
PathAugmented Graph Transformer Network
Much of the recent work on learning molecular representations has been b...
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Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and infere...
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Accomplishable Tasks in Knowledge Representation
Knowledge Representation (KR) is traditionally based on the logic of fac...
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Expansive Automata Networks
An Automata Network is a map f:Q^n→ Q^n where Q is a finite alphabet. It...
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SkipGNN: Predicting Molecular Interactions with SkipGraph Networks
Molecular interaction networks are powerful resources for the discovery....
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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 ϕ, as well as to find out which initial conditons would guarantee ϕ, or else how can the the graph be updated in order to satisfy ϕ. 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.
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