We construct a correlation-based biological network from a data set containing temporal expressions of 517
fibroblast tissue genes at transcription level. Four relevant and meaningful connected subgraphs of the network,
namely: minimal spanning tree, maximal spanning tree, combined graph of minimal and maximal trees, and
planar maximally filtered graph are extracted and the subgraphs' geometrical and topological properties are
explored by computing relevant statistical quantities at local and global level. The results show that the subgraphs
are extracting relevant information from the data set by retaining high correlation coeffcients. The design
principle of the underlying biological functions is reflected in the topology of the graphs.