Preprint Review Version 1 This version is not peer-reviewed

The Status of Causality in Biological Databases for Logical Modeling: Data Resources and Data Retrieval Possibilities

Version 1 : Received: 6 July 2020 / Approved: 7 July 2020 / Online: 7 July 2020 (09:50:40 CEST)

How to cite: Touré, V.; Flobak, Å.; Niarakis, A.; Vercruysse, S.; Kuiper, M. The Status of Causality in Biological Databases for Logical Modeling: Data Resources and Data Retrieval Possibilities. Preprints 2020, 2020070123 (doi: 10.20944/preprints202007.0123.v1). Touré, V.; Flobak, Å.; Niarakis, A.; Vercruysse, S.; Kuiper, M. The Status of Causality in Biological Databases for Logical Modeling: Data Resources and Data Retrieval Possibilities. Preprints 2020, 2020070123 (doi: 10.20944/preprints202007.0123.v1).

Abstract

Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. These regulatory networks can then be used to predict biological and cellular behavior by system perturbations and in silico simulations. Today, broad sets of these interactions are being made available in a variety of biological knowledge resources. Moreover, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. Therefore, data users can find it challenging to efficiently explore resources of causal interaction and to be aware of recorded contextual information that ensures valid use of the data. This manuscript presents a review of public resources collecting causal interactions and the different views they convey, together with a thorough description of the export formats established to store and retrieve these interactions. Our goal is to raise awareness amongst the targeted audience, i.e., logical modelers, but also any scientist interested in molecular causal interactions, about existing data resources and how to get familiar with them.

Subject Areas

causal interactions; databases; interoperability; biological pathway; logical modeling; computational biology

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