MIM-Logic: a logic for reasoning about molecular interaction maps

Les séries de réactions biochimiques apparaissant au cœur d'une cellule forme ce qu'on appelle des voies métaboliques. La plupart de ces voies sont très complexes impliquant un grand nombre de protéines et d'enzymes. Une représentation logique de ces réseaux contribue au raisonnement à propos de ces voies en général, allant du fait de répondre à certaines questions, compléter des arcs et nœuds manquant, et trouver des incohérences. Dans ce contexte on propose un nouveau model logique basé sur un fragment de logique de premier ordre capable de décrire les réactions apparaissant dans des Molecular Interaction Maps. On propose aussi une méthode de déduction automatique efficace capable de répondre aux questions par déduction pour prédire les résultats des réactions et par abduction pour trouver les états des protéines et de leurs réactions. Cette méthode automatique est basée sur une procédure de traduction qui élimine les quantificateurs des formules de logique premier ordre.The series of biochemical reactions that occur within a cell form what we call Metabolic Pathways. Most of them can be quite intricate and involve many proteins and enzymes. Logical representations of such networks can help reason about them in general, where the reasoning can range from answering some queries, to completing missing nodes and arcs, and finding inconsistencies. This work proposes a new logical model based on a fragment of first-order logic capable of describing reactions that appear in a Molecular Interaction Maps. We also propose an efficient automated deduction method that can answer queries by deduction to predict reaction results or by abductive reasoning to find reactions and protein states. This automated deduction method is based on a translation procedure that transforms first-order formulas into quantifier free formulas

Automated reasoning in metabolic networks with inhibition

International audienceThe 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

On Teaching Future Time to EFL Learners: Problems and Solutions

This study in all its overall presentation seeks to give a comprehensive account of the difficulties involved in teaching future time to students who learn English as a foreign language; and the (pedagogical) solutions humbly suggested for learners, teachers, text-book writers, linguists and psychologists since they are expected to be the best who can deal with the problems that impede the acquisition of the foreign language concepts. The work like many language teachers' works is a reaction to the frustrating state of the students who cannot use their knowledge in a real communicative situation. It spotlights the reasons of students' inefficient use of the English future structures; and projects, through a questionnaire, the most possible reasons of this problem propounding some useful techniques to overcome the problem depending on what is written in the literature of language learning and teaching. The study tackles the topic of tense and time as an entrance to investigate the nature of future structures. Apart from presenting a pedagogical view of future time references, it provides almost all the constructions used to express futurity and the indications they refer to getting use of the idea of Fleischman's time-line. The study ends with many results, findings and pedagogical suggestions.Keyword: Futurity, Pedagogy and pedagogical grammar, EFL acquisition, Tense and time, Time-Lin

Molecular interaction automated maps

Determining the role of genomes and their interference in a cell life cycle has been at the center of metabolic network researches and experiments. Logical representations of such networks aim to guide scientists in their reasoning in general and to help them find inconsistencies and contradictions in their results in particular. This paper presents a new logical model capable of describing both positive (activation) and negative (inhibition) reactions of metabolic pathways based on a fragment of first order logic. An efficient automated deduction method will also be introduced, based on a translation procedure that transform first order formulas into quantifier free formulas.Then questions can either be answered by deduction to predict reaction results or by abductive reasoning to infer reactions and protein state

A Logical Model for Metabolic Networks with Inhibition

International audienceMetabolic 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 bothpositive 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

A logical model for metabolic networks with inhibition

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 reasonin

Temporal Logic Modeling of Biological Systems

International audienc