I want to tell the story of a beautiful phenomenon in biology. In some\nsense it’s the prototype of much of the activity of life. The\nphenomenon is the way in which an individual cell of\nE. coli forages for nutrients. This process, known as\n“chemotaxis”—the “chemo-” for chemical and the “taxis” from the Greek\nτάξις, for tactics—is intelligence in one of\nits most elemental forms. An individual E. coli has no brain,\nobviously, and is even many orders of magnitude simpler than a human\ncell, and yet already it possesses something like a sense of smell,\ndrive, even a memory. Chemotaxis recasts E. coli not as some\naimless gut-pest but rather as an exquisitely sophisticated\nphysical computer.
\nI’m also telling this story because\nI never liked the way biology was taught in high school. It was too much about the names of things. A subject so vast is\nspoiled by a textbook, which can only point at the endless parade of\nstuff-there-is-to-know. It’s better approached with questions—like\n“what’s happening when you smell?” or ”what is a fever,\nactually?”—that contemplate narrow, deep slices.
\nChemotaxis is a great slice: it’s a triumph of systems biology—we\nunderstand it holistically but also in fine detail at almost every\nlevel. It acquaints you with many of the most important motifs in\nbiology, including the way in which protein structure determines\nfunction; how membranes control the information flow into cells; and\nhow chemical modifications store and communicate state. It involves\none of the most sophisticated and beautiful pieces of molecular\nnanotechnology, the flagellar motor. And it helps give an intuition\nfor how a bag of unthinking chemicals could possibly give rise to a\nbeing.
\nEven with simple rules, the E. coli finds food more often than not
\nThe basic idea is this: E. coli “smells” chemicals it’s\nattracted to with a set of nose-like receptors and decides how to\nswim. Depending on what it senses, it can either use its flagellar\ntails to swim forward—this is known as a “run”—or it can spin in a\nrandom direction (a “tumble”). By running when the getting is good and\ntumbling when it isn’t, the E. coli takes a meandering path\ntoward the attractant.
\nA little more detail now: there are half a dozen or so rotors on the\nE. coli’s body, each controlling a long whip-like tail that\nflows behind it. When all the rotors are spinning in the same\ndirection, the tails join together into a coil that torques the cell\nforward into a run. When even one rotor is spinning against the\nothers, the coil unbundles and E. coli spins into a tumble.
\nIn a uniform chemical environment, the E. coli swims in a\nrandom walk by balancing runs with periodic tumbles. By default, a run\nlasts about a second, or ten times longer than a tumble. The rate of\nruns versus tumbles, and their relative duration, is carefully tuned\nto balance “exploitation” and “exploration”: if runs happened too\noften or lasted too long, E. coli would range too widely and\nzip past its food; too seldom or too short, and it’d likely never find\nfood in the first place.
\nBut how is this balance achieved? The crux of it is a signaling\nmolecule called CheY (pronounced “KEY-why”). CheY is constantly\nbouncing around in the cytoplasm of the E. coli, interacting\nwith both the receptor complex (the “nose”) and the rotors, carrying\ninformation between them. In the steady state, when CheY encounters\nthe receptor complex, it gets chemically modified, or\n“phosphorylated,” at a certain rate to become CheY-p. Unlike the\nunmodified version, CheY-p has a strong affinity for the rotors, and\nwhen enough copies bind to one, it reverses its spin, causing a\ntumble.
\nThe trick is that when the nose detects an increase in the\nconcentration of attractant, that steady turning of CheYs into CheY-ps\nis interrupted. As a result, fewer CheY-ps bind to the rotors; fewer\nreversals take place; and so the E. coli runs more and\ntumbles less. In other words, the all-important relative rate of runs\nversus tumbles is determined entirely by how often the\nCheY→CheY-p process churns—and this, in turn, is determined by\nhow much attractant is detected by the nose.
\nYou can see this process in action in the interactive illustration.\nTry altering the ratio of CheY-p (white) to CheY (blue) by adding some\nattractant (pink). You‘ll end up inducing a long run.
\nLess attractant
\nMore attractant
\nWhy do you need all this complexity? You could imagine a system in\nwhich the motors themselves responded directly to attractant. We’ll\nsee later on that the stream of CheY-ps acts as a kind of adaptable,\ntunable chemical amplifier. “Bacterial cells can amplify\nsignals more than 50-fold; that is to say, a 2% change in receptor\noccupancy can bring about a 100% change in the output of the system at\nthe flagellar motors. This feature allows cells to sense minute\nchanges in concentration—less than three molecules per cell volume!”
