We are interested in the safety of critical systems whose development is based on models. Implementing failure analyses for this kind of system requires modeling the failures and conditions of their appearances. The failure analysis approaches are mainly based on the structures of systems where boolean equations depict the propagation of faults. The objective of the analysis is to calculate Minimal Cut Sets (MCS), i.e., the smallest sets of basic faults that may cause a feared event and their probabilities. The most efficient MCS resolution method is based on Binary Decision Diagrams (BDD). In this paper, we present a model-based toolset to construct from SysML structural models of systems, the fault trees, and their BDD-representation enabling us to compute MCS. Faults in our approach are not limited to boolean variables; they can be expressed by constraints coming from an arbitrary decidable theory. We validate the toolset capabilities with an oil burner system use case.

Embedded systems are becoming ubiquitous and are subject to demanding standards in both safety and reliability. Modern vehicles, which must respect ISO 26262 standards, use up to 100 Electronic Control Unit (ECUs). Advances in microelectronics enable integration of more functions in the ECU, but at the cost of greater unreliability in hostile operating environments, such as electromagnetic fields, temperature, and humidity. Their software mainly drives embedded system flexibility and smartness. However due to lack of automation, its validation and verification (V&V) takes place throughout the design process and tends to swallow up 40% to 50% of the total development cost. The “Enhanced Quality Using Intensive Test Analysis on Simulators” (EQUITAS) project intends to limit the impact of software V&V on embedded systems cost and time-to-market while improving reliability and functional safety. Project activities include: development of a continuous tool-chain to automate the V&V process of embedded computers, improving the relevance of the test campaigns by detecting the redundant tests using equivalence classes, providing assistance for hardware failure effect analysis (FMEA), and finally assessing the tool-chain under the ISO 26262 requirements.

MOTIVATION: The recognition of antigenic peptides is a major event of an immune response. In current mesoscopic-scale simulators of the immune system, this crucial step has been modeled in a very approximated way. RESULTS: We have equipped an agent-based model of the immune system with immuno-informatics methods to allow the simulation of the cardinal events of the antigenic recognition, going from single peptides to whole proteomes. The recognition process accounts for B cell-epitopes prediction through Parker-scale affinity estimation, class I and II HLA peptide prediction and binding through position-specific scoring matrices based on information from known HLA epitopes prediction tools, and TCR binding to HLA-peptide complex calculated as the averaged sum of a residue-residue contact potential. These steps are executed for all lymphocytes agents encountering the antigen in a wide-reaching Monte Carlo simulation. AVAILABILITY: http://www.cbs.dtu.dk/services/C-ImmSim-10.1/

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at .