<｜begin▁of▁sentence｜>### Instruction:
In the context of analyzing an intricate biological signaling network where the nodes represent signaling molecules and the edges indicate the direction and strength of signal transduction, imagine we have a network characterized by the following interaction data: nodes 1 and 2 are connected with a signal strength of 1.0, nodes 1 and 3 with a signal strength of 2.0, and nodes 2 and 3 with a signal strength of 3.0. Each interaction is directional, reflecting the directional flow of biological signals. Could you compute the global reaching centrality of this biological signaling network using NetworkX to provide deeper insight into the influence of individual molecules across the entire network? Remember, this measure could shed light on which molecules have a more prominent role in signal propagation within this system.
### Response:
import networkx as nx

# Create a weighted directed graph
G = nx.DiGraph()
G.add_weighted_edges_from([(1, 2, 1.0), (1, 3, 2.0), (2, 3, 3.0)])

# Calculate global reaching centrality
grc = nx.global_reaching_centrality(G, weight='weight')
print('Global Reaching Centrality:', grc)
<|EOT|>
