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Life depends on the capacity of individual cells to respond effectively to cues about their changing internal and external environments. Cellular decision making and responses are orchestrated by complex molecular networks consisting of entities such as proteins or RNAs connected by interactions such as activation or synthesis. Information contained in primary databases and in the experimental literature relevant to these networks is so extensive and rapidly growing that it is increasingly difficult to integrate. As an aid to theoretical and experimental research, it is convenient to distill the inferences contained in the experimental literature and databases into knowledgebases that consist of annotated representations of biological pathways. Pathway building has been performed by individual groups studying a network of interest (e.g., Kitano's group who assembled an immune signaling pathway [1]) as well as by large bioinformatics consortia (e.g., the Reactome Project [2]) and commercial entities (e.g., Ingenuity Systems). Pathway building is the process of identifying and integrating the entities, interactions, and associated annotations, and populating the knowledgebase. Pathway construction can have either a data-driven objective (DDO) or a knowledge-driven objective (KDO). Data-driven pathway construction is used to generate relationship information of genes or proteins identified in a specific experiment such as a microarray study. Knowledge-driven pathway construction entails development of a detailed pathway knowledgebase for particular domains of interest, such as a cell type, disease, or system. To help researchers get their bearings in this field, in the subsequent sections we provide a brief, practical orientation to existing knowledgebases and to the methods of pathway construction and analysis.