ABSTRACT
In previous chapters we examined interaction networks of various kinds. The wiring diagrams, or topological properties, of known biological interaction networks form a small subset of all possible kinds of network organization. The most prevalent topological features are thought to be power-law degree distributions and modular structure, that is, the appearance of locally dense clusters, both of which we discuss in greater detail in subsequent chapters. These specific features of network topology might have been selected by evolution because they impart robustness to random attack, as discussed in Chapter 1. Alternatively, they might have no specific utility and be the result of a historical accident that is merely propagated by evolution. In either case, however, one would surmise that structural or topological features of the network should be informative of biological function. This is because the structure either had selective value or was preserved due to functional constraints on certain genes (or their products). In other words, if the topology itself had a selective property, then the study of the conservation of topological properties in evolution could potentially reveal the biological function that is selected for. On the other hand, if topological features merely happened to be preserved as a by-product of the conservation of some other features, then these other features might be revealed by studying the conservation of topology. So far we have remained mostly silent on the biological relevance of
network structure. Here we approach this issue by examining in detail a few specific models of biological interaction networks. These are probably the most well-understood network models in biology-the genetic and regulatory network controlling the life cycle of a bacterial virus (bacteriophage) called lambda and the cell cycle regulation of the lower eukaryotic organism (Saccharomyces cerevisiae). These discussions are aimed at illustrating the mechanistic basis of individual interactions and how these biological mechanisms are embedded within the network at large.
