RNA in the cell and so then we had that the growth rate over some k hat was equal to some RNA to protein plus some you know RNA Not inactive say over total protein. And so then when we we sketch side, there's a minus idea So then when we sketch this We had this RNA to protein We had this growth rate and we had a straight line So night heart of magasinic we're interested in this in this fast growth Where this this offset is sort of negligible compared to the changes in the in the slope But I want to come back to that offset So that was night heart of magasinic. Let me pause now any any questions about any of that I'm going through fairly quickly, but it's it's fine if we go through in one detail Which which part so going from there to here Which is from from there is that part okay? Okay, so then what you end up with is this Which is an algebraic equation. It's okay, and I divide both sides by MP and Both sides by K Okay So what I mean by RNA over protein is this? Which is then converted into total RNA, which is why I had a hat on the K It's just a proportionality constant and then dividing through by the total protein mass gives me those ratios Yeah It's the inactive the active ribosome component So that would be right RNA affiliated with an inactive ribosome It not as message, but as structural RNA Okay, so so the the ribosomes or machinery that they translate If they're not actively translating they're still they're still made of RNA So if you measure the total RNA, you'll still pick that up I doubt that'll be part of your measurement, but it won't be functional. It's a problem That's a good. I mean that's an important point. Is it cleared everywhere that? These ribosomes are composed of RNA. That's their main structural element, and if they're not translating You're still picking it up in the chemical reaction that tells you how much RNA is in the test tube So they're sitting there. They're not driving any synthesis. They're not catalyzing any production They're just you know inert, but they're still made of RNA and so they still count when you measure the total RNA And so you still get this This sort of offset no matter what the growth rate is you're still going to pick up some of these inactive ribosomes So this would be then your RNA Zero over total protein So this is inactive Does that is that okay? Okay. Yeah No, no, no, no. No, there's an intermediary machine. Let's quickly go over that. Okay, so part Part of the difficulty with the structure that we've had in this course is it The all the biology I gave you in the first five minutes of the course and it's not obvious what stuff you needed So let's go back to it really briefly. So recall This is sometimes called the the central dogma of microbiology or biology in general See the DNA and it's read by an RNA polymerase Which we haven't talked about and we haven't talked about that because I Don't think it's limiting so there's there's a subpopulation of people that would disagree with me But for for our purposes and because they're not here. Let's assume it's not limiting So this is an RNA polymerase which turns it into RNA Now some of that RNA the RNA that we're we're typically thinking about when we're thinking of a biological scenario is made into functional protein It and it's made by ribosomes Which make the protein So about five percent of the total cellular RNA is is what's called translated into protein So this is about five percent so eighty five percent is ribosomal RNA and It's it's you know used to make ribosomes and by make I mean It's a structural element. It's not changed. It's not interpreted. It's the chemical is literally folded up You know, it's not so when I mean literal I mean, it's not it's not converted into protein or anything the RNA just bundles up and makes its own machinery so the ribosomes is is Two grams of this ribosomal RNA to one gram of Ribosomal protein and so again if we're talking about biological systems very often we focus on enzymes which are 100% protein typically Here's a machine that is You know comparatively little protein It has 54 of these little proteins stuck all over it But it is for the most part just a tangle of Ribosomal RNA There are a couple of consequences of that which I'll tell you right now But it may not be relevant until later on and then I'll remind you of it So if it doesn't make sense, we'll come back to it, but let me pause is this picture So again, this is something we talked about at the beginning, but it's not clear. It wasn't clear then in foresight What it was going to be used for so now in hindsight. Does anyone have any questions about these different processes? This is a genetic information There's very little of this. I mean, it's a very long molecule, but there's only one chromosome On the other hand and it comes back to your point is if this piece of DNA happens to be close to an origin of Replication well, then you're going to get duplicates of it as the cell replicates its origin And so you'll get multiple copies of whatever piece this DNA encodes. That's a small point. I'll come back to you That gets turned into this helper molecule the RNA Which then gets jumped on to by ribosomes and turned into protein Unless it's one of these ribosomal RNAs in which case it just folds up and makes a ribosome And then the missing 10% or 15% are what are called transfer RNAs and they're what are responsible for bringing amino acids to the ribosome But for now we won't we won't talk about them. They're going on in the background Pause again any questions K and k hat So k hat would then be if this thing is say You know