nanoHUB-U Principles of Nanobiosensors/Lecture 1.2: Nanobiosensors Introductory Concepts: Biomolecules
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[Slide 1] Welcome back. In the last lecture, in the introductory lecture, I explained to you that if you could detect biomolecules, or, at the ultra-low concentration, very small biomolecules by these nanobiosensors, then that will enable a wide range of applications. Like personalized medicine, sort of integrating things with mobile devices, like iPhone and so on, so forth. So you must be wondering, what type of biomolecule are we really talking about? So I want to give you a flavor of that.
[Slide 2] And these would be the actors that we would be considering. So I will very briefly explain, to give you a cartoon picture of how a biosensor works, so that you have the big picture within which you can put each individual biomolecules. We'll be talking about three types of biomolecules. In particular, DNA, protein, and viruses. And then a very important thing in here is how to calculate numbers. Because you see, it is one thing to know how things work, sort of. But it's very different there is to know how to calculate numbers precisely. And to get to the conclusions in a quantitative way. And then I will end by talking about diffusion and capture of biomolecules.
[Slide 3] And summarize and conclude. So this is sort of a cartoon view of what a biosensing system might look like. What I have here is a sensor on the bottom, on the red. And the biomolecule T in blue, is sort of sitting there. And of course it will diffuse around. It will diffuse around, and eventually after a while, it will land on the sensors surface. And once it lands on the sensors surface, depending on the mass or its charge, or some other intrinsic physical characteristics, the sensors -- if it's sensitive to that characteristic then it will respond. And then we'll say a molecule has been detected. Now there are several things about this box. For example, this box is a conceptual box. It could be a beaker. You could be putting a sensor on the bottom of a beaker. Or it could be a small droplet of water. For example, a teaspoon full of sea water may contain millions of these target molecules. Or it could be a droplet of blood. Or, in general, this box could also be a cell within which a protein molecule is moving around and getting latched onto another protein. Or one of the escape routes. For example, the ion channels that it would go through. So therefore, this is a very generic system that we'll be considering as a framework of our discussion, but I want to get started with the molecule itself.
[Slide 4] That target molecule T. Now we'll be thinking about three types of molecules. One molecule will be very small molecules like glucose and nitric oxide. For example, glucose, of course, all of you know, if you have a relative with -- older relative that is related to diabetes. And then nitric oxide has been -- nitric oxide has been sort of correlated to many diseases. But in particular to Parkinson's for example. The second type of molecules that we'll be thinking about are DNA and proteins. These are polymers. And third are the invaders. Which come from outside; make us sick. Virus, bacteria, and even chemicals like DDT. Which disrupt cell function. And therefore can be very bad for the ecology and for the environment. So three types of molecules. Let me quickly tell you a little bit about each one of those molecules. Now the thing that we'll be primarily be focusing on are the charges associated with this molecule. Or the mass or the potential -- the redox potential oxidation. Reduction potential. Or even optical index, depending on the sensing scheme we have. We'll not go into the very -- the significant detail in each one of these molecules. We'll only focus on these key characteristics. Because for electronic biosensing, these are the things that we detect. Not the entire structure, it's not really relevant or important in many of the discussions.
[Slide 5] So just to give you an idea about how things are. How small and big. What small and big mean in these particular structures. For example, glucose molecules will be about a nanometer. The protein molecule that we'll be talking about in the order of 10, 20 nanometers. Viruses and bacteria somewhat larger. A virus could be 100 nanometers, and the bacteria about a micron or so -- a couple of microns or so. Of course there are other molecules which are much bigger. We'll not be considering that. Smaller molecules, also is not something that we'll discuss. So let's discuss a little bit more about these molecules, biomolecules.
[Slide 6] The first one is glucose. As you know, the formula is pretty simple. Many of you may remember from your high school or college courses, that C6, H12, and O6, and essentially there is a carbon backbone, all the white atoms are hydrogen. Realitively light-weight molecule, it's 180 kilograms per mole. So a Avegadro's number. six times ten to the power 23 number of molecules contains about 180 grams of glucose. And the size as I mentioned before, is one nanometer. The important point here for this molecule, is that they are too small to be detected by a mass based sensor. Let's say cantilever sensor, it's very small. And the second is it doesn't carry much charge. This is all generally charged neutral. And so potentiometric sensors may not also work. The thing that works the best is amperometric sensors. And in fact, since 1960s most amperometric, glucose sensors have been based on being able to measure electron affinity through amperometric sensors.
