Working group: "Computational Biology"

2012, the 7th of February

Tatjana Petrov

Title: Abstraction, decomposition and reconstruction of stochastic semantics of rule-based models.

Internal dependencies of multi-site post-translational modifications�and conformational changes�of signaling proteins reflect the rich internal logic of proteins. Since chemical kinetics�operates on states which are based on descriptions of full molecular species, often times a model becomes too complex to analyze. We�discuss a method for decomposition, abstraction and reconstruction of the stochastic semantics of rule-based systems with conserved number of agents. Abstraction is induced by counting entities that are more abstract than species,�fragments. The rule-set can be decomposed to smaller rule-sets, so that the fragment-based dynamics of the whole rule-set is exactly a composition of species-based dynamics of smaller rule-sets. The reconstruction of the transient species-based dynamics is possible for certain initial distributions. If all the rules in a rule set are reversible, the full reconstruction of the species-based dynamics is always possible at the stationary distribution. We use a case study of colloidal aggregation to demonstrate that the method can reduce the state space of the underlying Markov process exponentially (with respect to the standard species-based description).