Scalable simulation of cellular signaling networks

Vincent Danos1

Description

Niveau: Supérieur
Scalable simulation of cellular signaling networks Vincent Danos1,4?, Jerome Feret3, Walter Fontana1,2, and Jean Krivine5 1 Plectix Biosystems 2 CNRS, Universite Denis Diderot 3 Harvard Medical School 4 Ecole Normale Superieure 5 Ecole Polytechnique Abstract. Given the combinatorial nature of cellular signalling path- ways, where biological agents can bind and modify each other in a large number of ways, concurrent or agent-based languages seem particularly suitable for their representation and simulation [1–4]. Graphical mod- elling languages such as ? [5–8], or the closely related BNG language [9– 14], seem to afford particular ease of expression. It is unclear however how such models can be implemented.6 Even a simple model of the EGF receptor signalling network can generate more than ???? non-isomorphic species [5], and therefore no approach to simulation based on enumerating species (beforehand, or even on-the-fly) can handle such models without sampling down the number of potential generated species. We present in this paper a radically different method which does not at- tempt to count species. The proposed algorothm uses a representation of the system together with a super-approximation of its ‘event horizon' (all events that may happen next), and a specific correction scheme to obtain exact timings. Being completely local and not based on any kind of enu- meration, this algorithm has a per event time cost which is independent of (i) the size of the set of generable species (which can even be infinite),

• distribution function


• rule rate


• count species


• map specifies


• method based


• graph-rewriting framework


• species beforehand


• simulation algorithm

Sujets

• Denis Diderot
• École polytechnique
• Distribution function

Informations

• Publié par: chaeh
• Nombre de lectures: 17
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Scalablesimulationofcellularsignalingnetworks VincentDanos 1,4? ,Je´roˆmeFeret 3 ,WalterFontana 1,2 ,andJeanKrivine 5 1 PlectixBiosystems 2 CNRS,Universite´DenisDiderot 3 HarvardMedicalSchool 4 E ´ coleNormaleSupe´rieure 5 E ´ colePolytechnique Abstract.Giventhecombinatorialnatureofcellularsignallingpath- ways,wherebiologicalagentscanbindandmodifyeachotherinalarge numberofways,concurrentoragent-basedlanguagesseemparticularly suitablefortheirrepresentationandsimulation[1–4].Graphicalmod- ellinglanguagessuchasκ[5–8],orthecloselyrelatedBNGlanguage[9– 14],seemtoaffordparticulareaseofexpression.Itisunclearhowever howsuchmodelscanbeimplemented. 6 EvenasimplemodeloftheEGF receptorsignallingnetworkcangeneratemorethan  non-isomorphic species[5],andthereforenoapproachtosimulationbasedonenumerating species(beforehand,orevenon-the-fly)canhandlesuchmodelswithout samplingdownthenumberofpotentialgeneratedspecies. Wepresentinthispaperaradicallydifferentmethodwhichdoesnotat- tempttocountspecies.Theproposedalgorothmusesarepresentationof thesystemtogetherwithasuper-approximationofits‘eventhorizon’(all eventsthatmayhappennext),andaspecificcorrectionschemetoobtain exacttimings.Beingcompletelylocalandnotbasedonanykindofenu- meration,thisalgorithmhasapereventtimecostwhichisindependent of(i)thesizeofthesetofgenerablespecies(whichcanevenbeinfinite), and(ii)independentofthesizeofthesystem(ie,thenumberofagent instances).Weshowhowtorefinethisalgorithm,usingconceptsderived fromtheclassicalnotionofcausality,sothatinadditiontotheaboveone alsohasthattheevencostisdepending(iii)onlylogarithmicallyonthe sizeofthemodel(ie,thenumberofrules).Suchcomplexityproperties reflectinourimplementationwhich,onacurrentcomputer,generates  abouteventsperminuteinthecaseofthesimpleEGFreceptormodel  mentionedabove,usingasystemwithagents. 1Introduction Animportantthreadofworkinsystemsbiologyconcernsthemodellingofthe intra-cellularsignallingnetworkstriggeredbyextra-cellularstimuli(suchashor- monesandgrowthfactors).Suchnetworksdeterminegrowth,differentiation,and ? ThisresearchwaspartlysupportedbytheNIH/NIGMSgrantR43GM81319-01. 6 Eg,fromRef.[15,p.4]:“programsimplementingthesemethodsincludeStochSim, BioNetGen,andMoleculizer.However,atthepresenttimeonlyapartoftheentire EGFRnetworkcanbeanalyzedusingtheseprograms”.