[Slide 7] Now the second class of molecule, which is a little bit bigger, but also very, very important, are these DNA and protein. So DNA, as you can see, this is deoxyribonucleic acid. This is essentially a polymer chain. Each, there are, each one of these elements, there are four types of these elements. So there can be these diamonds, there are four types of diamonds. A, T, C, G for short. And essentially, when you string them together you have a DNA molecule. Now the size of the molecule that we'll be interested in is on the order of maybe 100 to 1000 units. Not very long. So, in fact, human DNA is 4 billion base pair long. But we'll chalking it off and only looking at segments which is 100 to 1000. So a relatively small molecule in that sense. Now each one of the diamonds, if you looked inside, if you looked inside a little bit, then you will see that there's a five carbon ring, five ring carbon sugar. And the base is this ATCG of this nitrogen molecule, nitrogen atom containing base. And there is also a phosphatidate of phosphoric acid. What I mean by that, is that this was really H3PO4 The hydrogen went away. Making it charged, positively, or negatively charged, because the electrons were left behind. And generally, these molecules don't line up next to each other in a linear fashion. But they are staggered. The sugar connects through the phosphoric acidity -- acid acidity of the next molecule, giving rise to this helical structure. Because this is shifted. So therefore, it keeps sort of going around. Bottom line, each one of them carries a charge on the order of one to two electrons. Electronic charges. So if you had, let's say, 300 long, 300 base pair long DNA. Then the charges will be 300q to 600q, somewhere in between. Depending on the pH condition that we'll discuss a little bit later. The mass is also significant. Almost 300 Dalton. Dalton is a unit of proton mass sort of atomic unit. And so therefore, if you had let's say, 100 of these elements, that would be 3000 Dalton. That's pretty massive. So you can actually measure it in mass based sensors, but also you can measure this in a charge-based potentiometric sensor.
[Slide 8] Next up is protein. Protein is again, a long-chain. Almost very similar to DNA. In fact, every three bases of the DNA creates one protein. And since there are four bases to choose from, so there might have been 64 combinations of protein. Instead, there are actually twenty. So there, you can think about it as if you have a necklace that has twenty different colored beads. And these are strung together. Once again, very similar structure. Each one of the diamonds is essentially a simple molecule with a carbon in the bottom, in the center. And they're in this residue. And depending on this molecule, this is a complex molecule shown here in the box. There are twenty different versions of R. And in the solution, when you put it, put the protein in the solution, let's say in the physiological environment, then some of these R's are positive. Some of them are negative. Some of them are neutral. Unlike DNA, you see DNA are all negative. Here, depending on the surrounding conditions, there can be positive or negative. And so depending on the protein that we have, depending on the protein that you have, for example, this is PSA, Prostate Specific Antigen Protein. In that case, it will have some combination of these 20 residues. And so, therefore, this can be positive or negative. Or may have zero charge, depending on the environment, aqueous environment around. So one should certainly be able to detect it by potentiometric system, potentiometric sensors. But also the mass is significant when you have a long string. let's say this one has 261 amino acids. Each one of them is an amino acid. 261 of them multiplied by 125 Dalton. That is the mass. And so you should be able to detect it at relatively low concentration using mass based sensors as well.
[Slide 9] Now there are many markers of interest. There
[inaudible] biomedical instrumentation. For example, the molecule that I just showed was PSA. It's the marker for prostate cancer. Any time a person has a heart attack, the first thing the doctors do when they arrive at the hospital is to look for this particular protein, Cardiac Troprin T, in order to see whether, how significant the heart attack was. There are other types of proteins associated with ionizing exposure. And this BRCA1 and BRCA2, this stands for breast cancer 1. And although breast cancer 2 is really a marker for ovarian cancer. Bottom line is identifying these proteins in a solution at very low concentration is extremely important. And therefore, there are many tests associated with each one of those protein molecules. And we will see some of them during the course.
