How often have I said to you that when you have eliminated the impossible, whatever remains, however improbable, must be the truth? – Sherlock Holmes, A Study in Scarlet

The functional states of reconstructed networks are directly related to cellular phenotypes. With reconstructed networks represented formally, we can use mathematics to compute their candidate functional states. If one adopts the informatics point of view of the stoichiometric matrix, S, and its annotated information as a biochemically, genetically, and genomically structured knowledge base, then these in silico methods are viewed as query tools.

Whether viewed from an informatics or mathematical standpoint, the result of applying in silico analysis methods is the study of network properties, sometimes referred to as emergent properties. These properties represent functionalities of the whole network and are hard to decipher from a list of its individual components. In some sense, these properties are a reflection of the hierarchical nature of living systems.

A variety of methods have been developed to examine the properties of genomescale networks. The third part of this text summarizes the COBRA approach and some of its methods. The first four chapters describe the conceptual framework of the COBRA approach and how optimal states are computed. The next two describe how general properties of optimal solutions can be studied. The last chapter then discusses the objective function, that to some is perhaps one of the most interesting and fundamental topics in constraint-based modeling. More details of the COBRA methods and their implementation are available [236, 299, 344].