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Volume 7


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In Silico Biology 7, 0055 (2007); ©2007, Bioinformation Systems e.V.  



Cell System Ontology: Representation for modeling, visualizing, and simulating biological pathways

Euna Jeong, Masao Nagasaki*, Ayumu Saito and Satoru Miyano

Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo 108-8639, Japan

 Both authors contributed equally to this research

* Corresponding author
   Email: masao@ims.u-tokyo.ac.jp


Edited by E. Wingender; received July 04, 2007; revised September 05, 2007; accepted October 07, 2007; published October 28, 2007


Abstract

With the rapidly accumulating knowledge of biological entities and networks, there is a growing need for a general framework to understand this information at a system level. In order to understand life as system, a formal description of system dynamics with semantic validation will be necessary. Within the context of biological pathways, several formats have been proposed, e. g., SBML, CellML, and BioPAX. Unfortunately, these formats lack the formal definitions of each term or fail to capture the system dynamics behavior. Thus, we have developed a new system dynamics centered ontology called Cell System Ontology (CSO). As an exchange format, the ontology is implemented in the Web Ontology Language (OWL), which enables semantic validation and automatic reasoning to check the consistency of biological pathway models. The features of CSO are as follows: (1) manipulation of different levels of granularity and abstraction of pathways, e. g., metabolic pathways, regulatory pathways, signal transduction pathways, and cell-cell interactions; (2) capture of both quantitative and qualitative aspects of biological function by using hybrid functional Petri net with extension (HFPNe); and (3) encoding of biological pathway data related to visualization and simulation, as well as modeling. The new ontology also predefines mature core vocabulary, which will be necessary for creating models with system dynamics. In addition, each of the core terms has at least one standard icon for easy modeling and accelerating the exchangeability among applications. In order to demonstrate the potential of CSO-based pathway modeling, visualization, and simulation, we present an HFPNe model for the ASEL and ASER regulatory networks in Caenorhabditis elegans.


Keywords: cell system ontology, CSO, biological pathway, data exchange format, HFPNe, dynamic simulation, visualization