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Verification and Simulation of Dynamic Systems

Verification and Simulation of Dynamic Systems. Dagstuhl Seminar 17-22.4.2006. Corrado Priami, Hanne Riis Nielson, David Nicol, Harald Ruess, Adelinde Uhrmacher. Motivation. Bringing together different communities: Simulation

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Verification and Simulation of Dynamic Systems

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  1. Verification and Simulation of Dynamic Systems Dagstuhl Seminar 17-22.4.2006 Corrado Priami, Hanne Riis Nielson, David Nicol, Harald Ruess, Adelinde Uhrmacher

  2. Motivation Bringing together different communities: • Simulation • E.g. experimental design, hybrid, continuous, discrete simulation, optimization, sensitivity-perturbation analysis • Verification • E.g. explicit state enumeration, symbolic simulation, model checking, static program analysis and theorem proving • Basic ground: • Need to identify and define models • Development of component-based and abstraction-based validation techniques to deal with complexity

  3. Bringing together • Application areas: • Networks, concurrent programming systems • Systems biology • On first sight they seem to have a lot in common however, how on second glance?

  4. Categorization of models Zeigler, Praehofer, Kim 2000

  5. … Simplification Description/Example Simplification-Method Aggregation Combining groups of components into a single component that represents their combined behaviour, e.g. from micro to macro Leaving out: components, variables, interactions Omission Deterministic→ stochastic Deterministic algorithms with many factors are replaced by a distribution Replacing a distribution by its mean Stochastic→ deterministic

  6. after expRandom(unbindingT) free repressing removeCouplingTo(Operon) after deacTime [deacTime < reprTime] Trp accept / / reject / removeCouplingTo(Operon) active deacTime = expRandom(deactT) reprTime = expRandom(reprT) trying to dock after reprTime [reprTime  deacTime] / addCouplingTo(Operon) Trp dock Repressor Trp Trp undock / Modelling A Repressor as StateChart

  7. Repressor ::= (trp(c), reactionT) . ((c, releaseT). Repressor + (( freeRepr)(dock(freeRepr),reprT). (freeRepr, unbindingT) . (c, releaseT). Repressor)) Tryptophan ::= (( release) (trp(release), reactionT) . (release, releaseT) . Tryptophan Operator ::= ((rnapOp,opForwardT). (Transcribing | generate, generationT) | Operator)) + ((dock(d), reprT). (d,unbindingT) . Operator) Modelling a Repressor in Stochastic 

  8. Properties of interest • What parameters or component constellation does or does not lead to certain behaviour: • Reachability • Deadlock / lifelock • Causality • Oscillation properties • Stochastic bursts • … • Supporting model validation

  9. Questions • How does and how can simulation help verification and vice versa? • What success or failure stories do exist and what can be learned from them? • Determining adequate modelling techniques: quantitative modelling for simulation and qualitative modelling for verification, is this still true? • How to construct adequate abstractions for effectively answering key questions about dynamic systems, as e.g. biological systems or network systems? • Is a model for verification necessarily more abstract than that for simulation purposes, how to relate these different models? • When to use synthesis and verification, when to use simulation or are these the wrong questions? • How can we validate the success or failure of simulation and verification applications?

  10. .. continued • How do biological applications differ from technical ones with respect to modelling methods, evaluation methods, and the questions to be asked? Do specific requirements exist? • How are e.g. network modelling and biological modelling, simulation, analysis, and verification related? • How are modelling, analysis, or verification of hybrid systems and discrete event systems related? What can be learned from each other? • Should the different meanings of abstraction and refinement in simulation and verification be reconciled?

  11. … continued • Should some benchmark problems be defined to evaluate methods/tools? If so – which features should these benchmark problems have? • How can the modelling approximations made by domain experts be represented and accounted for in simulation and formal methods? • What are the required properties/features of the stochastic process algebra that allow to capture interesting and commonly occurring features of dynamic systems, e.g. . • binary component communication (CSS) versus n-ary CSP style communication, • dynamic versus fixed parallel component structure (pi versus pepa)?

  12. Questions updated • Process algebra’s communication pattern, cp. State charts, relation to verification and simulation (see 14) • Abstraction methods, refinements (see 11, 5) Overview of state of the art, using verification and simulation what are the basic modelling formalisms and what are the techniques used (see 2, 3, 6, 12) • Possible synergy (see 1, 9, 10) • Biological application somewhat different, specific requirements (see 8, 13) • Benchmark problems (see 7, 2, 12)

  13. Organization – General • 9 a.m. starting • Two coffee breaks • 12:10 a.m. lunch • 6:30 p.m. dinner • Afternoon dedicated to working groups • One evening panel discussion – Thursday / Friday evening ? • 5:00 p.m. summary of working groups, preparation of new ones • Saturday: summary of results and plans for exploitation and dissemination • Excursion: when and what ? • Thursday ? • Walking or to Trier?

  14. Tuesday • Introduction • Component-based modelling and simulation • Verification of simulation models • Hybrid Systems • Advanced Technologies to Accelerate Mixed Signal Simulation • Hybrid systems verification by constraint propagation-based abstraction refinement • Formal verification of hybrid models of genetic regulatory networks • Model checking of hybrid systems from reachability towards stability

  15. Wednesday • Overview talks • Systems Biology • Network simulation performance optimizations, and the need for validation • Networks application • Formal analysis of network simulations • Abstract Interpretation of Graph Transformation • Fluid approximation of PEPA models • Guiding Simulation by model checking

  16. Thursday • Why I am always late: using stochastic process algebras to model the circadian clock. • Simulation of bacterial transcription and translation in the pi calculus • Biological validation as model checking • Context dependent analysis of Bio-Ambients • Artificial biochemistry • Evening panel discussion – structuring of report

  17. Friday • Modelling and simulating biological processes with stochastic multi-set rewriting • Verifying spatially explicit simulation of virulence evolution Via analytical models • From pi-calculus to differential equations and return • Experiences with verification of dynamic systems • …

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