We present regular model checking, a framework for algorithmic verification of infinite-state systems with, e.g., queues, stacks, integers, or a parameterized linear topology. States are represented by strings over a finite alphabet and the transition relation by a regular length-preserving relation on strings. Major problems in the verification of parameterized and infinite-state systems are to compute the set of states that are reachable from some set of initial states, and to compute the transitive closure of the transition relation. We present two complementary techniques for these problems. One is a direct automata-theoretic construction, and the other is based on widening. Both techniques are incomplete in general, but we give sufficient conditions under which they work. We also present a method for verifying ω-regular properties of parameterized systems, by computation of the transitive closure of a transition relation.

Regular model checking is being developed for algorithmic verification of several classes of infinite-state systems whose configurations can be modeled as words over a finite alphabet. Examples include parameterized systems consisting of an arbitrary number of homogeneous finite-state processes connected in a linear or ring-formed topology, and systems that operate on queues, stacks, integers, and other linear data structures. The main idea is to use regular languages as the representation of sets of configurations, and finite-state transducers to describe transition relations. In general, the verification problems considered are all undecidable, so the work has consisted in developing semi-algorithms, and decidability results for restricted cases. This paper provides a survey of the work that has been performed so far, and some of its applications.

Mansouri et al. applied targeted deep sequencing to identify mutations within NF-κB core complex genes in CLL. NFKBIE, the gene encoding the inhibitory IκBε molecule, was most frequently mutated, especially in poor-prognostic subgroups of CLL. The authors show that NFKBIE mutations were associated with significantly reduced IkBε expression and p65 inhibition, ultimately leading to NF-κB activation and a more aggressive disease.