say it's called sigma Or what do you want to call it? Let's call it. What's the proportionality constant? Bbm Times RNA So, okay, where are you there? Yeah, right? So then you would put that in here and K hat would be Would be m times k? I'm just saying it's it absorbs a proportionality constant Does that make sense? Do you see what I'm saying? I convert from numbers to numbers of Ribosomes to molecules of RNA that means that this thing Has the same interpretation. It's just got different units now So, is that okay? It's okay. Okay. Any other questions now back to you. Yeah, so now tell me your question again. Yeah This is what you call the mRNA. This is what's called the message No, anything mRNA is turned into protein So this is one special class of RNA Then there's other RNA that's made by these guys that has never turned into protein. It's turned into ribosomes without any sort of Interpretation it just is the molecule turns into the to the machinery It's okay. I feel like I've dwelled on it enough that I may be gone from it being confusing to clear to confusing again So I can pull it back a bit. Does does anyone have any questions about this process? Could you say one more time? inactive, okay, so So then the question is what is the origin of this inactivity? I think so this is again I'm conjecturing, but I think there's reasonable Proof to to back up some of this One way that they can be inactive is that they need to find this RNA They need to bind to it. And so that's it. That's a diffusive surf search problem And so if they're searching for RNA, they're not making protein. And so there's a time associated with that Is that okay? And so some of them will be searching for RNA Some of them will be on the RNA and waiting for amino acids to make this and so again It's a diffusive search problem bringing amino acids to the ribosome to get incorporated into the protein Those would also be inactive for a moment, you know as they wait and So those I think are the two main ones the other one is that between the the time it takes to make this RNA and Then for it to fold up and be a functional ribosome takes a you know a couple of seconds maybe or a few minutes and So that also those will be inactive There's this transit time Those are probably the three big ones and the two I said first are the biggest that folding maturation time is pretty minimal Okay Any other questions? Yeah, yeah, yeah, yeah, that's the way that's the first way that we're gonna fiddle with this So she's suggesting I'm paraphrasing and interrupt me if I misrepresent you so her suggestion is you could perturb growth You can alter this By changing that or changing this. What was the other one? Those were the two we'll intervene here so these it's more sort of Yeah, it's more difficult to think about intervening here. It would take well. Yeah, so we focus here So the antibiotics that I'll show you first are antibiotics that target this process But there are other ways that we could intervene which is that to make this protein you need to supply with amino acids and So if you cut off that supply chain, then that will change your growth rate as well All right, let me pause any other questions. Good. Those were really good. All right Here we are. Yes. Okay And this okay Push too hard with my chalk Okay So like I say ribosomes are mostly made out of ribosomal RNA structurally, but they do have a protein component And for what I want to talk about today Yep, still today is oh, we have two lectures today, too So what I want to talk about today is constraints And it'll be easiest to see the type of constraints that I have in mind if we convert from RNA to protein And that's going to be a again another proportionality constant and so I'm going to change the the letter that I use the protein content of these ribosomes is Fixed like I say we we now know the the molecular origins of that that's a regulation But for our purposes take it as a as gospel if you like it's it's fixed you have a ribosome. You always have the same Mass if you like of proteins associated without ribosome Okay, so we can convert from Total RNA and now I'll use some other proportionality constant I don't want you to go crazy. Maybe I'll use sigma this time sigma times the our protein So if the total amount of Ribosomal RNA or RNA is proportional to the ribosomal RNA and that in turn is proportional to the protein Ribosomal protein we can re-express this fraction as Ribosomal protein to total protein and I'll write that up and then let's talk about it if I plug this into here. I will have Lambda over now. I'm going to call this kappa t and I'll tell you what kappa t is in a second our protein per total protein minus our protein inactive over total protein Where this thing now is your original k times m Times sigma if I haven't done anything wrong But again these m's and sigmas are just proportionality constants that make sure that my units are okay But k still retains the meaning of the interpretation of the translation rate Or I'm going to do one more thing I'm going to rewrite this as some fraction our protein per total protein is going to be equal to Lambda over kappa t Plus this now I'm going to call this by our mint right and it's this equation this Empirical relationship that I want to talk about today at least for the first lecture and we'll come back to it again in a second But it's exactly the night heart of magasanic relationship that we saw at the beginning of the of the Course but now Explicitly with an offset which empirically we also observe with the interpretation that this offset is ribosomes