Transactions on Computational Systems Biology XI PDF
Preview Transactions on Computational Systems Biology XI
Lecture Notes in Bioinformatics 5750 EditedbyS.Istrail,P.Pevzner,andM.Waterman EditorialBoard: A.Apostolico S.Brunak M.Gelfand T.Lengauer S.Miyano G.Myers M.-F.Sagot D.Sankoff R.Shamir T.Speed M.Vingron W.Wong Subseries of Lecture Notes in Computer Science Corrado Priami Ralph-Johan Back Ion Petre (Eds.) Transactions on Computational Systems Biology XI 1 3 SeriesEditors SorinIstrail,BrownUniversity,Providence,RI,USA PavelPevzner,UniversityofCalifornia,SanDiego,CA,USA MichaelWaterman,UniversityofSouthernCalifornia,LosAngeles,CA,USA Editor-in-Chief CorradoPriami TheMicrosoftResearch-UniversityofTrento CentreforComputationalandSystemsBiology PiazzaManci,17,38050Povo(TN),Italy E-mail:[email protected] GuestEditors Ralph-JohanBack IonPetre ÅboAkademiUniversity DepartmentofInformationTechnologies Joukahaisenkatu3-5,20520Turku,Finland E-mail:{backrj,ipetre}@abo.fi LibraryofCongressControlNumber:2009933672 CRSubjectClassification(1998):J.3,F.1,F.2,I.6,I.2,C.1.3 ISSN 0302-9743(LectureNotesinComputerScience) ISSN 1861-2075(TransactionsonComputationalSystemsBiology) ISBN-10 3-642-04185-XSpringerBerlinHeidelbergNewYork ISBN-13 978-3-642-04185-3SpringerBerlinHeidelbergNewYork Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting, reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965, initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable toprosecutionundertheGermanCopyrightLaw. springer.com ©Springer-VerlagBerlinHeidelberg2009 PrintedinGermany Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SPIN:12743292 06/3180 543210 Preface Biology is witnessing a transformation towards a more quantitative science, based on the major technological breakthroughs of the past decade. In this transformation,biology is incorporatingmathematical modeling techniques and computational approaches towards numerical simulations, model analysis, and quantitativepredictions.Animportantgoalistoformalizeandanalyzetheever- changing inter-connections between components (often on different time and space scales), their influence on one another, regulatory patterns, alternative pathways, etc. Formal reasoning rather than empirical observations is the main drivingforceinthisnewtypeofbiologicalresearch.Atthesametime,computer science and applied mathematics are faced with considerable methodological challenges in handling an unprecedented level of concurrency, stochastic effects, amixoflargeandsmallpopulations,combinatorialexplosionsinthestatespace, model refinement, and model (de)composition, etc. ThisspecialissueofTransactionsonComputationalSystemsBiologyonCom- putationalModelsforCellProcessesisbasedonaworkshopwiththesamename that took place in Turku, Finland, on May 27, 2008. The workshop was orga- nized as a satellite event of the 15th International Symposium on Formal Meth- ods that took place in Turku in the period May 28-31, 2008. This special issue however had an open call for paper submissions, with a separate peer-review process. The accepted papers span an interesting mix of approaches to systems biology,rangingfrom quantitative to qualitative techniques, from continuous to discrete mathematics, fromdeterministic to stochastic methods, fromcomputa- tional models for biology to computing paradigms inspired by biology. Overall, they give a good glimpse into some of the exciting current research avenues in computational systems biology. This volume also contains three regular submissions that deal with the rela- tionships between ODEs andstochastic concurrentconstraintprogramming(by Bertolussi and Policriti), with the equilibrium points of genetic regulatory net- works(byChesi),andwithprobabilitymodelsdescribinghowepigeneticcontext affects gene expression and organismaldevelopment (by Wallace and Wallace). July 2009 Ralph-Johan Back Ion Petre Corrado Priami LNCS Transactions on Computational Systems Biology – Editorial Board Corrado Priami, Editor-in-chief University of Trento, Italy Charles Auffray Genexpress, CNRS and Pierre & Marie Curie University, France Matthew Bellgard Murdoch University, Australia Soren Brunak Technical University of Denmark, Denmark Luca Cardelli Microsoft Research Cambridge, UK Zhu Chen Shanghai Institute of Hematology, China Vincent Danos CNRS, University of Paris VII, France Eytan Domany Center for Systems Biology, Weizmann Institute, Israel Walter Fontana Santa Fe Institute, USA Takashi Gojobori National Institute of Genetics, Japan