A major activity within our lab is to continue to develop computational tools (for example, NetworKIN and NetPhorest) and to deploy these on quantitative mass-spectrometry based proteomics (or other types) data to understand at a systems-level the principles of how spatio and temporal assembly of mammalian interaction networks transmits and process information in order to alter cellular behavior. Gaining broader network coverage will facilitate the move from prediction to descriptive modeling of mammalian signaling networks. We research the use of Bayesian statistics and machine learning (in particular Artificial Neural Networks) to model and integrate signaling networks with those related to cell behavior and phenotype. The development of the next generation of algorithms requires use of both ExaScale Computing large-shared memory systems (SGI ALTIX UV) as well as GPU based infrastructure for the acceleration needed for large-scale deployment. We also investigate statistical algorithms such as Markov Random Fields, Markov chain Monte Carlo simulations and Deep AutoEncoders (DAE) on biological data. Another major activity is the analysis of very high-dimensional non-linear data cubes such as those pertaining morphological data obtained through large-scale quantitative microscopy. We deploy both linear and non-linear dimensionality reduction to integrate these data with signaling dynamics.