Abstract
Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a vast amount of life science data, including pathway information such as metabolic and regulatory pathways. The rapid increase of these data for various organisms offers the possibility to perform analyses on the networks for single organisms (intra-species) as well as across different organisms (inter-species). However, the sheer amount and heterogeneity of the data pose a major challenge and call for an integrative system, allowing to manage all this information. With BN++, especially its C++ framework, we presented such a system [2]. In contrast to databases (e.g. KEGG, Reactome, IntAct,…), which offer only predefined analyses such as minimal connected component or pathway detection from a start to an end compound, the mathematical graph representation in BN++ allows additionally the implementation of own routines. The analysis of biochemical pathway information has different applications, e.g. in the process of target identification, drug design and in the search for causes of genetic diseases. Therefore, nodes or edges are removed and alternative pathways in an organism need to be identified. In basic research these networks can be used for the comparison of metabolic processes of different organisms. For example the information on the metabolism of one organism can be used to understand the newly sequenced genome (and, hence the metabolic pathways) of another organism as presented in [1].
