 So, if you take a look around the room, you'll notice something. You'll notice that we all look a little bit different. Inside, however, our genomic information, our DNA, is actually pretty much the same. We do have another thing in common, however, unfortunately, that is that we all get sick. How sickness manifests in us is different for each of us. It can be a scratchy throat, a runny nose, or a fever. These can be small changes if your illness is minor, or they can be major permanent changes if your illness is something major, like cancer. The changes that are happening inside of our bodies can happen well before symptoms occur. That really is the focus of my research program, to try to figure out how to identify and measure these changes inside of our bodies before sickness manifests. We have something that helps us in this quest, and that is biomarkers. We try to look at biological molecules, these can be DNA, or these can be proteins, to try to understand a specific signature for a specific state of illness. This is no small task. In fact, I would argue in some sense that this is really a grand challenge. We have some huge advances that have taken place with looking at genomic information, or DNA. DNA, I would argue, is something like a blueprint, as you see in the slide here. But if we want to look at what happens inside of a life process, we have these dynamic processes. We want to look at the machines that are building and modifying and zipping around the architectures, the blueprint outlines. These are proteins. Proteins are dynamic, which on the one hand is fantastic, because they may be more reflective of current disease states. So they may be great biomarkers of disease. On the other hand, the challenge is daunting. Because proteins are dynamic, they are modified and there are millions of them. So we try to figure out which of these specific sets of proteins holds information about a specific disease state for a specific individual at a specific time. So put another way as a bioengineer, what I seek to do is to try to understand how to bring the power of science, technology, engineering, and math to be able to make molecular measurements across a wide range of cells, cells that vary, they're heterogeneous, and to try to understand how that informs diagnostics. As I said in some sense, this is a grand challenge, but in another way I will say this is actually a tiny challenge. So what you see here is a micrograph of a single cell that is sitting on the head of a pin. So the question you might ask is, this proposal to measure the protein contents in a single cell, how do we do that with something that is so tiny and so inaccessible? In the case of our work, what we do is we create these tiny little cups, these little wells that hold individual cells. What you're looking at here are two cells in a confocal microscopy image that are seated in one of these little cups. The cups are made out of a polymer, something similar to Jell-O, but the diameter of this well is one-third the size of a human hair. How can we make that? We can make it actually with something that you brought with you today or maybe you're using right now, the cell phones and tablets that you have in your hands, the advanced manufacturing process that allow you to use those to route electrons, allow us to route biomolecules and manipulate these single cells. So it really is the power of these new manufacturing processes that allows us to make something like these microfluidic devices. So essentially you're looking here at a microscope slide that has a thin layer of this Jell-O, this hydrogel on top of it, with thousands of single cells. Because we don't want to look at just one cell, we want to look at thousands of cells. So I love to talk about measurements, but I also love to talk about chocolate. And I think this really lends into some of the challenges that we face. My colleague is gonna talk a little bit about looking at the surface of a cell. What we're interested in is after she looks at the surface, looking at the inside. Because the inside also can be quite different, even when the surface is the same. That's important when you're looking at disease, and it's also important if you don't like coconut in your chocolate. And so what does that mean? For us, inside, there's logic. Just like you see in an integrated circuit, what's happening inside of your cell phones and your tablets. But inside the surface of the cell is this cascade of signaling. It's a protein signaling pathway. Information is transmitted by those proteins and modifications on those proteins. So what we need to do is to crack open the cells to look at the inside. And this is where physics is actually not so much our friend. Much like a drop of ink in a cup of water or in a large swimming pool. As soon as we crack that cell open, that concentrated protein sample is diluted by diffusion. A random thermal process. And that means if we don't act fast enough, the signal will be so low that we can't measure it. So again, we use these microfabricated devices. These little wells that are cups that hold the single cells. And the very, very fast time scales that are accessible in microfabricated devices to separate out the two proteins in this example here. You can see that at the bottom of the image. We're interested not necessarily in looking at just a single cell, as I've shown you in the past example, but data from hundreds of cells. And in fact, we do this quite often for thousands of cells. We don't want to look at just one cell, we want to look at thousands. We don't want to look at one protein, we want to look at many. Here I just show you two proteins that are being analyzed from those hundreds of cells. With this, it allows us to establish what's different in the interior signaling between these cells that may look the same on the outside. And this for us allows us to get a view of what's happening at the molecular level, the cellular level, the tissue level to the whole organism. And in this way, we seek to chart a map of a disease state in a particular individual. Actually, I would say not a map. It would be something more dynamic, like an up to the day traffic report, about what's happening to those molecular machines in a given individual. And so our goal, and the goal of my colleagues as well, is to understand the variation within us, to understand the variation among us. And if you look around this room, I think you would agree that one thing we're all interested in is moving from a guess and check method of medicine to something that's more precise for us, those we care about, and the world as well. Thank you.