that are not Driving protein synthesis and hence growth rate So I've introduced two new Two new Symbols if you like this phi Which is a protein to protein ratio? In this kappa which has the same meaning that we had at the beginning of the week Which is a translation rate how rapidly does one ribosome make protein in? This in this scenario would be how many grams how fast does one gram of ribosomal protein make another protein if you like Okay, let me pause though Are there any questions up to this point? So the the interpretation that we had previously is retained It's just an update to the symbols and I promise you it's not a semantic update I mean it's not just superficial that will gain some true insight by not Looking at the RNA to protein ratio, but rather the protein to protein ratio Okay Let me pause though. Is that okay? Okay. Let's look at some data then so this guy then The interpretation is that this is the protein synthesis rate Per ribosome Okay, I put those in brackets because the original K was protein synthesis rate per ribosome in numbers Then we converted that to the RNA mass of a ribosome Then we converted that to the mass of a protein, but it doesn't the interpretation is the same all right, so now Let me show you some data that That's a that then corroborates this idea, okay Any questions though before I do that? Yeah, yeah, yeah here and then I'll go there. Yeah You go for you go first No, no, no good point so What not how the magasinic did was to ask what does RNA have to do with protein synthesis and their speculation was well Maybe it's the ribosome that's driving protein synthesis. That's validated. That's clear So here I haven't done anything yet. I've just updated their view But what I'm gonna give you some foreshadowing which is as she suggested she suggested We're gonna start fiddling with this So they did not fiddle with this They got this rationalized the role of ribosome and protein synthesis and that was the end of that That's an avenue of research But what we'll see is that fiddling with this opens up a whole box of Of insight if now that was a terrible analogy a whole a whole different view of what's going on in the cell Okay, which comes to your other question, which is or no, I think her other question Which was what if we start fiddling with something else like? Your question the supply rate and things like this came will come to that But I think in this framework, it's easier to see than in this framework where one it has to keep Manually converting units from RNAs to proteins But you had a question. Is that okay? Wait, I sorry. Yeah, sorry. Go ahead. Yes Yeah, yeah, so it's important to distinguish the template RNA, which is being read and The machinery that's reading it which also contains RNA Yeah, exactly. So the template never has protein associated with it. It's just an instruction set yet But then the machinery always has protein associated with it and it and whether it's active or inactive It has the same content of protein associated with it Because what happens is the machinery forms and then it's formed and it stays formed and whether it's doing its job or not It retains that physical characteristic that it's tangled of its RNA tangled with proteins Is that okay? Yeah, I mean that's important because otherwise this proportionality you couldn't carry through Any other questions? All right, so let me show you some data then and this is now it's something that another Technological advance that I didn't talk about but is equally important is our access to mutants Which is something that night heart of magasanic. It was incredibly laborious for them You had to you know, you devote many postdocs and and PhDs to try and find a few useful mutants now You just I mean it's a piece of cake You can go in and strategically Change pieces of the DNA of the organism it will it'll take you a week maybe two. It's incredible So what I'm going to talk about are some mutants that we can look at that We're not they weren't available until the mid 70s, but now you can make them in a couple of weeks Okay, so this is a data that we had with night heart of magasanic But now I'm plotting the vertical axis as a protein fraction So ribosomal protein to total protein the shape of the line is the same It's just that they we have this proportionality between the RNA to protein ratio and the ribosomal protein ratio All right, the colored symbols are different nutrients. So You know warmer colors are the are the least nutritious media and then the greens have lots of vitamins and things like that and And the sort of primary colors or glucose as a carbon source the lighter colors are a different carbon source called glycerol But the point here is that group going along this line is changing the nutrient quality Which is what we've been talking about the past lectures The squares are data from another lab the diamonds are from another lab the diamonds are from I think 1968 The squares are from 1976 Different strains of E. Coli the purpose there is to show you that these these measurements are incredibly robust lab-to-lab day-to-day strain-to-strain In in some way an intrinsic property of the bacteria Okay, and as physicists that I think that's an appealing facet Okay, so now I want to talk more about so that that line is this line and that intercept then is this is this Intercept I'm giving you the interpretation that it's an inactive fraction But you could just think of it as a phenomenological parameter that this data is strongly positively correlated All right, so strongly correlated that you can compress all the