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

Chen M, Hofestädt R (2003)
In Silico Biology 3(3): 347-365.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
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.
Erscheinungsjahr
Zeitschriftentitel
In Silico Biology
Band
3
Zeitschriftennummer
3
Seite
347-365
ISSN
PUB-ID

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Chen M, Hofestädt R. Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell. In Silico Biology. 2003;3(3):347-365.
Chen, M., & Hofestädt, R. (2003). Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell. In Silico Biology, 3(3), 347-365.
Chen, M., and Hofestädt, R. (2003). Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell. In Silico Biology 3, 347-365.
Chen, M., & Hofestädt, R., 2003. Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell. In Silico Biology, 3(3), p 347-365.
M. Chen and R. Hofestädt, “Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell”, In Silico Biology, vol. 3, 2003, pp. 347-365.
Chen, M., Hofestädt, R.: Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell. In Silico Biology. 3, 347-365 (2003).
Chen, Ming, and Hofestädt, Ralf. “Quantitative Petri Net Model of Gene Regulated Metabolic Networks in the Cell”. In Silico Biology 3.3 (2003): 347-365.

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PMID: 21685563
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