\nIf the system were as described above, then\nE. coli wouldn’t have much dynamic range. Imagine: if the\ncell has a huge reaction to just three molecules of attractant,\nwouldn’t a thousand times as many just completely overwhelm it?
\nIn reality, the E. coli is able to respond sensitively across\nfive orders of magnitude\nof attractant concentration. The cell learns to treat whatever\nconcentration it stumbles into as the new normal, so that the\nslightest increase triggers the same hypersensitive response as\nalways.
\nThe mechanism powering this adaptation is extremely clever. You can\nthink of each receptor as being equipped with “struts” that have\npockets in them. When the receptor is bound to attractant, its struts\nchange shape so that these pockets open up, and become the targets for\nlittle molecules known as methyl groups. Methyl groups are ubiquitous\nin biochemistry: for instance, they help determine which parts of your\nDNA get expressed. Methyl groups bind to the structural proteins your\nDNA strands coil around, called histones; the “methylated” histone can\nkink the DNA strand into or out of view of your transcription\nmachinery, turning it on or off.
\nIn this case, methylation serves to fill up the strut’s pockets,\ncausing it to become more rigid. (I’m simplifying the actual physical\ndetails somewhat, as we’ll see later.) With more rigid struts the\nreceptor’s signaling power is dampened: it takes more attractant to\nelicit the same response. Because there are many methylation sites per\nstrut and many struts per receptor, there’s a wide range of possible\ndampening values—as if those pockets were really the holes of an\nelaborate wind instrument.\n[Bray]\nThis wide dynamic range is what allows the bacteria not just to find a\nfavorable environment but to keenly and speedily nose its way up a\nchemical gradient. No wonder a similar mechanism is used by cells in\nyour immune system to track and hunt down invaders.
\nMethylation of the receptors gives E. coli a “simple chemical memory.” This is a powerful and somewhat profound idea: individual bacteria\ncan model their environment and remember important features of it by\nencoding that information in internal chemical modifications.\nE. coli “knows” whether attractant has become more or less\nconcentrated in its surroundings going back\nseveral seconds; that helps it determine whether it’s swimming in a good or bad\ndirection. Which is not that different in principle from what brains\ndo. In fact one reason that it requires an artificial neural network\nof about a\nthousand\nelements just to model the computational capabilities of a single real\nneuron is that the real neuron stores so much “state” in its internal\nchemistry.
\n(Here‘s an aside: should we be surprised at how resilient people can\nbe, given the mechanisms available to a single cell for accepting\npreviously extreme conditions as “a new normal”? No doubt our macro\nresilience is in some cases actually underwritten by similar cellular\nmechanisms.)
\nThe video above is a very legible overview of\nE. coli chemotaxis, from a popular textbook. It layers in\neven more detail, including not just the proteins that phosphorylate\nCheY but those that dephosphorylate it; and not just the proteins that\nmethylate the receptors but those that demethylate it. What you come\nto see is that these doers and undoers define a sort of equilibrated\ncircuit whose activity can be conveniently dialed up or down.
\nDennis Bray describes these sorts of circuits nicely in his book,\nWetware: A Computer in Every Living Cell:
\n\nIn a typical signaling pathway, proteins are continually being\nmodified and demodified. Kinases and phosphatases work ceaselessly\nlike ants in a nest, adding phosphate groups to proteins and\nremoving them again. It seems a pointless exercise, especially when\nyou consider that each cycle of addition and removal costs the cell\none molecule of ATP—one unit of precious energy. Indeed, cyclic\nreactions of this kind were originally labeled “futile.” But the\nadjective is misleading. The addition of phosphate groups to\nproteins is the single most common reaction in cells and underpins a\nlarge proportion of the computations they perform. Far from being\nfutile, this cyclic reaction provides the cell with an essential\nresource: a flexible and rapidly tunable device.
\nIf the cell really needs to change the concentration of the modified\nprotein very quickly, it can. All it has to do is to switch on or\nshut off the phosphate-adding reaction and the concentration will\nfall precipitously—at the speed of the spinning cycle. There is no\nbuildup of products or depletion of substrates to slow down the\nprocess, as there would be in a linear chain of enzyme reactions.
This is a clever way to regulate the level of some protein or\nmetabolite. Rather than producing the thing you want via a lengthy\nchain reaction, you just have this running cycle that activates and\nthen de-activates it, for example via phosphorylation and\nde-phosphorylation. When you want more of the active version, you just\ntamp down the de-activating reaction in the cycle, as if sliding down\nthe volume on a stereo.
