Network reconstruction is a long and arduous process that involves building a large network in a step-by-step fashion by identifying one reaction at a time. It is foundational to the bottom-up approach to biology. A reconstruction collects all the available biochemical, genetic, and genomic (BiGG) information that is available on a cellular process of interest, and then organizes it in a formal, mathematical fashion that is consistent with the corresponding fundamental chemical and genetic properties. In this chapter we illustrate this process by looking at the familiar glycolytic pathway. Then we introduce the module-by-module nature of the network reconstruction process. We then show how detailed information about the enzymes, genetic information, and structural properties are incorporated in a reconstruction. Next, we will detail the reconstruction of the central metabolic pathways in Escherichia coli and how a knowledge base is formed from the reconstruction process. We close the chapter by discussing the features of genome-scale reconstructions and the computational models formed from them.

Many Reactions and Their Stoichiometry

Networks are composed of compounds (nodes) and reactions (links). When many reactions are known that share reactants and products, they can be graphically linked together. More and more reactions can be added to a graphical representation as one grows the scope of the network under consideration, and Figure 2.1A shows the first few steps in glycolysis as an example. Such reaction maps were formed historically as more metabolic reactions were discovered and they were eventually joined together.

Such a map can be represented mathematically (Figure 2.1B). Such mathematical representation is based on the stoichiometric coefficients that count the molecules that are consumed and produced by a biochemical reaction. All such coefficients for all the reactions in a network can be organized in a matrix format; a mathematical matrix akin to a table. This information is exact, quantitative, and forms the basis for mathematical characterization and assessment of integrative biochemical properties of a network as a whole.