data into a two-parameter fit You could think of it that way if you prefer I Will however suggest that the interpretation of this is a translation rate is probably sound because I'm going to show you some Data that corroborates that view so I'll do that in one moment, but let me pause Are there any questions about the data? It's okay. All right so now like I say we have mutants and so Ignore the inset for a second How am I going to do this? All right These circles are like the data that I have over here. So circles correspond with circles There's nothing wrong with these bacteria These triangles have a mutation inside one of their ribosomal proteins that makes them produce protein much slower and The reason that they do that is interesting in and of itself. They proofread They're very fussy and so they make sure that they make no mistakes, which makes them very slow These up triangles are moderately slow these damn triangles are very slow All right, and we know that because as I said we can make them produce protein inside a test tube And we can measure how quickly they make protein All right, and that's what's shown along the horizontal of this inset Is how quickly they make protein in a test tube? Okay, is that set up? Okay, and so these mutants the triangles are mutants Make proteins more slowly per ribosome. That is to say their genetic Changes that change that kappa t if you believe that kappa t is the translation rate And what you see is it I mean it's hard because there's only four data points because these strains are quite sick And they don't grow a lot of things, but the slope changes the ins the intercept Is more or less constant doesn't really matter, but you get this linear relationship Retained except now with a steeper slope It takes more ribosomes to make the same amount of protein if you like in the same amount of time And now if you take this slope and you take the reciprocal of the slope That's what we interpret as this protein synthesis rate and you plot that along the vertical and you plot this test tube Translation rate along the horizontal they corroborate very well I just to say you get almost a perfect correlation between the two and so that that suggests that Our interpretation at least of this parameter is reasonable. Let me write that up We will you ponder the data and then let's let's talk about it. We'll talk about the numbered circles in a second so we can look at at Mutants, how do we provide these mutants? So these ones came because they have a secondary feature which is that they're resistant to a particular antibiotic called streptomycin and the streptomycin is an antibiotic that goes into the ribosome and then Keeps it from making protein the mutation that makes it so that that antibiotic doesn't bind Also, serendipitously makes it so that the ribosome double checks when it's protein synthesizing So it has very low error rates very slow procession speed Did that answer the question so the way they found them was by screening for antibiotic resistance? And then when they measured the what these resistant mutants did they noticed Surprisingly that they had very low growth rate and they made very few errors when producing protein Okay, so this wasn't the original intent of those mutants. We we took these mutants 30 years later and and did this We can look at mutants With impaired protein translation rate And so I'm going to distill this figure into a chalkboard version And I put that figure up there so that you can see that I'm I'm going to be making some idealizing You know these are going to be cartoon representations of that and so you'll see the degree to which I exaggerate Okay, so we have Oops, we have some different media. So I'll have again these different growth media Growth rate is going to go like this. So this is slow This is fast and then we have mutants So I've got this mutant one which I'm going to use a dark color for I have mutant two Which is going to be or let's call this guy mutant two this guy. I'm going to use stripes and Then I have my my wild type. Oh, I have two mutant twos. This guy's mutant one This guy's severe and this is non and so their protein translation speed is going to be like this too what I want to do is is Plot the the ribosome of abundance per growth rate in each of these media with each of these mutants And that's the figure that I have on the right. But now as I say, I'm going to idealize it in a cartoon like this so now I have this ribosome mass fraction have this First line which is going to be zero This and then this triangle square circle circle Triangle and I made this guy strong Strong mutant and this guy is the moderate mutant and this guy is it This is is m2 m1 and this is what I call the wild type So the parent and this is a growth rate. Pardon me. Wild type is no mute. This guy Is just the regular old strain Okay, and I'm idealizing But I'm doing it with a purpose Okay, and so this is meant to represent that and now if we take the slopes of these guys And we look at there so if we take the reciprocal slope then what you end up with is so one over slope Which is going to be this Is this kappa t? Parameter and you look at the translation rate inside a test tube your acids per second inside a test tube You get an almost Perfect correlation and so you would end up with Mutant one down here Mutant two here No, two here mutant one here and wild type me up here Okay, so this this is meant to to Substantiate or validate or at least rationalize to some extent this view that the reciprocal slope So remember that this straight line relationship is empirical we it