\nRegulation in this manner via phosphorylation and dephosphorylation\n(by “kinases” and “phosphatases” respectively) is an extremely general\nfeature of life. “About 30–50% of human proteins contain covalently\nattached phosphate. [. . .] A typical mammalian cell makes use of\nhundreds of distinct types of protein kinases at any moment.”\n[Alberts]
\nIn the interactive figure above, phosphorylation is represented by the\nblue dots becoming white, and de-phosphorylation happens when they\nturn blue again. This cycle is constantly running. The speed of the\ncycle determines how quickly the cell can react to levels of\nattractant. Notice that when you add some, the blue→white\nreaction stops happening as much. But the blue←white reaction\nkeeps going at the same rate. So blue CheY proliferates, and the cell\nruns more. (If the cycle spun more slowly, the blues wouldn‘t take\nover so quickly.)
\nOne thing I don’t love in presentations of chemotaxis—and of\nbiological concepts generally—is that they often prominently feature\nflowcharts and network diagrams. In the case of chemotaxis, as you can\ngather from the video above, there are many players with nearly\nindistinguishable names: CheA phosphorylates CheY to become CheY-p,\nand CheZ dephosphorylates it back to CheY; CheW couples CheA to the\nreceptors, and CheR methylates those receptors’ struts; CheB,\nmeanwhile, “clips off” the methyl groups added to the struts by CheR.\nA network diagram is no doubt useful for organizing this sea of names\nbut in a sense it foregrounds the most abstract view of the process.\nI’d rather try to get a sense of the parts as a living whole or in\ntheir individual physical detail.
\nWhen you do that, it’s amazing what you find.
\nAlmost every action in a cell depends on proteins changing shape and\nbinding to each other. It’s no different in the\nE. coli receptor complex.
\nThe way it works is that there are stimulus-specific proteins embedded\nin the E. coli’s cell membrane, protruding into what’s known\nas the periplasm. These proteins are “stimulus-specific” in the\nliteral sense that they are shaped so as to bind favorably with\nindividual molecules of attractant. E. coli has five or six\nof these, for instance one that detects a crucial amino acid called\naspartate. This sensor protein has little clefts in it that are shaped\njust so for molecules of aspartate to fit snugly into them.\n[Falke]\nIn schematic form the aspartate receptor looks like this:
\n\nYou can see that the sensory part—up top, where the aspartate binds—is\nconnected to the signaling proteins CheW and CheA by a columnar\nstructure that straddles the cell’s membrane. What does this protein\ncomplex “actually” look like?
\nAn individual protein is small enough—like a few nanometers wide—that it can’t really be seen through a regular light microscope.\nThis receptor from top to bottom measures about 350 angstroms, or 35\nnanometers. But modern biology is all about seeing the unseeable.\nNowadays, we try to find out what nanostructures look like by\nX-ray diffraction\nor, more and more often, by\ncryo-freezing them\nin an electron microscope. Once we determine a protein’s structure\nit’s usually rendered using ribbon diagrams, a style\ninvented by the biochemist Jane Richardson\nin the late 1970s. Here’s a ribbon diagram for the\nE. coli serine receptor (really it’s a “trimer of dimers,” or\na complex of six receptors):
\nSource:\nKeith Cassidy
\nThis whole thing is the receptor. (Those parts just inside the\nmembrane, with little yellow methyl groups lingering stuck to them,\nare the struts.) How exactly it works is quite complex, and the\nsubject of current research. But in simplified terms it acts like one\nbig piston: when the asparate binds to the part in the periplasm, the\ncolumnar structure it’s attached to changes shape—a real biologist\nwould call these subtle allosteric effects; to me it looks like\ndipping and tilting—in such a way to lock the thing that’s supposed to\nbe phosphorylating CheY, the CheA kinase, into an inactive state.
\nAdapted from:\nCheA conformation change, posted by Keith Cassidy
\nWhen I think of a cell I imagine a Rube Goldberg–type contraption\nwhere an arm swings here, which drops a ball into a slide there, which\nrolls down and opens a trap door, which… eventually turns on or off\nsome important cellular function. Indeed, E. coli’s “sense of\nsmell” rests ultimately in a series of physical lock-and-key\nmechanisms, starting with literal molecules of e.g. aspartate nuzzling\ninto a protein and transmitting that physical shape-change across the\nmembrane.
