This paper exploits a hybrid Petri Nets for representing gene regulatory networks. A hierarchial approach with HPNs makes easier the arrangment of the components in the gene regulatory network based on the biological facts and provides a prospective view of the network.
Reason for using Hybrid Petri nets: Representing a continous value such as a concentration of mRNA or protein is an essential factor in expressing gene regulation. Can do it only with HPN. It allows to handle the continous factor.
Modeling of metabolic pathways, extracellular and intracellular signaling pathways, or gene regulatory networks.
Things that you need to do:
Model discrete values : is certain protein present or not
Model continous values : like concentrations of certain things
Account for time delays associated with transitions ( time it takes to transcribe a gene or something)
Speed of the transitions that can take place.
Associate probabilities of the transition taking place.
This makes two kinds of transitions discrete and continuous (I don't really know what a continous trasition really means)
Something to represent the feedback mechanism ( this is key)
Also need to account for the Protein Protein Interaction
Protein DNA Interaction: Postive of Negative Effect on the synthesis of the protein associated
with a gene.
Solutions and Reactions and Enzymes
Dynamics of the reaction. (forward or backward)
Paper for that
Things that can be used to model these things
Hybrid Petri Nets
Hybrid Automatos
mu-calculus.
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