<｜begin▁of▁sentence｜>### Instruction:
In the course of our ongoing medical research, we've been modeling the interactions between two groups of biological elements using a bipartite graph. Consider we have two distinct sets of elements where set U={'u1', 'u2', 'u3'} represents one type of proteins and set V={'v1', 'v2', 'v3', 'v4'} possibly represents another type, such as receptors. The relationships between these proteins and receptors are defined by the edges E={('u1', 'v1'), ('u1', 'v2'), ('u2', 'v3'), ('u3', 'v4')}. For analytical purposes, we need to transform this relational data into a biadjacency matrix. How would we go about constructing this biadjacency matrix utilizing the capabilities of the NetworkX library? Note that no specific data files are mentioned in this scenario.
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a bipartite graph
B = nx.Graph()
B.add_nodes_from(['u1', 'u2', 'u3'], bipartite=0)
B.add_nodes_from(['v1', 'v2', 'v3', 'v4'], bipartite=1)
B.add_edges_from([('u1', 'v1'), ('u1', 'v2'), ('u2', 'v3'), ('u3', 'v4')])

# Define node order for rows and columns
u = ['u1', 'u2', 'u3']  # nodes from one set (U)
v = ['v1', 'v2', 'v3', 'v4']  # nodes from other set (V)

# Get the biadjacency matrix
bi_matrix = nx.algorithms.bipartite.matrix.biadjacency_matrix(B, row_order=u, column_order=v, dtype=None, weight=None, format='csr')
print(bi_matrix)
<|EOT|>