doesn't come from any sort of model in the background It's like Boyle's law or something or Mendel's laws of genetics It operates at a very high level Then we can go lower and we can infer what each of the parameters in the in the relationship mean And one such inference is that this reciprocal slope is the translation rate per riba zone and That came from that night hard mega-sanic Interpretation that the RNA is in some sense a proxy of the riba zone abundance and the riba zones make proteins Well, that's true. Then if we have mutants that have Slower riba zones, we should see this linear relationship, but now with a changed slope The more slowly they translate the steeper the slope and that's indeed what we see. Okay, so that's On the surface of it what I wanted to show you but there's something deeper going on here Which I'll talk about in a moment. So first any questions about the data Does everybody see what I'm trying to get at but clear on On what I mean by mu M2 M1 and WT Okay, so I'll say one thing for that. So it's standard Biological practice to call things wild type if you haven't broken them if you haven't mutated them I mean, it's a relative term now because everything's evolving out in nature And so that say you happen to isolate some E. Coli from a sewer 10 years ago and put it in your freezer That becomes your lab's wild type. I mean, that's not advisable But that's how people used to do it in the 50s and then every mutant you make from that becomes a mutant strain Relative to that so-called wild type and so you'll see wild type throughout literature as sort of your canonical lab strain If you like, okay. Yeah, come on back to your question though. Well, yeah, okay So the severity that the this qualitative Distinction between severity I'm making on the basis of their growth rate. So if I have So for example, it's easiest probably to see in the light blue strains No, I mean, it's easier to see in the dark blue. So dark blue is all in the same growth medium So the recipe inside the test tube is the same But the guys with the downward pointing triangles grow about half as quickly as the circles do So the diets exactly the same but one of them grows twice as fast Okay, so I would say that the one who's twice as slow or half as fast is Severely is a severe mutant It shows a lot of change Does that make sense? Yeah Any other questions? Yeah of the horizontal axis amino acids per second. Sorry. Yeah So here they can they can look in the in the test tube and count How quickly amino acids are assimilated into proteins by these ribosomes? So again, you've got a test tube You've got ribosomes that are extracted from these cells but purified you add energy you add amino acids and you add some other sort of Ions to make sure that everything's Happy and then you let it go and it makes protein at a certain rate You measure how quickly it's making protein and that will tell you how fast these guys are operating Yeah, right exactly. So the slope of this line then would tell you what these What these proportionality constants are exactly? but there's that's exactly true, but there's a There's a subtle point which is that we can't get protein to get produced as quickly as in the cell in a test tube so You'll get this M and a sigma for test tube protein translation, which is which is fine And then then you just say wow probably there's another factor from the test tube to the bacterium Is that okay? Okay, any other questions? All right, so we More or less in the last hour or so retrod the I mean those some preamble But we've retrod the or walked over again to the Megasonic night heart paper of 1960 And really all we've done is changed our units and looked at some mutants to corroborate this picture Okay, but what I'm suggesting to you is that although it's not I haven't labor the point yet There's more going on in this figure Okay, and and we'll we'll talk about that in five minutes. So why don't we take a break? But before we do that There are other ways besides genetic mutation to slow down the translation rate and one of those ways is by adding antibiotic Okay, and so when we were talking earlier about how do you fiddle with this translation rate? If you add an antibiotic like for example, there's an antibiotic called chlorenphenol, which he used to treat eye infections It's chief mode of action is to go and jam into the ribosome so that it can't take in amino acids And it's fairly reversible and so it goes in there comes out goes in there comes out and the net effect Is that these ribosomes translate more slowly? Okay, and that's that dashed line and so I won't want to come back to that after the break But let me know if you have any questions. Otherwise, why don't we take a break and come back in five minutes? Any questions good. Okay. I'll see you in five minutes some things came out of that. All right, so I Okay, two things now, maybe I'll erase this and then let's talk about it Because we have you know the pictures over there on the on the left Okay, and so I want to tell you the relationship conceptually between this parameter and the protein translation rate Before I do that. Oh good. It's coming around. Okay, so remember we have this mRNA This template RNA We have this ribosome and it's making sorry Its whole job is to take free amino acids and covalently bond them with other free amino acids well to a chain of amino acids to make a protein so this is protein which is a polymer and the monomer is this amino acid so we got about 20 of them and The the sequence within which you join them gives the polymer that forms special