Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell - Biological Petri Nets - page 38

Biological Petri Nets

Quantitative Petri Net Model of Gene
Regulated Metabolic Networks in the Cell

Ming Chen ∗ and Ralf Hofestadt
Bioinformatics/Medical Informatics, Technical Faculty, University of Bielefeld, Bielefeld, Germany

ABSTRACT: A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic
networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the
bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control
mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a
HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities
for metabolic engineering and medical care.

Finally, the perspective of Petri nets on modeling and simulation of metabolic

networks is discussed.

KEYWORDS: Metabolic network, gene regulation, Petri nets, quantitative model, urea cycle, modeling and simulation

INTRODUCTION

With the success of the human genomic project, we have experienced increasing floods of data, both
in terms of volumes and in terms of new databases and new types of data. More and more experimental
data both on the genetic and cellular level are systematically collected and stored in specific databases
that are also available to public via the Internet [Baxevanis, 2003]. Some well-known databases are:
gene sequence (e.g. GenBank, EMBL, DDBJ), protein (e.g. SWISS-Prot, PIR, BRENDA), biochemical
reactions (e.g. KEGG, WIT/MPW), transcription factors (TRANSFAC) and signal induction reactions
(e.g. CSNDB, TRANSPATH, GeneNet). In the post-genomic era, the focus is now shifting to the so
called “from the sequence to the function”, i.e., in addition to completing genome sequences, we are
learning about gene expression patterns and protein interactions on the genomic scale. Undoubtedly,
analysis of metabolic networks is becoming a promising field, which requires providing new algorithms
and tools to fulfill this task.
The study of gene regulated metabolic networks plays an important role in the detection of ge-
netic/metabolic defects as well as drug research. Genetic/metabolic defects often lead to metabolic
blockades, resulting in metabolic diseases. Many inborn errors of metabolism result from a single gene
encoded enzyme deficiency. Regarding drug research, it is necessary to first understand the reaction
pathways that are affected by the drug, directly and indirectly, and to know the effect of the modification

∗ Corresponding author: Ming Chen, Bioinformatics/Medical Informatics, Technical Faculty, University of Bielefeld, Postfach
10 01 31, D-33501 Bielefeld, Germany. E-mail: [e-mail] .