\nThis piston-shaped receptor complex is just one of a huge array\narranged near the front of E. coli’s body. In cross section\nthey appear almost to have been laid down through a lithography\nprocess, in a neat hexagonal pattern:
\nSource:\nTheoretical and Computational Biophysics Group, UIUC
\nSource:\nDirect visualization of Escherichia coli chemotaxis receptor\narrays using cryo-electron microscopy
\nCalling E. coli’s receptor complex its “nose” is no mere\nmetaphor. Our own noses operate on a similar principle: when you smell\na flower, it means that actual flower-molecules—possibly only a tiny\nnumber of them—have reached the inside of your nose and bound to some\nprotein with a specific affinity for that very molecule. This signal\nis then transmitted via nerves to your brain. The human nose has\nseveral hundred receptor proteins for smell; a dog has\nmore than a thousand.
\nEvery one of our senses works like this. Touch is underwritten by\nproteins that get “squished” by tactile forces into cell membranes,\ntriggering a set of downstream responses. Sight is my favorite\nexample. There’s a protein called opsin that lives in the cells of our\nretina. What’s so cool about it is that the thing that changes its\nshape is a literal photon. That is,\nopsin converts the electromagnetic force of an incoming photon into\na biomechanical / biochemical signal. This is why I tend to think of molecular biology as the science of\nshapes bumping into each other.
\nI think of E. coli’s receptor complex as a protoversion of\nour own sensory apparatus. Its nose has only\nfive or six\nattractant-specific sensory proteins, but their signals are\nintegrated, as if different sets of receptor-protein activations were\nplaying different “chords” on the\nE. coli’s sensorium. “In short, the chemosensory array\nfunctions as an ultrasensitive, ultrastable biological integrated\ncircuit or sensory chip.”\n[Falke]
\nSo a bit of attractant binds one of the receptors, and lo, the\nequilibrium inside the cell begins to shift. Because the CheA kinase\nis now inactive, CheYs are no longer getting phosphorylated as\nquickly; the process that de-phosphorylates existing CheY-ps\nstarts winning out. Recall that this is a response that is dynamic, a\nflow that is tuned. The net number of CheY-ps in the cell is carefully\nfaded down. And then what?
\nThe CheY-ps had been binding to the flagellar rotors, flipping them,\ncausing tumbles. That now no longer happens as much, because the\nunphosphorylated CheY doesn’t have the same affinity for the rotor as\nCheY-p. As a result, the cell tumbles less, runs more, and biases its\nrandom walk toward the attractant.
\nThere’s something really important worth dwelling on here. When we say\nthat CheY-p has an “affinity” for the rotor protein, it’s not like it\ngets directed there; nor does it have some long-acting magnetic\nattraction for it. What this really means is that it has a strong\ninclination to bind to the rotor protein when it gets really really\nclose to it. (And CheY, without the -p, doesn't have such an\ninclination.) Given how small a single CheY-p is in the scheme of the\nwhole cell’s cytoplasm, it might seem improbable that it’ll somehow\nsidle up right next to one of these rotor proteins somewhere on the\nother end of the E. coli’s body. But that gets at the heart\nof\nthe crazy kinetic chaos inside our cells.
\nSource: David Goodsell, The Machinery of Life
\nCells are dense with stuff, but everything in it is also\nextremely fast-moving:
\n\nTo get an idea of how fast this motion is, imagine a typical\nbacterial cell, and place an enzyme at one end and a sugar molecule\nat the other. They will bump around and wander through the whole\ncell, encountering many molecules along the way. On average, though,\nit will only take about a second for those two molecules to bump\ninto each other at least once. This is truly remarkable: this means\nthat any molecule in a typical bacterial cell, during its chaotic\njourney through the cell, will encounter almost every other molecule\nin a matter of seconds.\n[Goodsell]
Just to put this in perspective: imagine you took an\nE. coli cell and scaled it up so that it was the length of a\nfootball field. And imagine you kept all the physics the same. A water\nmolecule would be about an inch wide; a protein would be about the\nsize of a basketball.\n[BioNumbers]\nThe proteins would be juddering violently due to the thermal motion of\nthe water particles bombarding them—so violently in fact that if left\nunchecked they’d be moving at 500 meters per second. But they aren’t\nleft unchecked: if you were in such an environment it would be so\ncrowded as to be nearly impossible to see. What you really get, then,\nis an incredible ceaseless shaking and bouncing-into-each-other of all\nthe component parts.
\nThis is why shape changes that lead to different bonding affinities\nare so important in biology. It’s as if inside a cell everyone is\nconstantly going up to everyone else, seeing if they fit together.\nProteins sample the space of interactions with other proteins so\nquickly that for a long time, most biologists didn’t really\ncontemplate where in the cytoplasm two reactants lived; they knew that\nyou never had to wait too long for them to meet each other. In fact it\nwas a\nrelatively recent discovery\nthat inside the cytoplasm certain proteins that share functional\nrelationships do seem to keep especially close together, inside little\noil drops known as “phase-separated liquids.” Weak interactive forces\nbetween the floppy tails of different proteins cause them to\nspontaneously “phase separate” into these more viscous pools, and this\nbiases certain proteins to interact more frequently.
