Video 2-1: Biology big picture

0:03 In the next chapter, we'll look generally at at h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h h messages uh in particular in in genomic sequences.

0:12 And there will be some some cute and maybe surprising and maybe insightful math about this. But before we get there, I want to set a bit more of the context. And so in a way this is what in this module maybe should have uh been in week one but uh I wanted to start with some computer sciency fun. Uh so let's catch up a bit on the background from biology. Uh I said I'm not assuming any biology knowledge for for this module. So I wanted to stay true to that and equip you with the little things that might be relevant or necessary to make make sense of the puzzles we're discussing. uh which is isn't isn't going to be a lot uh but I hope to peique your interest so that you dig a bit more into this just because it's it's interesting to know and uh there's so many uh exciting things to learn in science.

1:05 Biology is the study of living things. Uh I'm not going to get into the definition of what living means in this case is surprisingly controversial. I think the usual is it has some metabolism. It generates energy. It has some way of reproducing and it has some way of compartmentalization. There's some sort of uh stability over time. You don't just disintegrate uh into uh space. I'm not sure if that's if that's universally agreed. Uh especially for virus for viruses, this is uh excluding them from living things because they don't have a metabolism. Okay. um a little bit of the kind of evolution, the history of the field maybe better to say. Um I think biology had for a long time essentially consisted of people that were excited about nature, observing what happens in nature and writing these things down and drawing beautiful pictures and of course doing uh a lot of uh structuraliz structuring um life in in the way they could see it.

2:18 Um maybe I'm not doing them justice by saying that they didn't do controlled experiments to the extent that where uh biologists would do. Now uh an example if you want to read up about this are the Darvin Finch's uh specific uh the beaks of these birds show high variability and that's um reported to be an an uh Eureka moment or Darvin in his theory of evolution that he said uh these these peaks evolve to solve specific functions. Um okay, maybe this is a bit unfair, gradually more scientific, but uh people tended to use the scientific method um more systematically. Um a nice example with a German background is Mendle's inheritance theory. So this experiments with certain peace and he specifically tried to check how are certain traits inherited.

3:16 uh and then more and more things moved from what you can see with your naked eye to smaller and smaller things. It turns out life is complicated and um if you're trying to understand the mechanisms at the macro scale or at our scale uh it's essentially uh imperative to understand the lower levels first. uh a big thing for biology therefore was the development of microscopes first optical and then more refined versions and I guess it's it's now quite clear that uh the origin of life was microscopic and also that many of the fund me fundamental mechanisms can only be understood at this micro scale first and then you can try to scale up uh with all the complications that come from the complex systems uh that living things Sorry. Okay. Uh we'll try to dig a bit into this. Um the main thing that happened in the 20th century from my uh humble perspective maybe uh is that things moved much closer to computer science. Uh it was the discovery of of DNA and genes and in particular that things are much more discrete than maybe they first look. Uh I'll show this video a bit because it it nicely shows a bit of the structure of DNA.

4:38 um that I I can't possibly explain in the same same visual way. Uh but from a computer science perspective, you could you could say your genetic information stored in in DNA is one long string over these four bases. Um pretty much nailed down to this three billion letters. As always in biology, nothing's completely exactly written in stone and there's variations. Um but uh the rough number is clear so he can work out how many bits of information that is. Uh the somewhat depressing fact so is I don't know some 4 gigabytes roughly. Uh the more depressing fact is if you take the difference between two of us in the room and take your genetic information as the the encoding of your difference from your neighbor that's roughly 30 megabytes. So you could email it. Um, okay. Because the information is discrete, it's one of those four bases and not too much else. Uh, you can discreetly copy it and you can make precise copies.

5:45 That's what we love about digital data. And turns out life invented that much earlier than we did. And then mutations are what happens if there's mistakes in the copying among other things as always. Okay. uh for the structure of uh I'll come back to the um to this function but let's first have a look at um what the structure looks like.

6:17 Large organisms like humans are made up of organ systems such as the circulatory system and digestive system. These systems include a number of organs. >> Number of organs. >> One of them is the liver. >> Is the liver. >> We'll get there in a moment. >> They want to show us. And these organs are made up of tissues.

6:46 >> And these tissues are built of cells. Each specialized to perform a particular function. >> A particular function. Within each cell are structures and compartments called organels. >> Organels. >> These include the cell membrane. >> Mitochondria. >> You don't have to remember the names, right? Just [clears throat] say >> nucleus.

