Inferring Compensatory Kinase Networks in Yeast using Prolog
George A. Elder (Queen Mary University of London), Conrad Bessant, (Queen Mary University of London)

TL;DR
This paper presents a Prolog-based method to infer compensatory kinase networks in yeast, leveraging phosphoproteomics data to understand signaling mechanisms and identify potential therapeutic targets.
Contribution
It introduces a novel symbolic reasoning approach using Prolog to analyze kinase interactions and compensation in yeast signaling pathways.
Findings
Prolog effectively infers compensatory kinase networks.
The approach reveals new insights into kinase regulation.
Potential therapeutic targets identified in yeast signaling pathways.
Abstract
Signalling pathways are conserved across different species, therefore making yeast a model organism to study these via disruption of kinase activity. Yeast has 159 genes that encode protein kinases and phosphatases, and 136 of these have counterparts in humans. Therefore any insight in this model organism could potentially offer indications of mechanisms of action in the human kinome. The study utilises a Prolog-based approach, data from a yeast kinase deletions strains study and publicly available kinase-protein associations. Prolog, a programming language that is well-suited for symbolic reasoning is used to reason over the data and infer compensatory kinase networks. This approach is based on the idea that when a kinase is knocked out, other kinases may compensate for this loss of activity. Background knowledge on kinases targeting proteins is used to guide the analysis. This…
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