Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Dieter Maier, Wenzel Kalus, Martin Wolff, Susana G Kalko, Josep Roca, Igor Marin de Mas, Nil Turan, Marta Cascante, Francesco Falciani, Miguel Hernandez, Jordi Villà-Freixa, Sascha Losko

To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype-phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.