properties These are the enzymes and the operational proteins in the cell Okay, this is not a D. This is an amino acid circle. Okay, and this Translation rate down here is how quickly one ribosome takes this and Sticks it into this chain Gain chemicals that would look something like this you'll have Ribosomal protein is Going to Take in some amino acid and it's going to make protein plus ribosomal protein So this guy is the same thing as this thing. It's like an enzyme It's converting amino acids Into proteins and this rate of reaction is this kappa t or sorry. No is this Well, yes, we think it's kappa t the rate of this reaction We think of as K. So K is amino acids per second pro ribosome. So it's numbers This cap at t if everything is this, you know, our interpretation is correct. It's the exact same scenario, but now per protein gram inside of this ribosome and we do that so that we can get this Fraction, so it's just a units conversion Which means that if we plot the translation rate this k on one axis and this kappa Which again is an empirical parameter It's it's just you take a ruler stick it on your plot and measure the the rise over the run If we take that slope that kappa t and we plot it on the vertical We should see a straight line that goes through the origin and the slope of that straight line is a proportionality constant between the rate of protein synthesis per ribosome in our test tube and The rate and this slope on our the rate of protein synthesis per gram of ribosome in ourselves I think I made that money toward the end But are there any so are there any questions about the meaning or the interpretation of this plot? Which is this inset here? This inset is meant to suggest or meant to convince you that this empirical slope is Actually What we think it is we it's the rate of translation per ribosome. Let me pause is that does anyone have any questions about that? I don't know if I made that clearer or much worse It's a it's a reaction rate if you like k and I'm saying kappa t is also the same reaction rate And my evidence for that is this inset point number two These guys are like different species so you can think of them as like a cat dog bunny rabbit and I change the colors for each species by changing what I feed them So the straight lines are with the triangles pointing down would be bunny rabbits different diets Then cats different diets are the up triangles and then dogs different diets of the circles, okay? And so these are completely independent Experiments of one another and these guys are the creatures upon which I'm doing this experiment. I hope that was clear or is now clear Is that now clear? All right last thing that I wanted to say is this this denominator Necessarily includes ribosomal proteins because when I chemically measure the protein content, I can't distinguish In the bulk measurement that I'm doing Ribosomal protein from regular protein or not that I can't I don't this is a total protein Which means that this fraction is somewhere between zero and one and that's important Okay So let me go through those one more time. This thing is the same as this thing If you or that's what I'm trying to convince you with this plot Okay, second point is This ratio is always between zero and one those are the two takeaways I want you to have in the next five minutes, so I mean we'll carry it on to the next five minutes But let me pause any questions Yeah, yeah, so that line that that hard solid line should pass through the origin and so there What I have here is the best fit between those three points and it doesn't pass with the origin Equally what I could have done is constrained it to fit through the origin, but then that imposes That imposes my own beliefs on the on the interpretation. Do I mean? Yeah, if we're if the interpretation is correct, and it passes through zero and so this error here I I think there are two sources one is the error bars on these and the error in our interpretation and So if both of those are if that error is small then both those errors Hopefully are also small, but again, there's no guarantee right and so this that's it. Is that okay? Yeah, oh, okay, so this points to a larger picture, which is that? It's also true in physics, but more so in biology where we have been direct evidence I want you to think of these as as a as a court case Basically near the jury, and I'm trying to convince you All right, and so I can give you this kind of evidence But you you look at it and you say well, I'm not you're not really reliable witness You have a vested interest in this and so you look at this and you ask you know what kind of errors are going on Okay, okay, I'll take that and then you look for other bless you Circumstantial evidence and what you want is for all the Forks of evidence to be pointing in the same direction You don't want there to be some convincing contradictory evidence anywhere now, of course I'm I'm the one who's presenting this and so if there was Contradictory evidence, and I was unscrupulous. I wouldn't show it to you But the point here is that her question why doesn't this go through the origin is a perfectly valid jury type question And so if you're looking for a reasonable doubt that might be one you write that down when you're going to do your final Deliberation does everybody know what I mean and So that what what I said to her and and I'll say again in different words is it if You believe this interpretation that the slope is the translation rate The slope of that line is it is the translation rate Well, then this dark line should pass through the origin and it