[Slide 10] Next up, virus. This looks very bad. Looks like a dangerous pathogen, which is what it perhaps is. Now this virus is in one hierarchy up of the size of a modern transistor. Let's say if you had an iPhone with a 20 nanometer or 30 nanometer technology. Then this virus, the size of the virus, would fit nicely on top of one of those transistors in your iPhone. This outer coating is a protein that we just discussed in the long chains of this. And inside, there is this DNA. Now this combined structure is on the order of let's say, 100 nanometers. But this generally doesn't carry a significant amount of charge. Or stable amount of charge, so therefore, very difficult to use potentiometric sensors. But look at this. This is massive. So therefore, even a few viruses, let's say 10 to 100 or so, you may be able to detect it with cantilever based sensing. And so therefore, this is certainly an option to detect this type of quantities, this type of biomolecules. Of course you always have the option of breaking this apart and looking at the DNA or RNA directly. That gives you one option of identifying the viruses. The other thing you can do, is that although this is charged neutral, you can have a potentiometric sensor, which it has a certain amount of preexisting charge. And allow the viruses to come and displace some of the charges. And so therefore, whatever was the sensors characteristics in the beginning, based on the preexisting charge, will suddenly be changed now. Because some of them have been replaced by this charged neutral quantity. And thereby, although this is charge neutral, one may be able to detect it by potentiometric method. But of course there are other simpler methods to detect it.
[Slide 11] Next up, a little bit bigger, the size of the 1970s transistor is the bacteria. So if you open up the old computer, maybe you'll find something of this size. About 3 microns here. A much more complex structure. Don't worry, we don't have to know any of this structure. The bottom line is that this is a relatively heavy molecule which can be captured on a sensor surface. And the mass and the size will essentially allow us to separate it from other parasitic molecules. Now one interesting fact is that about a millimeter cube of soil, let's say, can have tens of millions of bacteria. In fact, one pound -- every pound of our bodyweight is made of bacteria itself. So it plays a large, significant role in the physiology or wellbeing of human beings. So it's a very important biomolecule that we should be thinking about.
[Slide 12] Very quick examples now. I told you about this three types of small, medium, and large biomolecules. Now let me tell you, give you an example about the density of small molecules. Because you should be comfortable with numbers. Let's say you just have taken 1,000 milligram headache medicine. How much would your concentration of this molecule would be in your blood? Easy calculation? You can see this formula is almost like a, the glucose formula is a little bit lighter. This has 151 kilograms, 151 grams of this Tylenol. You can just add up the molecular, the atomic weight of this. And 151 grams of it contains the Avogadro's number of molecules. So 1000 milligrams is like a gram. So that gives you, that will contain, four times ten to the power twenty-one molecules of Tylenol. And our blood is about 5 liters. And if you then convert it to per liter, then eight times ten to the twenty and as a fraction of the Avogadro's number, that gives you 1.32 millimolar. And if you remember, my blood test result from the last lecture, then you might remember that this concentration is very high. In fact, equal to some of the concentration of other molecules that we have in our system. So the entire -- the headache medicine will work in minutes, as it often does. Especially at this very high concentration.
[Slide 13] That's about small molecules. What about larger molecules? But before we get there, the important thing is about first sample volumes. It's very important to think about how big a volume do you need in order to be sure that you have the target molecule. Because no matter how sensitive your sensor is, if you don't have any molecule in your sample volume, then of course you're not going to detect anything. So for example, let's a cancerous organ is releasing at a very early stage of disease about 10,000 cells. And we want to know whether if we just went to the doctor, the doctor drew a centimeter cube of blood. Or did a finger prick. And then do a millimeter cube of blood, whether there's any chance that the doctor will be able to find the cancer cells. Let's say, can it be done? No matter how sensitive the sensor is, let's give the doctor the best technology he might possibly have. And the answer is that, the answer may surprise you. You see, first of all 1 centimeter cube of blood will contain two cells. And a millimeter cube of blood will contain almost nothing. And so therefore, there, statistically it's almost impossible that from a millimeter cube of blood, that you are going to pick up this disease. Not possible, because the chances of getting a cancer molecule in there is very small. But even if you had took a blood, about a centimeter cube of blood, then you see, then the poisson statistic says, that on average, if there is two, there's also probability that you may have 3, or you may have 1, or you may have nothing. So, in fact, there's a 13% of the time you can show that you will essentially, the sample will have no molecules, no cancerous cell at all. The doctor will say that you are good. You can go home. Which is a false negative. And, in fact whereas there was a problem, and if you could detect it early on, that would have helped. So sample volume is very important, regardless of your sensitivity of your sensor. Because first there should be molecules before you take the picture of it or by the sensor.
