Soundness of workflow nets: classification, decidability, and analysis

Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets.We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.

Modeling prostate cancer: a perspective on transgenic mouse models

Despite considerable success in treatment of early stage localized prostate cancer (PC), acute inadequacy of late stage PC treatment and its inherent heterogeneity poses a formidable challenge. Clearly, an improved understanding of PC genesis and progression along with the development of new targeted therapies are warranted. Animal models, especially, transgenic immunocompetent mouse models, have proven to be the best ally in this respect. A series of models have been developed by modulation of expression of genes implicated in cancer-genesis and progression; mainly, modulation of expression of oncogenes, steroid hormone receptors, growth factors and their receptors, cell cycle and apoptosis regulators, and tumor suppressor genes have been used. Such models have contributed significantly to our understanding of the molecular and pathological aspects of PC initiation and progression. In particular, the transgenic mouse models based on multiple genetic alterations can more accurately address the inherent complexity of PC, not only in revealing the mechanisms of tumorigenesis and progression but also for clinically relevant evaluation of new therapies. Further, with advances in conditional knockout technologies, otherwise embryonically lethal gene changes can be incorporated leading to the development of new generation transgenics, thus adding significantly to our existing knowledge base. Different models and their relevance to PC research are discussed.