An overview of data models for the analysis of biochemical pathways

Biochemical pathways such as metabolic, regulatory or signal tranduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains however far from straight forward due to the nature of the databases, which are often heterogeneous, incomplete, or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics. Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, we classify the different types of data models found in the literature using a unified framework. In addition we describe how these models have been used in the analysis of biochemical networks. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions