  The understanding of molecular cell biology requires insight into the
structure and dynamics of networks that are made up of thousands of interacting
molecules of DNA, RNA, proteins, metabolites, and other components. One of the
central goals of systems biology is the unraveling of the as yet poorly
characterized complex web of interactions among these components. This work is
made harder by the fact that new species and interactions are continuously
discovered in experimental work, necessitating the development of adaptive and
fast algorithms for network construction and updating. Thus, the
"reverse-engineering" of networks from data has emerged as one of the central
concern of systems biology research.
  A variety of reverse-engineering methods have been developed, based on tools
from statistics, machine learning, and other mathematical domains. In order to
effectively use these methods, it is essential to develop an understanding of
the fundamental characteristics of these algorithms. With that in mind, this
chapter is dedicated to the reverse-engineering of biological systems.
  Specifically, we focus our attention on a particular class of methods for
reverse-engineering, namely those that rely algorithmically upon the so-called
"hitting-set" problem, which is a classical combinatorial and computer science
problem, Each of these methods utilizes a different algorithm in order to
obtain an exact or an approximate solution of the hitting set problem. We will
explore the ultimate impact that the alternative algorithms have on the
inference of published in silico biological networks.
