Skip to content
BY-NC-ND 4.0 license Open Access Published by De Gruyter October 18, 2016

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2

  • Huaiyu Mi EMAIL logo , Falk Schreiber , Stuart Moodie , Tobias Czauderna , Emek Demir , Robin Haw , Augustin Luna , Nicolas Le Novère , Anatoly Sorokin and Alice Villéger

Summary

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Published Online: 2016-10-18
Published in Print: 2015-6-1

© 2015 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Downloaded on 19.3.2024 from https://www.degruyter.com/document/doi/10.1515/jib-2015-265/html
Scroll to top button