We have developed some of the most frequently used bioinformatics algorithms in studying protein disorder (GlobPlot and DisEMBL) as well as linear motifs in signaling proteins (ELM). Recently we launched the NetPhorest automated machine learning framework which is now a community resource for signaling biologists. A major goal is to integrate the NetPhorest atlas into NetworKIN. This will enable more accurate predictions to be made for a larger fraction of the kinome and facilitate modeling of interactions mediated by e.g SH2 and BRCT phospho-binding domains. We are developing new systems specific contextual algorithms which will enable us to calculate protein-associations similar to STRING in a systems (e.g. a particular cell-line) specific manner. We aim to release all our code as Open Source and as web-services once they have matured. We are developing and applying new powerful tools for quantitative and systems biological research. In collaboration with Prof Tony Pawsons laboratory in Toronto we have recently published the large-scale open source laboratory information management system OpenFreezer and an in-house pipeline for proteomics data analysis, ProteoChart (Pasculescu et al., unpublished).