The variety and complexity of data gathered from biomolecular and neuroimaging studies require an appropriate support, both algorithmic and numerical, from the disciplines of computer science, mathematical modeling, and statistical modeling, in order to provide researchers from the biomedical fields with a more extended toolset capable to aid in the identification of particular pathological phenomena; e.g., using novel “pattern recognition” techniques. Modern neurosciences are not limited to the study and description of single altered parameters, but they frame them in the context of different processes and stages, and, in the clinical case, in the context of “disease and disorder models”, which are concerned with the progression of several observable phenomena. Such progressions need to be measured and often reconstructed from cross-sectional data; moreover they need to be described by means of controlled vocabularies and ontologies in order to communicate concise, shared, and informative summaries to different researchers and clinical practitioners. Many of the algorithmic and numerical underpinnings of such multifarious methods require sophisticated and large scale computational infrastructure, not to mention the special issues that arise from direct modeling and, above all, simulation of neuronal assemblies and activities. Within the H2020 program, the area “Information and Communication Technology”, alongside the theme “Exascale and Parallel Computing”, is foundational to most “Excellent Science” research programs, including those clustered under the Health heading. Among these, the theme “Systems Biology” appears under different guises. To this end, the collaboration among the Neuroscience Center, the Sysbio Center of the University of Milan Bicocca, and the Departments of the Science Area will foster several integration activities of the data generated from the different kinds of studies just mentioned, also as a consequence of the cross-cutting effort on ICT, Parallel Computing, Big Data administration and management, and more traditional algorithmic and computational research.