7:21 The nucleus contains 23 pairs of chromosomes, >> including a pair that determine your biological sexolog ical sex. >> Each chromosome is one long length of tightly coiled DNA. DNA.

7:43 >> Unwind it and we see the DNA is wrapped around proteins called histones. >> The DNA is a double helix like a twisted ladder. >> It is made up of four nucleotides. which contain different bases >> adinine, cytosine, guanine and thymine.

8:03 >> A, C, >> G, and T >> and T. >> A always pairs with T and C always pairs with G. >> The order or sequence of these bases in specific sections of the DNA called genes carries the instructions for a cell's structure and function structure and function.

8:27 One copy of the entire DNA instructions for an organism is known as the genome. >> The genome. >> The genome, organized in chromosomes within the nucleus, tells each specialized cell how to function with its neighbors. These make the tissues that together build organs, which work in organ systems and collectively enable an organism to grow and live. and live.

9:03 >> Yeah, it's made by uh your genome is a a British not not for profofit that makes some um they're funded by uh uh or they're working with the Sanger Institute on on bi bionformatics research. Some of the named after one of the first people that sequence DNA and they have this as part of their outreach program. I think the the visuals are are compelling. It's it's much nicer than I could possibly draw it. It's also somewhat scientifically accurate to my knowledge.

9:34 So you um you see there's more to it than just the abstraction of one long string. On the other hand, it is really one long string to a good degree of approximation. um in particular for working with it and the function of it. Uh that's often what we can do. Uh one one thing where we're lying is certainly that the chromosomes are not really connected. It's not one long string. It's one string per chromosome at best. Um that's also if you if there's websites where you can look at the human reference genome for example and zoom down to all the acts. uh not that these by themselves uh help us often that much but there you usually have to first select which chromosome you're looking at.

10:29 Okay. So the the main amazing part for me is that there's so much computational components in there. There's copying, there's data, there's more coming in terms of translation and essentially little computer-like processes going on.

10:41 It's certainly a distributed and parallel processor different from the centralized way of thinking that we prefer. Uh but it has comput computational components which I think is mind-blowing enough. Uh but it also means uh computerized methods can help a lot in uh advancing biological understanding.

11:08 Now the the latest twist to that of course data science and AI comes also into this because all of the computational things aside there's enough chaos and complication in the systems that more fuzzy techniques can often help. Uh but I'll I'll focus more on the uh the parts of bionformatics where there's a clear edge for discrete and comput um combinatorial algorithms.

11:33 Uh last exciting thing is that sometimes you can replace biological experiments with insilico experiments. So you simulate and that's a mainstream part of of uh biology research these days.

11:49 Okay, we won't make it through the second video I want to watch with you. We'll do that next time. But I want to show you uh what I started preparing namely um uh it's both both a collection of of resources that I like but also a tool. Ah you didn't see the slides. Uh so there was some text accompanying what I said but uh not not much uh beyond what I told you verbally. Um right let me show this briefly. So I I prepared a little mind map with just various links and it's my attempt to involve you a bit in this. So it's a collaborative line mind map. Uh the link is on on elas and campus wire if you want to join. I think some people already played a bit with it. So just very briefly uh it's a cool tool in itself. It's a German startup infinity maps. The basic thing is is a nested mind map. So you can always zoom in and you can zoom in indefinitely and you can um have a lot of complexity baked into some specific part but then immediately zoom out and say oh what's the big picture again ah here we are so you can try to use it to organize knowledge uh whether you want it or not that's you can do that on on your separate account uh you can try it for free um what I what I tried is to structure a bit this the resources I know and which I think are especially because I have trust in their uh scientific accuracy to some degree, but they're at various levels of being whimsical and fun and internet stuff. Uh there's the the videos from the book that I mentioned, bionformmatics algorithms. Uh there's also some some great videos from Ben Langme. He's a bionformatics researcher, produced one of the main tools for read alignment. Um some uh yeah the tree of life is this uh idea that you can uh cluster all the species according to where they diverged in biology in in evolution. I encourage you to look at that as well. And there's a few more um YouTube channels um some of which we'll see videos from.

14:18 Yeah. Yeah. So, have a look at that and add cool things that you find. Uh, if you're not sure whether they're just fun or also scientifically useful, maybe just add a comment of that sort. Uh, up to you to make something from out of this.