<｜begin▁of▁sentence｜>### Instruction:
In the realm of endocrinology, think of the body as a network of hormones and receptors working in concert, akin to the interplay of nodes and edges in a graph. Imagine we are analyzing the communication pathways within this network, and we've come across a dataset that represents the interactions within the endocrine system. The dataset, in the form of an adjacency list, is as follows: ['1 2 5', '2 3 4', '3 5', '4', '5'].

As an endocrinologist looking to model these interactions using NetworkX, a tool for studying the relationships and networking of the system, how would we transcribe this dataset into a NetworkX graph to visualize and study the intricate web of hormonal signaling pathways? Once we construct this graphical model, could you elucidate what the resulting graph's node and edge set might resemble, thereby revealing the fundamental connections akin to our hormonal interactions?
### Response:
import networkx as nx

lines = ['1 2 5', '2 3 4', '3 5', '4', '5']
G = nx.parse_adjlist(lines, nodetype=int)

print('Nodes:', list(G.nodes()))
print('Edges:', list(G.edges()))
<|EOT|>
