Inferring the function of genes from synthetic lethal mutations

Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that uses synthetic lethal mutations for mapping genes of unknown function to enzymes in a known metabolic network. We show how such mappings can be automatically computed by a logical learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL)

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

A framework for modelling Molecular Interaction Maps

Metabolic networks, formed by a series of metabolic pathways, are made of intracellular and extracellular reactions that determine the biochemical properties of a cell, and by a set of interactions that guide and regulate the activity of these reactions. Most of these pathways are formed by an intricate and complex network of chain reactions, and can be represented in a human readable form using graphs which describe the cell cycle checkpoint pathways. This paper proposes a method to represent Molecular Interaction Maps (graphical representations of complex metabolic networks) in Linear Temporal Logic. The logical representation of such networks allows one to reason about them, in order to check, for instance, whether a graph satisfies a given property $\phi$, as well as to find out which initial conditons would guarantee $\phi$, or else how can the the graph be updated in order to satisfy $\phi$. Both the translation and resolution methods have been implemented in a tool capable of addressing such questions thanks to a reduction to propositional logic which allows exploiting classical SAT solvers.Comment: 31 pages, 12 figure

LGEM+^\text{+}: a first-order logic framework for automated improvement of metabolic network models through abduction

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.Comment: 15 pages, one figure, two tables, two algorithm

On deducing causality in metabolic networks

© 2008 Bodei et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution Licens

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

Cortical hyperexcitability in Amyotrophic Lateral Sclerosis: Diagnostic and pathophysiological biomarker

Amyotrophic lateral sclerosis (ALS) is a progressive and degenerative disease of the motor system clinically defined by the presence of upper and lower motor neuron (UMN/LMN) signs. In this thesis the current diagnostic criteria were evaluated, both with a meta-analytical approach and a prospective multicenter design. The lack of an objective UMN biomarker resulted in a delayed diagnosis. Hence a novel threshold tracking transcranial magnetic stimulation (TMS) technique was utilised to measure cortical hyperexcitability, as a biomarker of UMN dysfunction. Cortical hyperexcitability facilitated an earlier diagnosis. This technique was then utilised to gain insights in familial ALS (c9orf72 repeat expansion). Cortical and peripheral nerve abnormalities were evident in familial ALS, but asymptomatic carriers had no evidence of cortical or peripheral nerve dysfunction. We then studied atypical ALS phenotypes such as the clinically UMN predominant variant, primary lateral sclerosis (PLS), reliably differentiating PLS from mimic disorders such as hereditary spastic paraparesis (HSP). In the lower motor neuron variant of ALS, termed flail leg syndrome, cortical hyperexcitability was only evident in patients with upper motor neuron signs. Taken together, these findings suggest that cortical hyperexcitability is a potentially robust diagnostic and pathophysiological biomarker in sporadic, familial and some atypical ALS variants