The whole-cell behavior arises from the interplay among signaling, metabolic,
and regulatory processes. Proper modeling of the overall function requires accurate
interpretations of each component. The highly concurrent nature of the inner-cell
interactions motivates the use of Petri nets as a framework for the whole-cell modeling.
Petri nets have been successfully used in modeling of metabolic pathways, as
it allows for a straightforward mapping from its stoichiometric matrix to the Petri
net structure. The Boolean interpretation and modeling of transcription regulation
networks also lends itself easily to Petri net modeling. However, Petri net modeling of
signal transduction networks has been largely lacking, with the exception of simple ad
hoc applications to specific signaling pathways. In this thesis, I investigate the applicability
of Petri nets to modeling of signaling networks, by systematically analyzing
initial token assignments, firing strategies, and robustness to errors and abstractions
in the estimates of molecule concentrations and reaction rates
