Biology Reference
In-Depth Information
Biological Petri Nets
Short Communication
Ontology Based Standardization of Petri Net
Modeling for Signaling Pathways
Takako Takai-Igarashi
Graduate School of Information Science and Technology, University of Tokyo, 7-3-1, Hongou, Bunkyou-ku, Tokyo
113-0033, Japan
E-mail:
taka@bi.is.s.u-tokyo.ac.jp
ABSTRACT:
Taking account of the great availability of Petri nets in modeling and analyzing large complicated signaling
networks, semantics of Petri nets is in need of systematization for the purpose of consistency and reusability of the models.
This paper reports on standardization of units of Petri nets on the basis of an ontology that gives an intrinsic definition to the
process of signaling in signaling pathways.
KEYWORDS:
Petri net, signaling pathway, ontology, knowledge representation
Abbreviations:
AP-1, activator protein-1; CRM1, Chromosomal Region Maintenance 1; CSNO, Cell Signaling Networks Ontology; IL-3,
interleukin-3; imp-b, importin-beta; MH2 domain, MAD Homolog 2 domain; NPC, nuclear pore complex; Ran, GTP-binding
nuclear protein Ran; Smad, Mothers against decapentaplegic homolog; Smurf, Smad ubiquitination regulatory factor; TGFb,
TGF-beta, transforming growth factor-beta; TGFbR, transforming growth factor-beta receptor
INTRODUCTION
Petri nets are a popular formalization used for modeling and verifying non-deterministic discrete event
systems, focusing on causal relationships between two of the discrete events [1]. A Petri net is a bipartite
graph composed of two classes of nodes called
places
and
transitions
, normally associated with systems
conditions for occurrence of events and actual occurrence of the events, respectively. Topological
connections of nodes in the graph concisely indicate static relations of conditions and events. Dynamical
behaviors of the systems are indicated by distributions of
tokens
changed progressively along individual
fulfillments of conditions at
places
and succeeding firing the events at
transitions
.
Application of Petri nets to biological pathways began with metabolic pathways. The application
was extended to metabolic pathways including gene regulation, and then to gene regulation including
signaling pathways. Petri nets have been studied for the framework of quantitative simulations that
qualitative representation such as KEGG [2] was applied to. In Petri net models of biological pathways,
places, transitions
, and
tokens
account for 'conditions of reactants for occurrence of biological reactions',
'actual occurrence of the biological reactions', and 'concentration of the reactants', respectively.