\nThe rate-limiting step in E. coli’s reaction to attractant is\nthe time it takes for CheY-p to diffuse from the nose to the motor. It\ntakes about a tenth of a second. The journey has actually been tracked\non camera, using a fluorescent version of the protein:
\nSource:\nSingle-molecule imaging of electroporated dye-labelled CheY in\nlive Escherichia coli
\nLet’s talk about these motors. These things are so intricate and\nbeautiful and seem so reminiscent of machines we’d engineer ourselves\nthat they’re sometimes cited as evidence for intelligent design.
\nThe flagellar motor operates with close to 100% energy efficiency. It\nspins at about 1,500 rotations per second. And the craziest part is\nthat like all molecular nanomachines it is entirely self-assembled.\nThere’s an amazing 30-minute documentary\navailable on YouTube\nthat details the mechanics of the self-assembly process—and,\nrefreshingly, profiles some of the scientists who figured it out,\ndescribing the methods they used to make their discoveries.
\nFrom:\nSelf-Assembling NanoMachine: a film about flagellar biosynthesis
\nMy favorite part of the self-assembly process is that after building a\nbase for the rotor, a sort of tunnel is built and the proteins that\ncomprise the whip-like “hook” of the flagellum are extruded through\nit—as if the flagellum were built by vomiting forth parts of itself.
\nAnyway, at the base of each rotor there are a series of proteins\ncalled FliG, FliM, and FliN—pronounced like “Fly G,” “Fly M,” “Fly\nN”—to which CheY-p, our Frodo-esque bearer of the message from the\nnose, attaches once it finally arrives. CheY-p has a strong affinity\nfor FliG and will readily glom onto it. We’ll see how that actually\naffects the flagellum in a second. But for now it’s worth noting that\nthere’s a thresholding mechanism here: just one CheY-p attaching to\nFliG won’t be enough to flip the motor from counter-clockwise to\nclockwise (thereby causing a tumble)—it actually takes a handful of\nCheY-ps conspiring to make that happen. In fact the motor has\nsomething like seven states, from rotating quickly counterclockwise at\nthree discrete levels of decreasing speed—as if stepping through three\ngears on a bike—to stalling entirely, to starting back up again in the\nclockwise direction, also with three speeds.
\nEven as the motor is in the process of changing direction, any CheY-ps\nthat do attach to FliG are under constant threat of being removed by\nyet another player, CheZ. That is, the proteins that would reverse the\nmotors are subject to removal by other proteins that un-reverse it.\nAgain we have a responsive regulatory circuit reminiscent of the one\nthat phosphorylated and de-phosphorylated CheY in the first place\nupstream at the receptor. The idea is that every effect is reversible,\nand in fact is reversed at a regular rate. This means that in the\nabsence of further signal the cell will quickly return to baseline.
\nAs a matter of pure mechanics this might be the most ingenious part of\nthe story. It took quite a long time to figure out and even still it\nseems that we’re not entirely confident with our explanation. But one\nmechanism that’s been proposed is that CheY-p binds to a protein\ncalled FliM (“Fly EM”) embedded in that ring that defines the base of\nthe rotor. This tilts it and causes a 90-degree rotation in an\nattached protein called FliGc. That protein sits at the interface\nbetween the rotating part of the motor and the so-called “stator,”\nwhich drives it from the part that’s anchored solidly in the cell\nmembrane.
\nSource:\nA molecular mechanism of direction switching in the flagellar\nmotor of Escherichia coli
\nWhen FliGc changes orientation, the stepper-motor-like cycle that\nnormally drives the motor counterclockwise starts driving it clockwise\ninstead. In the illustration below, Figure A shows the stator, i.e.,\nthe driving mechanism of the motor. It works by stepping back and\nforth between the “open” and “closed” states, schematized by the\nand\nsymbols respectively. In Figure B you can see how, in the normal CCW\ndirection, the repeated cycling between these two states drives the\n“teeth” of the motor—the crucial FliGc proteins, here tilted\nleft-to-right.
\nSource: Supplementary figure S9,\nA molecular mechanism of direction switching in the flagellar\nmotor of Escherichia coli
\nWhen the CheY-p arrives at the rotor it has the effect of flipping the\nFliGc proteins so that now they tilt right-to-left. In that\norientation the step-drive action works the opposite way, and the\nmotor rotates clockwise:
\nYou can see this more clearly below in the interactive version of\nthose figures. Click “Step” to drive the motor in one direction, and\n“Reverse” to flip the orientation of the FliGc proteins; Step again\nand you’ll see it run in the opposite direction.