does not and So my suggestion is that that becomes because there are errors in the data But then you can come back to me and say no no no Maybe the errors in the data are just fine It's an error in your interpretation and that's fine And then we're at a then then we we leave these two contradictory ideas up in the air And then you come back at me and say provide me with some evidence that this is really the translation rate Do you like do you see what I'm saying? It's like a courtroom drama Well, he's probably less exciting to watch Well, I hope not maybe it's the same. All right. Okay, but is this interpretation okay? Now I keep I keep harping on this I want to keep my interpretation distinct from the data and I think that's important too But it's very very easy to do what we call reify where we have some data Like this straight line and we have a slope and instead of calling it the slope I keep calling it the translation rate I mean, I don't do that But one can do that which is imposing a view on the data that is not there Right, there's the data and then there's our interpretation of the data and although it's tedious at least in this in this course I'm going to keep them distinct as as much as I can because I think it's It facilitates the learning Okay Pausel Well, it's okay Okay, now it's said before the break that that the point of interest that I wanted to draw your attention to is this dashed line and And what I meant to not do was erase that All right, I'll just have to do I'll have to use the data Although it's not altogether clear in this picture the different colors are different nutrients So I'm feeding them different things and if you squint It looks a little bit like each color has its own set of lines going through it I mean it's not as convincing Because there's quite a bit of scatter and they're only three points But now another way that you can affect this translation rate Like I said is not through genetic mutation, but through the addition of antibiotics Okay, and that's what I want to talk about now, and I think yeah, I think that this will work out very well Okay, so instead of genetic mutation we can um Alter or inhibit the protein translation rate or protein synthesis rate with antibiotics and so clinically I mean in the 50s with this boom of Research activity in in antibiotics and these are chemicals that Inhibit bacterial growth and they do that through a number of ways one of the largest families of antibiotics Which is not really used too much anymore because of resistance is protein synthesis inhibitors And so these are often chemicals that look a lot like amino acids that just jam the works So it's like you've got a you know, maybe a paper press or something something synthesizing and you just keep Standing next to it and jamming in a wrench That's what these antibiotics do okay, so for example the one that I want to talk about and the and the the The sort of the The name of the antibiotics not important unless it's of interest to you the one that I'm going to talk about is chloramphenicol And as I say, it's not really used clinically except if you have an eye infection So what I'm going to do now is take a a Go back to this graph I'm going to say take light blue which is some particular medium composition and I'm going to break that up into six test tubes and add no antibiotic a little bit a little bit more a little bit more a Little bit more a little bit more so now that light blue circle test tube has an increasing concentration of antibiotic in it I'm going to inoculate with bacteria and I'm going to let these cells resume exponential growth So it's not enough antibiotic to kill them. It is enough to make them grow more slowly Okay, so what I'll do is take For example, what did I have here circle? Square Triangle, so this was growth rate and this is this protein Revital protein mass fraction. I'll take one of these and And I'll add antibiotic to it. Okay, so now I've got sort of you know So these are all squares and then my antibiotic concentration goes like this So the feedstock that I'm giving them the composition of what's in the test tubes is the same in all the test tubes But what's different is the amount of antibiotic I either inhibition of their of their protein synthesis. Does everybody see the scenario? Okay, now the question becomes What happens to this square? If I start to measure the ribosome protein and divided by the total protein So I add antibiotic it's going to grow more slowly So each of these test tubes is going to appear sort of further to the left Because that's what an antibiotic does now the question is what happens to the ribosome content Is everybody comfortable with what I'm about to do So this is like a rat out of a hat you might already be able to tell from the Rightmost figure What we get is an increase in the ribosomal protein fraction And so here different colored lines correspond to their other cognate Initial points and the numbers are the concentrations of this antibiotic And so the reason that I told you the name is so that it wouldn't be confusing to see that blue line So these are the concentrations in micro molar of chlorophynical So everybody see what's happening? So the solid black line was that line by night heart and mega sanic that we saw before Then you start at any given color like light blue And you start to add more and more chlorophynical and you parametrically move up a line of negative slope So this was now something that I had called fireman And what happens as you add antibiotic is something more or less like this The intercept is not as clean as