Design and Synthesis of Synchronization Skeletons Using Branching-Time Temporal Logic
Logic of Programs, Workshop
Model and simulation of Na+/K+ pump phosphorylation in the presence of palytoxin
Computational Biology and Chemistry
Probabilistic model checking of complex biological pathways
Theoretical Computer Science
Using probabilistic model checking in systems biology
ACM SIGMETRICS Performance Evaluation Review
A temporal logic to deal with fairness in transition systems
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Bio-PEPA: A framework for the modelling and analysis of biological systems
Theoretical Computer Science
SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Ymer: a statistical model checker
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
SBMF'12 Proceedings of the 15th Brazilian conference on Formal Methods: foundations and applications
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Probabilistic model checking (PMC) is a technique used for the specification and analysis of complex systems. It can be applied directly to biological systems which present these characteristics, including cell transport systems. These systems are structures responsible for exchanging ions through the plasma membrane. Their correct behavior is essential for animal cells, since changes on those are responsible for diseases. In this work, PMC is used to model and analyze the effects of the palytoxin toxin (PTX) interactions with one of these systems. Our model suggests that ATP could inhibit PTX action. Therefore, individuals with ATP deficiencies, such as in brain disorders, may be more susceptible to the toxin. We have also used heat maps to enhance the kinetic model, which is used to describe the system reactions. The map reveals unexpected situations, such as a frequent reaction between unlikely pump states, and hot spots such as likely states and reactions. This type of analysis provides a better understanding on how transmembrane ionic transport systems behave and may lead to the discovery and development of new drugs to treat diseases associated to their incorrect behavior.