 We know a lot about the biology of Huntington's disease, but we don't know a lot about the kind of computational approaches that IBM does. This is a disease that is largely about numbers. It's about CAG repeats within your DNA that cause the disease. And it's about numbers of prevalence and statistics about how the disease works. And so I think that the solution to this will come in numbers themselves. And that's where IBM can come in. Drug discovery is difficult in general. Neurodegenerative work has been even more difficult than the average drug discovery program. And sadly, so far in Huntington's, there's really, except for one symptomatic treatment, there's never been anything approved. At the end of the day, the major impediment to finding effective therapeutics for Huntington's is understanding the disease itself. It's a neurological disease caused by a single gene that evolves over a person's lifetime. It's in the brain, it's very, very complicated. And if the tools and techniques that IBM can bring to bear on the data that CHDI has been collecting for patients and from DNA and the molecular level, if they can help us better understand the biology of the disease, that will give us ideas for how to create effective interventions. The challenge in brain modeling is taking observations from across neuroscience made from many different structures, at many different scales, and integrating them into a single model, one which can be validated against observations that neuroscientists make at the single cell level, at the circuit level, at the tissue level, at the whole brain level, and ultimately at the behavioral level. We are using our ability to model, the computationally model, the structure of the protein, to not only manifest what might be the possible changes that are occurring in the structure due to the mutation, but then also ask the question, if these are the changes that happen to the shape of the protein, what difference would that make to the various interactions that this protein has with other proteins? We have the ability to build detailed models. We have the ability to do analyses of neuroimaging. We have tools like high-performance computing. We build some of the largest computers in the world. And biology is an extremely complex thing, right? Biological organisms are actually very, very complex. So it's necessary for us to actually bring the scale of computing and this aggregation of technology talent to do this in a systematic and highly, highly relevant manner. This is incredibly exciting and is really, I think, a turning point for this disease. There is, for the first time, hope in the future that we might be able to make a real dent in it.