  Much of contemporary systems biology owes its success to the abstraction of a
network, the idea that diverse kinds of molecular, cellular, and organismal
species and interactions can be modeled as relational nodes and edges in a
graph of dependencies. Since the advent of high-throughput data acquisition
technologies in fields such as genomics, metabolomics, and neuroscience, the
automated inference and reconstruction of such interaction networks directly
from large sets of activation data, commonly known as reverse-engineering, has
become a routine procedure. Whereas early attempts at network
reverse-engineering focused predominantly on producing maps of system
architectures with minimal predictive modeling, reconstructions now play
instrumental roles in answering questions about the statistics and dynamics of
the underlying systems they represent. Many of these predictions have clinical
relevance, suggesting novel paradigms for drug discovery and disease treatment.
While other reviews focus predominantly on the details and effectiveness of
individual network inference algorithms, here we examine the emerging field as
a whole. We first summarize several key application areas in which inferred
networks have made successful predictions. We then outline the two major
classes of reverse-engineering methodologies, emphasizing that the type of
prediction that one aims to make dictates the algorithms one should employ. We
conclude by discussing whether recent breakthroughs justify the computational
costs of large-scale reverse-engineering sufficiently to admit it as a mainstay
in the quantitative analysis of living systems.
