<｜begin▁of▁sentence｜>### Instruction:
As a gene therapist, imagine you're working on a project to identify key genetic pathways within a complex network of genes implicated in a particular disorder. You have data representing gene interactions, akin to a graph with edges denoting connections between genes. Consider a scenario where you have a dataset containing the gene interactions: gene A interacts with gene B, gene B interacts with gene C, gene C interacts with gene D, and gene D interacts with gene A.

Now, you're tasked with quantifying the "closeness" of each gene within this network, which could signify how centrally located a gene is in the genetic pathway. To achieve this, you'd employ specialized tools, such as the Graph module in igraph, a software package commonly used in biological network analysis. Specifically, you'd utilize the `closeness` function from the Graph module in igraph to calculate the closeness centralities of all genes in the network. This measure could provide insights into which genes play pivotal roles in the disorder's genetic mechanisms, aiding in the development of targeted gene therapy interventions.
### Response:
from igraph import Graph

# Define the edges for the graph
edges = [(0, 1), (1, 2), (2, 3), (3, 0)]

# Create the graph with the defined edges
g = Graph(edges=edges)

# Calculate the closeness centrality for all vertices
closeness_centrality = g.closeness()

print('Closeness centrality:', closeness_centrality)
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