 Welcome to this short introduction to network biology. So as a biologist, why should you even care about networks? Why are they interesting? Well, in biology, we are very often interested in the complex interplay between many different entities. And for that, a network is a useful abstraction, and it is an abstraction that lends itself naturally to visualization. However, when you first get into the topic, it can be a complicated thing to understand the literature, especially because of the terminology. So, when I'm talking about a network, I'm talking about something that looks like this. It consists of nodes and edges. However, in the more computational literature, the node is called vertex, and the network as a whole is called graph. It's still the same thing. The nodes in the network represent the things that we want to link to each other. So that could be genes, it could be proteins, it could be diseases, or it could be many other things. Meanwhile, the edges represent the connections or the links between the things. So that could be protein interactions telling you which proteins interact with each other. It could be disease comorbidities telling you which diseases go together in patients. Or it could be gene disease associations telling you which genes are involved in which diseases. There are many different types of networks, and these networks are in fact typically defined by the edges. So the edges can be undirected, and that means that whenever you're looking at an edge between two entities A and B, there's no difference between the edge AB and the edge BA. So that means that you get a network that looks like this, where you have four nodes, ABCD, and you have edges connecting them. You can also have directed networks, and that means that AB and BA are not the same thing. So if you draw them, you will typically have arrowheads on your edges telling you the direction of each edge. These are often encountered when you're looking at things like gene regulation, because obviously the gene A regulating the gene B is not the same as B regulating A. You also run into it whenever you're doing temporal analysis or looking at disease trajectories in patients, for example. The disease A coming before B is not the same as the other way around. Also the edges can be unweighted, which means that all edges are equal, or they can be weighted, meaning that different edges are stronger or more important than other edges. And when you're representing a weighted graph or weighted network, it will look something like this. Some edges are drawn stronger than others. Of course, to work with networks, you have to first get networks from somewhere, and there are many different sources of network that you can explore. It can be interaction databases, or protein interaction databases, for example. One of these is the string database, which I will cover in another presentation, but there are many alternatives as well. You can go to pathway databases and pull out metabolic pathways, for example, even though they are typically drawn in a different way, they can be thought of as networks. You can look at similarity networks where you're linking nodes to each other based on their similarity. That could be from sequence alignment, for example, so you can make protein networks where you link proteins based to homologous proteins. And that then allows you to look at protein families. You can do correlation networks where you're looking across data like, for example, gene expression, and you can then derive networks based on which genes are co-expressed. Then you get these kinds of networks where you can then cluster the networks, for example, and find out which functions reside in which parts of the network. You can look at electronic health registries, so data of patients, and you can correlate the diseases across patients and figure out which diseases tend to lead to which other diseases later on in life. And you can use text mining to dive into the massive biomedical literature and automatically extract associations between many different types of entities, whichever you might be interested in. So this is all I want to say about networks in this presentation. I've linked to another presentation up here, which covers a related topic. Thanks for your attention.