REVERSE ENGINEERING OF MOLECULAR NETWORKS FROM A COMMON COMBINATORIAL APPROACH
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. To use these methods effectively, 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 on the so-called “hitting set" problem, which is a classical combinatorial and computer science problem, Each of these ...