Dynamic Input/Output Automata: a Formal and Compositional Model for Dynamic Systems

We present dynamic I/O automata (DIOA), a compositional model of dynamic systems, based on I/O automata. In our model, automata can be created and destroyed dynamically, as computation proceeds. In addition, an automaton can dynamically change its signature, that is, the set of actions in which it can participate. This allows us to model mobility, by enforcing the constraint that only automata at the same location may synchronize on common actions. Our model features operators for parallel composition, action hiding, and action renaming. It also features a notion of automaton creation, and a notion of trace inclusion from one dynamic system to another, which can be used to prove that one system implements the other. Our model is hierarchical: a dynamically changing system of interacting automata is itself modeled as a single automaton that is "one level higher." This can be repeated, so that an automaton that represents such a dynamic system can itself be created and destroyed. We can thus model the addition and removal of entire subsystems with a single action. We establish fundamental compositionality results for DIOA: if one component is replaced by another whose traces are a subset of the former, then the set of traces of the system as a whole can only be reduced, and not increased, i.e., no new behaviors are added. That is, parallel composition, action hiding, and action renaming, are all monotonic with respect to trace inclusion. We also show that, under certain technical conditions, automaton creation is monotonic with respect to trace inclusion: if a system creates automaton Ai instead of (previously) creating automaton A'i, and the traces of Ai are a subset of the traces of A'i,then the set of traces of the overall system is possibly reduced, but not increased. Our trace inclusion results imply that trace equivalence is a congruence relation with respect to parallel composition, action hiding, and action renaming. Our trace inclusion results enable a design and refinement methodology based solely on the notion of externally visible behavior, and which is therefore independent of specific methods of establishing trace inclusion. It permits the refinement of components and subsystems in isolation from the entire system, and provides more flexibility in refinement than a methodology which is, for example, based on the monotonicity of forward simulation with respect to parallel composition. In the latter, every automaton must be refined using forward simulation, whereas in our framework different automata can be refined using different methods. The DIOA model was defined to support the analysis of mobile agent systems, in a joint project with researchers at Nippon Telegraph and Telephone. It can also be used for other forms of dynamic systems, such as systems described by means of object-oriented programs, and systems containing services with changing access permissions

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock