CE45 - Mathématique, informatique, automatique, traitement du signal pour répondre aux défis de la biologie et de la santé

Automating Building from Literature of Signaling Systems – ABLISS

Submission summary

G protein-coupled receptors (GPCR) are very good targets for drugs. Their presence in the cell membrane make them accessible to drugs, and these receptors are involved in the vast majority of cellular processes. Indeed, GPCR are targeted by more than 30% of marketed drugs. To increase the efficacy and decrease adverse side effects of these drugs, a better comprehension of GPCR signalling is necessary. Knowledge concerning the different receptors has drastically increased these last years. The downside of this phenomenon is the profusion of omics data and scientific papers, the integration of which is a real challenge.
The objective of ABLISS is the development of a method for building these signalling networks from available data as a whole: literature and large-scale datasets. The method will encompass two main components: (1) a natural language processing module, allowing to extract and format experimental results from scientific papers and (2) a knowledge-based method, allowing the inference of the network from these results. The framework will be applied to the deciphering of GPCR-triggered ß-arrestin- and ERK-dependent signalling.
A first workpackage will be devoted to the knowledge-based method. The principle will be the formalization in ASP (Answer Set Programming) of the reasoning that allows the expert deducing network elements from experimental results. We have developed a first prototype, and thus demonstrated the feasibility of our approach. In ABLISS, we will extend the rules and predicate database to cover more experiment types, but also to adapt the reasoning module to the predicate-arguments structures that can be automatically generated by the natural language processing module. We will also study the reliability of a deduced fact. Finally, we will develop abductive reasoning to propose experimental plan allowing verifying hypotheses within the network.
A second workpackage will concern the natural language processing module. During the preliminary work on knowledge-based network inference, the necessary manual extraction and formalization of experimental facts has appeared as a major limitation. We have shown, for a limited number of experimental results, that a transducer cascade allows extracting and formatting predicate-arguments structures directly from scientific publications. In ABLISS we will pursue this task, in particular through the development of a transducer cascade allowing extraction and formalization of experimental facts obtained through a large diversity of experiment types. Iteratively, we will ensure the completeness of this predicate ensemble. Finally, we will develop modules to complete the arguments of a predicate when these are not all present locally.
The third workpackage will apply the framework to the building of ERK- and ß-arrestin-dependent signalling triggered by different GPCR. A first reason for this choice is that the concerned scientific publications corpus is relatively modest (around 1300 publications), allowing a manual control of obtained results. A second reason is the expertise of the partner coordinating the project in this particular area. New knowledge hypothesized in the network will be validated experimentally.

Project coordination

Anne Poupon (Physiologie de la reproduction et des comportements)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

PRC Physiologie de la reproduction et des comportements
LRI Laboratoire de Recherche en Informatique
EA6300 LABORATOIRE D'INFORMATIQUE

Help of the ANR 441,626 euros
Beginning and duration of the scientific project: September 2018 - 48 Months

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