\nReverse
\nStep
\nIt would be nice if the original paper presenting this theory included\nan illustration like this. But even this crude version took me many\nhours to make. As Bret Victor argues in\nStop Drawing Dead Fish, making moving pictures shouldn’t be so hard. If it were easier,\nsuch animations would spread everywhere in scientific communication,\nbecause so often what a paper describes is some kind of dynamic\nprocess.
\nDynamic illustrations would help readers grasp proposed mechanisms\nmore quickly. As it is, someone who understands a complex mechanism\nusually has to explain it in patient detail to someone\nelse who’s good at animating; this costs time and money; and\nmost people simply opt not to go through with it. Perhaps someday the\nprocess will be democratized by better tools, or by a multimodal AI\nsystem.
\nThe final part of the story—for me, anyway; there’s a lot more to\nexplore!—is why exactly the clockwise rotation of just one of the\nflagellar motors would send the whole cell a-tumbling. It helps to\nunderstand how the thing works in “run” mode, when all the flagella\nare oriented the same way.
\nTraced from:\nHow Escherichia coli Move | National Science Foundation
\nEven though this bundle of flagella sort of looks like a propeller,\nwhen you actually think about it, that’s not really what it is. It’s\nmore like a pig’s curly tail that spins with a whip-y sort of motion.\nHow exactly does that propel the entire cell? A wonderful book called\nRandom Walks in Biology gets into the physics in some detail:
\n\nSource: Howard C. Berg, Random Walks in Biology.
\n“Fig. 6.3. Analysis of viscous drag on two segments of a flagellar\nfilament moving slowly to the right and turning rapidly\ncounterclockwise. The velocity of each segment, *v*, is\ndecomposed into velocities normal and parallel to the segment,\nvn and vp, respectively. The segment shown on the left is moving upward in\nfront of the plane of the paper; the one shown on the right (denoted\nby primes) is moving downward behind the plane of the paper. The\nfrictional drags normal and parallel to each segment,\nFn and Fp, act in directions opposite to vn and\nvp, respectively. Note that their magnitudes are in the ratio\nFn/Fp = 2vn/vp. Fn and Fp are decomposed into\ncomponents normal and parallel to the helical axis,\nFΩ and Fv, respectively. FΩ and F'Ω act\nin opposite directions and form a couple that contributes to the\ntorque. Fv and F'v act in the\nsame direction and contribute to the thrust.”
To get a grip on things like this, it helps to have a model of some\nkind that you can hold in your hand. And actually the question of how\nseparate filaments running in phase near each other would come to\nbundle was explored nicely in\nthis paper. The authors used physical models of the flagella by wrapping hollow\nTygon tubes around a mandrel and filling them with epoxy. They then\nused a couple stepper motors to drive the counter-clockwise rotation.\n“The flow field generated by each helix tilts the other helix, causing\nthe helices to roll around each other and form a right-handed\nwrapping”:
\nFrom:\nMacro Scale Model E.Coli Flagella Bundling
\nWe tend to think of a colony of something like E. coli as an\nundifferentiated evil goo, each bacterium identical to its neighbors.\nBut people who’ve studied these organisms under the microscope observe\na surprising amount of individual personality.\nA 1976 Nature paper, “Non-genetic individuality: chance in the single cell,” explores\nvariation in the context of chemotaxis using strains of Salmonella and\nEnterobacter bacteria.
\nThe paper came out before the exact mechanism behind chemotactic\nregulation was well-understood; all the authors knew was that “control\nof tumbling can be rationalised as caused by changes in the levels of\na tumble regulator.” They hypothesized that although bacteria of the\nsame strain would all share the exact same DNA, there might be a\nrelatively small number of copies of that “tumble regulator,” and\nnatural variation in the transcription, translation, and destruction\nof these regulator proteins could account for differences in behavior.\nTheir experiments were mostly at the behavioral level. They observed\nhow different individuals—including those in a particularly “tumbly”\nmutant strain (I love that word)—reacted to environments with and\nwithout attractant, and found plenty of variance.
\nTheir theory was spot-on. We talked above about how\nE. coli adapt to higher and higher concentrations of\nattractant via a clever methylation mechanism. Well, it turns out that\nthe methylation of the receptor struts is governed by only about\n100 CheR proteins\nin the cell. The number of those proteins—along with CheB, which\nun-methylates the struts—determines the speed of the “futile cycle”\nthat reacts to changes in attractant concentration. That is, it\naffects how quickly the bacterium adapts when the concentration goes\nup and refracts when it goes down.\n[Gore\n1:06:30]\nBecause 100 copies of CheR is so extraordinarily tiny in the context\nof the full cell buzzing with something like ten million proteins,\nvariation by just a handful can have a relatively large effect on the\ncell’s behavior.\n[Gore\n1:13:20]\nThat helps account for why different E. coli with the exact\nsame genetic sequence will tumble and adapt at different frequencies.
