Abstract
The use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a logical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient automated deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive reasoning. © Springer International Publishing Switzerland 2013.
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CITATION STYLE
Demolombe, R., Fariñas Del Cerro, L., & Obeid, N. (2013). Automated reasoning in metabolic networks with inhibition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8249 LNAI, pp. 37–47). https://doi.org/10.1007/978-3-319-03524-6_4
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