<｜begin▁of▁sentence｜>### Instruction:
As a Bioinformatics Analyst, you might encounter situations where you need to analyze integrated networks resulting from combining data from distinct biological pathways. Imagine you are working with two such pathways represented by networks: our first network, which we'll refer to as Network G, models a signaling pathway forming a feedback loop composed of three proteins with interactions denoted as 0-1-2-0. Protein 0 in Network G plays a significant role, indicated by its attribute 'key1' valued at 5, which might represent some biological metric such as expression level or binding affinity.

Our second network, Network H, represents a different pathway or interaction set, showcasing three interactions: 0-3, 1-2, and 2-3. Here, protein 0, maybe a receptor or regulator, is highlighted by an attribute 'key2' with a value of 10. This could suggest a different biological feature or quantitative measurement from that in Network G.

We are tasked with the challenge of merging these networks into a unified network, which we'll call Network U, for a holistic analysis. However, it's critical to ensure that the identities of the proteins (nodes) from Networks G and H are distinct in Network U to prevent any erroneous amalgamation of data - a scenario that could happen if proteins from different pathways are inaccurately assumed to be the same simply because they share the same identifier.

To proceed correctly, you would need to create Network U as a combination of both Network G and Network H while preserving their specific node attributes. After constructing Network U, we require a detailed readout that includes the profiles (nodes and their attributes) and the interaction details (edges) of the combined network, effectively enabling us to examine the comprehensive map of interactions alongside the particular characteristics associated with each protein.

For record-keeping and future reference, could you reformulate the method to achieve this network combination while maintaining protein uniqueness and attribute integrity, followed by a summary output of the composite Network U's topology and attribute data?
### Response:
import networkx as nx

# Define two example graphs G and H
G = nx.Graph([(0,1), (0,2), (1,2)])
H = nx.Graph([(0,3), (1,2), (2,3)])

# Add a node attribute to G and H
G.nodes[0]['key1'] = 5
H.nodes[0]['key2'] = 10

# Perform the disjoint union of G and H
U = nx.disjoint_union(G, H)

# Print the nodes with their data and the edges of the union graph
print('Nodes with data:', U.nodes(data=True))
print('Edges:', U.edges)
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