 So I'm going to take the liberty of almost summing up the entire class this far. Remember how we start from DNA. And then we go from DNA to RNA first. We go from RNA to a polypeptide amino acid sequence. And we go from that to a beautiful folded protein. Kind of a different way of showing the central dogma of molecular biology. But what we have interchangeably done is that we've occasionally looked at this in computers. And another way of identifying that is that we say we do things in silico. Or with information or something. While the traditional way that you do this all the time, even in your sleep, is in vivo. In the cell. And it's interesting to compare these two processes because they're quite different. You might think that going from DNA to RNA is easy. Nothing could be further away from the truth. This is an exceptionally difficult problem. Here you needed the entire transcription polymerase. I know I touched upon it, but we so didn't look at all the binding modes, exactly how it works, the rate by width this happens. This is an insanely hard process, hard with capital letters. While I bet what you thought about is that in DNA you have AGCT. In RNA it's EC. You just have AGCU. So you probably thought of this as changing T2U. But that's what you do in a computer. Not in the cell. So the cool thing in the computer, this is a very easy problem, or at least in terms of information. When we go from RNA to the polypeptide, if the transcription was hard, this is translation in the ribosome, almost 60 protein chains. That's a much, much, much harder problem. It's insanely hard. While here, this is just the genetic code. Triplets. Also EC, if you remember them. So do you see the difference here? On a molecular level, these processes are super hard to understand, while it's super easy for us to understand in terms of information. So now I just need to write down the last part here, right? I'd argue that computers have this much easier. But here is where we lose. Because this requires a many body simulation. This is so hard that in general we can't do it. I've shown you a couple of cases where we've been able to use a molecular dynamic simulation to fold the protein. But in general, if you pick any protein at all, I would say that this is still impossible. So what do we do here? Well, this is not even easy. This is just the loss of physics, right? Which is trivial. Take that polypeptide, throw it in water. Boom, it folds, such as Christian Anfinsen predicted. So the thing is that they do a crossover here. EC, EC, EC. Hard, hard, hard. Ideally, we would like to combine this more. And we will combine this more. This is kind of why I'm going to go over to buy informatics. So the physics is important. It's just that that physics is difficult to describe in the computer. So we would like to find ways around this. And with bioinformatics, we occasionally can that by cheating a bit, by looking at what evolution has done. You are allowed to cheat if you check what evolution did, nor would your classmate did. But in principle, these are two sides of the same coin again. Life, we can see that either from a physics point of view or from an information point of view. And that's going to be a really powerful way of combining the concepts, which is why we will look at bioinformatics in the next lecture.