Catechol-O-methyltransferase (COMT) is an enzyme that catalyses the methylation of catechol substrates, classically in catecholamine metabolism, but also acting upon other substrates such as oestrogen and polyphenols. Although its classical function has been established for more than five decades, an ever expanding COMT role in other pathways and diseases has become a subject of active study in recent years. The most highlighted domains are related with COMT involvement in neuropsychiatric disorders and its role in the neurobiology of cognition, behaviour, emotions, pain processing and perception, sleep regulation, addictive behaviour and neurodegeneration. Nonetheless, great attention is also being devoted to a possible COMT contribution to the development of cardiovascular disorders and hormonally influenced diseases, including cancer. This review aims to update the role of COMT function and its involvement in cardiovascular and neurological disorders.

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Over the last years there has been some interest in models that combine first-order logic and probabilistic graphical models to describe large scale domains, and in efficient ways to perform inference on these domains. Prolog Factor Language (PFL) is a extension of the Prolog language that allows a natural representation of these first-order probabilistic models (either directed or undirected). PFL is also capable of solving probabilistic queries on these models through the implementation of four inference algorithms: variable elimination, belief propagation, lifted variable elimination and lifted belief propagation. We show how these models can be easily represented using PFL and then we perform a comparative study between the different inference algorithms in four artificial problems.