A logical model for metabolic networks with inhibition

Abstract

Metabolic networks formed by long sequences of biochemical reactions have been widely investigated to determine the catalytic role of genomes and how they interfere in the process. Many tumors have been reported to be the result of a pathology in the cell's pathway. Knowing that the complexity of the imbrication of such networks is beyond human reasoning, the use of artificial intelligence to help scientists in their experiments might seem adapted. This paper aims to present 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 an efficient automated deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive reasoning