\nRecent\nexperiments\nhave used fluorescent microscopy to quantify the individuality of\ndifferent E. coli cells, individuality that arises not from\ndifferences in gene expression but from the dynamics of signaling\nnetworks.
\nWe don’t yet have the technology to just observe all of the activity\ninside a living cell. That Goodsell painting above that shows the\ncrowded cytoplasm packed with proteins is an artistic composite—backed\nby rigorous research to be sure—because there’s no way to capture all\nthe different players in situ at once. And obviously it’s a “still\nlife,” not a video. So how could we possibly know all this detail\nabout what exactly a given protein looks like, and how and when it\ninteracts with others to kick off some particular part of the\nchemotaxis process?
\nThere seem to be three or four major kinds of experiment. Probably the\nmost common is genetic: you can selectively disrupt one gene at a time\nand, by observing how the mutant E. coli behaves, begin to\nget a grip on each gene’s function. All of the proteins “CheY,”\n“CheZ,” “CheW,” and so on are named simply because they are the\nproducts of genes that, when excised, “cause a general defect in\nchemotaxis.”\n[Blair] As you\ncan imagine, identifying all of these is painstaking work, and\ninvolves a considerable amount of clever inference. For instance you\nmight observe that without gene X the bacteria never seems to tumble;\nis that because that gene is involved in recognizing attractant or in\nforcing the rotor to run clockwise?
\nOnce you have a hypothesis, a second kind of experiment involves\npurifying some subset of these proteins-of-interest in vitro to see\nhow they work together to form a particular signaling pathway. For\nexample you could put CheA and CheY along with some phosphate groups\nand other necessary reactants and observe whether and how much\nphosphorylation takes place. That’s what the authors did in\nthis paper\nin Cell, in 1990. They used a radioactive version of\nphosphate as a tracer. “Incorporation of [32P]phosphate into CheA or\nCheY was determined by excising the radioactive band out of the dried\ngel and quantitating in scintillation fluid or by analysis of the\nintact gel using a Phosphorimager (Molecular Dynamics, Sunnyvale, CA)\nand compare with known radioactive standards.” Another common method\nfor observing in vitro dynamics is to genetically modify proteins to\nfluoresce; or to “find” a protein in solution using an antibody that\nrecognizes some part of it—you attach that antibody to another\nprotein, and that one you fluoresce, so you can find the hidden one.
\nTo understand the literal lock-and-key mechanics at a particular\nbinding site—for instance how exactly a molecule of aspartate causes a\nreceptor to deform, kicking off a signaling cascade—involves\n“structural” biology work, i.e., taking pictures of individual\nproteins or, increasingly, ensembles of them in situ. For this you can\nuse X-ray crystallography, nuclear magnetic resonance imaging,\ncryo-electron microscopy, super-resolution light microscopy, or some\ncombination.
\nA group at\nUniversity of Illinois at Urbana-Champagne uses atomic-scale molecular\ndynamics simulations, in software, to understand structural\ndetails—like the exact\nway that\nCheA changes shape to kick off a downstream signaling process—that\nwouldn’t be apparent from high-resolution imaging alone. (Keith\nCassidy, whose figures appeared above, now has a\nlab at the University of Missouri-Columbia\nthat’s studying the molecular dynamics of the receptor signaling\ncomplex.)
\nSometimes you can’t get a direct picture. It may require deduction to\nunderstand, say, how exactly a protein fits in.\nOne experiment\nfound that CheA didn’t bind to a receptor except in the presence of\nCheW; that plus the fact that adding too much CheW into the mixture\nactually led to a decrease in the ability of CheA–CheW complexes to\nbind receptor suggested that CheW competed with that complex for the\nbinding site on the receptor and that therefore it must sit between\nthe receptor and CheW in the receptor–CheW–CheA trimer\n[via Blair].\nBiology is lousy with heroic inferences like that. It’s a world that’s\nhard to see; sometimes you just have to imagine what’s going on down\nthere, and back up those imaginings with the right experiments.
