The conventional reductionist approach to cardiovascular research investigates individual candidate factors or linear signalling pathways but ignores more complex interactions in biological systems. The advent of molecular profiling technologies that focus on a global characterization of whole complements allows an exploration of the interconnectivity of pathways during pathophysiologically relevant processes, but has brought about the issue of statistical analysis and data integration. Proteins identified by differential expression as well as those in protein–protein interaction networks identified through experiments and through computational modelling techniques can be used as an initial starting point for functional analyses. In combination with other ‘-omics’ technologies, such as transcriptomics and metabolomics, proteomics explores different aspects of disease, and the different pillars of observations facilitate the data integration in disease-specific networks. Ultimately, a systems biology approach may advance our understanding of cardiovascular disease processes at a ‘biological pathway’ instead of a ‘single molecule’ level and accelerate progress towards disease-modifying interventions.

Langley SR, Dwyer J, Drozdov I, Yin X, Mayr M. Proteomics: from single molecules to biological pathways. Cardiovasc Res. 2013; 97 : 612-622.

Data Processing

For the increasingly complex analyses, we use 3 different algorithms (MASCOT, SEQUEST, X! Tandem) to achieve the best possible match of  observed mass spectra with theoretical ones in the spectral libraries. The high performance computing power required for data analysis has recently been provided by the Apple Research & Technology Support programme (ARTS), which backs leading research across Europe. The initial calculations to merge thousands of mass spectrometry spectra into a single file is a computer-intensive task, but once the merged dataset is generated, new software, such as Scaffold, allows us to visualize complex mass spectrometry datasets in a user-friendly manner and share the data with our collaborators even on standard computers.