[Slide 14] Now let me tell a little bit, tell a little bit about, and thereby I will conclude, is about the molecules diffusing around. Because the molecule, biomolecules when you put in a solution, they don't stay put. Rather, they drift around; diffuse around, and eventually are caught by the sensor.
[Slide 15] Now the diffusion process is known as a random work. If you look at the biomolecule, it could be a protein or it could be a DNA. You'll see that if I had a microscope, and if I could somehow look at that molecule, the molecule would zig-zag around in the solution. And every molecule that you put will take a different path. Why do they zig-zag around like that? Because there are invisible water molecules around it, and this red molecule essentially is bumping off against this speed bumps in a random direction. And that gives it, reorienting it every time. So that gives it this Brownian motion. Now this Brownian motion is very important, because this tells us that although we cannot say exactly how far a molecule will go, a given molecule, let's say given molecule number one. How far will it go at the end of time T? But we can say on average, if you inject at the same point 1000 of these red molecules, each one of them will go a slightly different distance. But there is an average distance that it will go. And that is the statistical average. It's a very important number. Diffusion distance that we'll have to think about. But first of all, the statistical distribution of this diffusion is described by this diffusion equation. Here Rho is the probability of finding this red molecule at a distance R at a time T and D is the diffusion coefficient. If it gets scattered more by this molecule, because it's more viscous D will be more. If the red molecule is bigger, let's say a protein compared to a glucose molecule, the D will be smaller. And so the diffusion equation tells us that how this molecule on average will drift around. Now if you have forgotten the diffusion equation, I will have an appendix later on, in which I will derive it. For the time being, I hope, you can accept the equation as is, and we can move on.
[Slide 16] So let me tell you how far will it move. So the diffusion equation the way I have set up, this is the original diffusion equation. And I'm assuming, just like a drop of ink in water, at a given point A, I have put it in. In this one-dimensional space as a delta function. And, which is the density at time T equal zero when you started. Was, everything was concentrated at one point. Then, if you come a little bit later, where will the particles be? Of course they'll not be here anymore. They'll be walking around. And after a time, T, essentially they will diffuse around. They will diffuse symmetrically in both directions, and this is the solution. N divided by 4 pi Dt, e to the power x squared over 4Dt. This one is in one dimension. If you had in two or three, this square root would be replaced by the corresponding power. And if you looked at the average displacement of how far it has gone, you will see on average, the square of the displacement is 2Dt. And as a result, the molecules essentially diffuse approximately square root of Dt how, from the center after the time, T. Depends on the diffusion coefficient of course.
[Slide 18] But in general, that average number is what we are looking for. So that sort of concludes my basic introduction to biomolecules. So I mentioned to you, that of course we want to detect a range of biomolecules to enable nanotechnology, nano biotechnology. And these biomolecules include from glucose all the way to bacteria for various diseases. Now as I mentioned, the typical sensors detect millimolar. That's what's in my blood test. That's what's in the headache medicine. And so therefore, this is a relatively large number. The promise is that even when the concentration is orders of magnitudes smaller, this nano biosensor, the one that we have not discussed yet. We'll begin to discuss in the next class, that can detect femtomolar concentration. Can detect at a very early stage of the disease progress, progression. Of course, the sample volume must be large enough so that it has that statistically meaningful number of molecules. And then, I started talking about how the molecules walk around. And the bottom line was, it follows on average, the diffusion equation. And the diffusion, it goes about the distance of square root of Dt on average, for within a time, T. If the molecule is big, D is small. If the molecule is viscous, D is small. So for each molecule, there is a diffusion coefficient. And therefore it allows you to calculate how far the molecule will go once you have put it some place at time T = 0. That's it. So in the next class, I will talk about the next piece, which is the sensors themselves. Because now the biomolecules are moving. They can come and get captured by the sensor surface. So we'll talk about the sensors next time. Take care. Thanks.