\nThe very idea that bacteria run and tumble came from experiments\npublished in 1972\nby Howard Berg and Douglas Brown, who used a special three-dimensional\ntracking microscope of their own design to watch the little suckers in\naction. (A fun fact is that they called the non-runs “twiddles”\ninstead of “tumbles.”) Some of the physics of flagellar\npropulsion—like how much force the little tails generate—was discovered\nlater by tethering the flagella to a microscope slide: because it’s\nanchored, the “tail wags the dog“ and you can measure how fast the\nE. coli’s body spins. We know that bacterial flagellar motors\nare powered by the\nproton motive force\nfrom a\n1977 paper\nthat measured how cells ran or “twiddled” in the presence or absence\nof an electrical potential. But the research has become even more\nrefined than that. Just by observing the strength of the rotation\nunder various conditions—different viscosities, temperatures, and so\non—we know that “rotation is tightly coupled to proton flow, with a\nfixed number of protons (~500) used to drive each revolution.”\n[Blair]\nThink of how detailed an understanding we’ve gotten!
\nOne reason I’m particularly attracted to studies of\nE. coli chemotaxis is that it’s an early star of what’s been\ncalled “in silico” biology. It’s been the subject of many computer\nmodels. Dennis Bray, the author of that book that put me onto this\nstuff in the first place, made one of the more\npopular models. Here’s a nice screenshot of the model in action:
\nMaybe the chief role of a computer model is that to get it working in\nthe first place you have to explicitly articulate every one of your\nassumptions. In much the same way that writing tends to clarify your\nthinking (or at least reveal how unclear it really is), a computer\nmodel forces you to synthesize what you know. If anything it’s even\nmore exacting than a blank page.
\nOnce you have a model, you can use it to explore variations on those\nassumptions. “The program gives the correct phenotype of over 60\nmutants in which chemotaxis-pathway components are deleted or\noverexpressed,” Bray writes. At best, a good enough model lets you\ndiscover things you didn’t already know, or suggests your next\nexperiment. “In order to match the impulse response to a brief\nstimulus [. . .] we also had to increase the activities of the\nadaptational enzymes CheR and CheB at least an order of magnitude\ngreater than published values.”
\nWhy should you care about E. coli chemotaxis? A typical\nanswer to that sort of question—and I’m sure the answer given in many\nof the grant applications supporting the work cited here—is that there\nare medical and practical uses. For instance: if you understand the\nsignaling pathways of bacterial chemotaxis you can disrupt them; that\nwork might lead to a new kind of antibiotic, which, in an era of\nincreasing resistance, is direly needed. Or you might hijack\nchemotaxis pathways to create “intelligent sniffers” (Keith Cassidy’s\nphrase) that could home in on cancer cells or environmental waste.
\nMore generally you might say—and in fact I led with this up top—that\nunderstanding this specific phenomenon equips you to understand all\nkinds of others. “Bacterial two-component pathways\n[def’n]\ncontrol a dazzling array of functions including cell division,\nvirulence, antibiotic resistance, metabolite fixation and utilization,\nresponse to environmental stress, sporulation, and taxis.”
\nBut I don’t know, to me the real reason is that it’s neat. It’s just\nfun to find out about. “To learn, and at due times to repeat what one\nhas learnt, is that not after all a pleasure?”
\nPlease send feedback or corrections to James.
","headings":[{"level":3,"text":"The 30,000-foot view","id":"the-30-000-foot-view"},{"level":3,"text":"The story gets more complicated: adaptation","id":"the-story-gets-more-complicated-adaptation"},{"level":3,"text":"The full picture: a complex signaling network","id":"the-full-picture-a-complex-signaling-network"},{"level":3,"text":"Down the rabbit-hole…","id":"down-the-rabbit-hole"},{"level":3,"text":"What does it mean for a receptor to detect attractant?","id":"what-does-it-mean-for-a-receptor-to-detect-attractant"},{"level":3,"text":"How the signal is carried","id":"how-the-signal-is-carried"},{"level":3,"text":"What happens when the signal reaches the motor?","id":"what-happens-when-the-signal-reaches-the-motor"},{"level":3,"text":"How does the motor actually change directions?","id":"how-does-the-motor-actually-change-directions"},{"level":3,"text":"How the motor changing directions causes the *E. coli* to tumble","id":"how-the-motor-changing-directions-causes-the-e-coli-to-tumble"},{"level":3,"text":"Individuality in the bacterial population","id":"individuality-in-the-bacterial-population"},{"level":3,"text":"How did we figure all this stuff out?","id":"how-did-we-figure-all-this-stuff-out"},{"level":3,"text":"So what?","id":"so-what"}]}}![Keith Cassidy](\"https://ckcassidycom.files.wordpress.com/2015/12/full_trimer.jpg\"){kind=link}