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Although no naming system is enforced by esyN, it is highly recommended that you use a consistent system when making a network. This will help avoid errors in your own projects, and will make any projects you make public much easier for others to use. We advise users to stick to the gene name (e.g. SOD1) as given by Ensembl. This will be a symbol appropriate the the organism. You can search ensembl for the name of the currently selected node by clicking "Search Ensembl" in the tool panel on the right hand side of the page.
Add all the missing edges between the nodes in your network!
If you have built your network using a database you can add interactions from another database (e.g. H. sapiens (Biogrid) and H. sapiens (DrugBank).
If you have added nodes in your network  you can make sure all interactions between the new nodes and the rest of the network have been added.
A coarse (or parent) place is different from a normal place in that it contains other places (its children) rather than tokens. It is a placeholder representing the idea that the edges connected to it actually apply to a number of other places (the places it contains). Coarse places can be considered as "classes" of place nodes, which represent a general category of place rather than a specific entity (and therefore they don't require tokens). For example, the coarse place "cities" may contain "Paris" and "London", or the coarse place "Kinase" would contain "Protein kinase A", "Protein kinase B". Any edges that apply the the coarse place will be inherited by the places it contains.
Coarse places are represented by orange nodes.
Discover which nodes are the most central in your network. By default we are treating the network as made of indirected edges and we are calculating the betweenness centrality for each node, which is equal to the total number of shortest paths that pass though that node.
You can change these settings by selecting the "Advanced Options", where it is possible to choose to treat the network as made of Directed, Undirected or Mixed edges and calculate the Degree, the Closeness, the Betweenness, the Eccentricity, the Radiality, the Stress or the Centroid Value Centrality.
The Degree is the conceptually simplest centrality measure. It measures the number of edges attached to a node. If the network has directed adges then the degree can be separated into indegree (incoming edges) and outdegree (outgoing edges).
The Closeness centrality counts how far is each node from all the orthers. The distance between any two nodes is set to be 1. The value of the closeness for one selected node v is the reciprocal of the sum of all shortest distances connecting this node with all other nodes in the network. A node with a high value compared to the average closeness has the shorter distances to other network components then most of the other nodes and is therefore considered to be closer to the other network components.
The betweenness of a node v is obtained by counting the number of all shortest paths, connecting any pair of nodes within the network, which are going through that particular node v. The value is divided by the number of all shortest paths connecting two nodes. A node with a high betweenness compared to the average has an increased number of shortest paths going through, and therefore even if it does not have a high degree or a high closeness its a key node in the network.
The eccentricity of each individual node is the reciprocal of the longest shortest path connecting the node with all other components of the network. A node with a high value for eccentricity compared to the average has shorter distances to the other nodes and is therefore considered to be central in the graph.
The radiality of a node is the sum of the maximum shortest distance overall the network (i.e. the diameter) plus one minus the distance to all other nodes. This value is divided by the number of nodes minus one. A high radiality compared to the average is pointing out a close distance to a high number of other network components.
The stress of a node v is obtained by counting the number of all shortest path connecting any node couple in the networks, which are going through that particular node v. A node with a high value of stress compared to the average value has an increased number of paths passing through, which makes this node more important for the connectivity of the network.
The centroid value of a node v is obtained by checking, for each node pair v, w which other nodes are closer to v rather than to w. The centroid value of v is therefore, the minimum such value for all node pairs involving v.
The Collective Influence is the product of a node reduced degree (number of links minus one) times the sum of the reduced degree of the nodes that are a certain number of steps away (the radius). See: Morone, F. & Makse, H. A. Nature 524, 6568 (2015).
Discover what happens to the centrality measures when one or more nodes are removed from the network.
If you have calculated one of them previously you can repeat the calculation and view the differences.
Discover the shortest path between any two nodes. By default we are treating the network as made of indirected edges and we are calculating the shortest path between the source and the target.
You can change these settings by selecting the "Advanced Options", where it is possible to choose to treat the network as made of Directed, Undirected or Mixed edges and calculate the shortest path between the source and the target and viceversa.
Enter a commaseparated list of genes or upload a list of genes. Uploaded files must be .txt format with genes as a single column. Note this might take a moment for very large lists.
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Place  Coarse place 
Disperse place  Selected place 
Transition  Selected transition 
Coarse transition  Edge 
Inhibitor edge  Selected edge 
Welcome to esyN!
Click in the blue area to create/ edit nodes and edges.
Select a layout from the layouts tab on the left.
Interactions for the selected node can be retrieved at the bottom of the page.
Directed Edges 
Undirected Edges 
Mixed Edges (slow) 
Directed Edges 
Undirected Edges 
Mixed Edges (slow) 
Find Shortest Path (source _{↔} target)  Find Shortest Path (source _{→} target) 