A petri net application to model metabolic processes
Systems Analysis Modelling Simulation
Modelling with Generalized Stochastic Petri Nets
Modelling with Generalized Stochastic Petri Nets
Petri Net Representations in Metabolic Pathways
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
A Combinatorial Approach to Reconstruct Petri Nets from Experimental Data
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Petri nets for systems and synthetic biology
SFM'08 Proceedings of the Formal methods for the design of computer, communication, and software systems 8th international conference on Formal methods for computational systems biology
Discrete, Continuous, and Hybrid Petri Nets
Discrete, Continuous, and Hybrid Petri Nets
An algorithmic framework for network reconstruction
Theoretical Computer Science
How might petri nets enhance your systems biology toolkit
PETRI NETS'11 Proceedings of the 32nd international conference on Applications and theory of Petri Nets
On Minimality and Equivalence of Petri Nets
Fundamenta Informaticae - Concurrency, Specification and Programming
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Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we exemplify how molecular mechanisms, biochemical or genetic, can be consistently respresented in the form of place/transition Petri nets. We then describe the application of Petri nets to the reconstruction of molecular and genetic networks from experimental data and their power to represent biological processes with arbitrary degree of resolution of the subprocesses at the cellular and the molecular level. Petri nets are executable formal language models that permit the unambiguous visualization of regulatory mechanisms, and they can be used to encode the results of mathematical algorithms for the reconstruction of causal interaction networks from experimental time series data.