I've got it here but we'll come to that Makes sense and so this is with chlorophynical I'll call it CM And this is nutrient So the better the nutrient the faster you grow The more the chlorophynical the slower you grow And then we have some intercept here And so what I want to suggest is that We now have a secondary relationship that has some at least superficial resemblance to that night heart and mega sanic relationship We end up with a family Lines that again So we end up with now a negative correlation between the ribosome abundance and the growth rate But that correlation is so strong that again we can compress the data more or less into a line For each growth medium Let's say nutrient condition So we now look at the ribosomal protein fraction It's going to be negatively related to the growth rate And the slope now I'm going to denote by kappa n Well one over kappa n For now we don't know what that means It's just an empirical fit You tell me the growth rate that you started with You say dark green I say okay kappa n is 4.6 It's just a fit And then we have some intercept Which I'll call fire max For symmetry So this now is fire max Now you can see there's some spread in the fire max And in fact there's some weak growth dependence in that intercept But to a 0th order approximation Probably first order approximation We've got more or less growth independent intercept And then a slope that depends upon where you started from Because of the growth independence in this intercept And so we have this second relationship Where this guy appears to correlate What I'm going to call nutrient quality Which is not a quantitative term Okay and then this is some approximately growth rate dependent intercept And again that's an empirical fit Or if you like a family of empirical fits And I've offered no interpretation to either of those parameters yet And if you like think about how you might rationalize them And if you feel even more ambitious Think about how you would test that rationalization independently of this data All right let me pause Does everybody see what I've done? So initially in the previous picture what I had was mutants Genetically altered different species if you like of bacteria Well I'm not really different species There's still E. coli but I've gone in there with a screwdriver And ruined their genetics Okay that gave me a discreet march with different slopes But I can get a continuum sampling of that same process by adding an antibiotic Now you notice that for example all the twos don't really nicely line up on the same line But there's no reason that they should I mean it's not apillary necessary that the bacterium will perceive the same concentration as the same amount of inhibition inside For genetics it should but for chemicals it doesn't need to Well that's fine because we're using this as a parametric variable if you like We're twisting the concentration of antibiotic and that scoots us up and down this colored line So another way to think of this then is that we've got this space Ribosomal protein fraction and growth rate Tiled not by vertical and horizontal lines But by these overlapping diagonals So we've got one parameter that gives you the kappa t's And then we have one parameter that gives you the kappa n's So it's like a change of coordinates If you want to think of it that way But what I want to talk about then after lunch So let's break for some questions and then we'll break for lunch Is the interpretation of this guy and this guy Okay but let me pause first Are there any questions about the experiment, the data, anything? Yeah, yeah, yeah So these we will talk about that on probably on Monday So the question is what about resistance to this antibiotic These, that's right, yeah So these empty circles have no intrinsic resistance to the antibiotic That is to say they don't have any proteins that confer much resistance You can find strains that do make proteins that, what do you call it Deactivate the antibiotic, so they enzymatically change it And we'll talk about what effect that has on the growth rate But for here these guys are susceptible They have no genetic predisposition to be resistant to these If we let this experiment go on for months or weeks or even hours Days, let's say You could start to see mutants emerge that were resistant to the antibiotic But this experiment is not done on those timescales It's only a few hours long Was that the worry that we see mutants? Any other questions? Yeah, good Yeah, so here all of the ribosomes are inactive in our interpretation If there were any, they would be making protein and cell would be growing Up there, there's a huge number of them They're all possibly active There's still some small offsets of inactive But they're not translating at any, they're translating at a zero rate So whether you want to call them inactive or active is sort of semantic But at least as you take that limit What you have is many, many more ribosomes than you typically would They're all active, but their protein production rate is very slow Tending towards zero So here what we're turning, so if our synthesis rate is I guess I'll erase these guys Exactly, so if this is lambda m which is this k times n ribosomes Active, say The bottom one is when this goes to zero The top one is when this goes to zero Is that okay? That would be the interpretation Any other questions? It's okay? That's our timeline, good Okay, so why don't we break for lunch and I'll see you guys at 2.30 or something I think, yeah, did everyone who wanted to sign this sign this? Okay, alright, I'll see you guys after lunch