Information Processing Letters
BioAmbients: an abstraction for biological compartments
Theoretical Computer Science - Special issue: Computational systems biology
Formal Molecular Biology Done in CCS-R
Electronic Notes in Theoretical Computer Science (ENTCS)
Scalable simulation of cellular signaling networks
APLAS'07 Proceedings of the 5th Asian conference on Programming languages and systems
Abstract interpretation of cellular signalling networks
VMCAI'08 Proceedings of the 9th international conference on Verification, model checking, and abstract interpretation
Beta binders for biological interactions
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
Transactions on Computational Systems Biology VII
Rule-based modelling of cellular signalling
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
Investigation of a Biological Repair Scheme
Membrane Computing
Modelling Epigenetic Information Maintenance: A Kappa Tutorial
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Stochastic modelling and simulation of mobile systems
Graph transformations and model-driven engineering
Lumpability abstractions of rule-based systems
Theoretical Computer Science
Complex functional rates in rule-based languages for biochemistry
Transactions on Computational Systems Biology XIV
Self-assembly models of variable resolution
Transactions on Computational Systems Biology XIV
Complex Functional Rates in the Modeling of Nano Devices (Extended Abstract)
Electronic Notes in Theoretical Computer Science (ENTCS)
Topological computation of activity regions
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
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Rule-based modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexes -i.e., not to have to specify all the traits of the entitities partaking in a reaction but just those that matter- is the key to obtaining compact descriptions of what otherwise could be nearly infinite dimensional dynamical systems. This also makes these descriptions easier to read, write and modify.In the course of modelling a particular signalling system it will often happen that more traits matter in a given interaction than previously thought, and one will need to strengthen the conditions under which that interaction may happen. This is a process that we call rule refinementand which we set out in this paper to study. Specifically we present a method to refine rule sets in a way that preserves the implied stochastic semantics.This stochastic semantics is dictated by the number of different ways in which a given rule can be applied to a system (obeying the mass action principle). The refinement formula we obtain explains how to refine rules and which choice of refined rates will lead to a neutral refinement, i.e., one that has the same global activity as the original rule had (and therefore leaves the dynamics unchanged). It has a pleasing mathematical simplicity, and is reusable with little modification across many variants of stochastic graph rewriting. A particular case of the above is the derivation of a maximal refinement which is equivalent to a (possibly infinite) Petri net and can be useful to get a quick approximation of the dynamics and to calibrate models. As we show with examples, refinement is also useful to understand how different subpopulations contribute to the activity of a rule, and to modulate differentially their impact on that activity.