Martijn A. Huynen Center for Molecular and Biomolecular Informatics The Netherlands Marta Kwiatkowska University of Birmingham, UK Doron Lancet Crown Human Genome Center, Israel Pedro Mendes Virginia Bioinformatics Institute, USA Bud Mishra Courant Institute and Cold Spring Harbor Lab, USA Satoru Miayano University of Tokyo, Japan Denis Noble University of Oxford, UK Yi Pan Georgia State University, USA Alberto Policriti University of Udine, Italy Magali Roux-Rouquie CNRS, Pasteur Institute, France Vincent Schachter Genoscope, France Adelinde Uhrmacher University of Rostock, Germany Alfonso Valencia Centro Nacional de Biotecnologa,Spain Table of Contents Computational Models for Cell Processes Process Algebra Modelling Styles for Biomolecular Processes .......... 1 Muffy Calder and Jane Hillston Simple, Enhanced and Mutual Mobile Membranes.................... 26 Bogdan Aman and Gabriel Ciobanu Bio-PEPAwith Events ........................................... 45 Federica Ciocchetta In Silico Modelling and Analysis of Ribosome Kinetics and aa-tRNA Competition..................................................... 69 D. Boˇsnaˇcki, T.E. Pronk, and E.P. de Vink Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway ............................. 90 Maria Luisa Guerriero Rule-Based Modelling and Model Perturbation ...................... 116 Vincent Danos, J´eroˆme Feret, Walter Fontana, Russ Harmer, and Jean Krivine Extended Stochastic Petri Nets for Model-Based Design of Wetlab Experiments..................................................... 138 Monika Heiner, Sebastian Lehrack, David Gilbert, and Wolfgang Marwan A Projective Brane Calculus with Activate, Bud and Mate as Primitive Actions......................................................... 164 Maria Pamela C. David, Johnrob Y. Bantang, and Eduardo R. Mendoza Accepting Networks of Non-inserting Evolutionary Processors ......... 187 Ju¨rgen Dassow and Victor Mitrana Discrete Modeling of Biochemical Signaling with Memory Enhancement.................................................... 200 John Jack and Andrei P˘aun Dynamical Systems and Stochastic Programming: To Ordinary Differential Equations and Back ................................... 216 Luca Bortolussi and Alberto Policriti VIII Table of Contents Computing Equilibrium Points of Genetic Regulatory Networks........ 268 Graziano Chesi Code, Context, and Epigenetic Catalysis in Gene Expression .......... 283 Rodrick Wallace and Deborah Wallace Author Index.................................................. 335 Process Algebra Modelling Styles for Biomolecular Processes Muffy Calder1 and Jane Hillston2 1 Department of Computing Science, University of Glasgow, Glasgow G12 8QQ,Scotland 2 Laboratory for Foundationsof ComputerScience and Centre for SystemsBiology, Edinburgh The Universityof Edinburgh,Edinburgh EHA 9AB, Scotland Abstract. We investigate how biomolecular processes are modelled in process algebras, focussing on chemical reactions. We consider various modelling styles and how design decisions made in the definition of the processalgebrahaveanimpactonhowamodellingstylecanbeapplied. Ourgoalistohighlighttheoftenimplicitchoicesthatmodellersmakein choosing a formalism, and illustrate, through the use of examples, how this can affect expressability as well as the type and complexity of the analysis that can be performed. 1 Introduction Much recent researchhas considered the problem of providing suitable abstract models to allow biologists to construct mechanistic models to enhance under- standingofbiomolecularprocesses.Processalgebras,formalmodellinglanguages originallyconceivedformodellingconcurrentcomputations,havebeenwidelyap- plied, most notably in the area of signalling pathways [RSS01, CGH06, TK08]. This is experimental science and we are currently evaluating the hypothesis that such formal models can add value to the mathematical analysis that is al- ready undertaken within systems biology in terms of ordinary differential equa- tion (ODE) models or stochastic simulations directly. In exploring this goal, even within work on process algebras, several different styles of modelling have emerged.Ultimatelywehopetobeabletogiveguidanceonhowtochooseamong these modelling styles,oronhowto mapmolecularcomponentsandtheir inter- actionsto processes,processcommunicationandprocesscomposition.However, in the first instance we investigate how design decisions made in the definition of the language have an impact on how a modelling style can be applied, and highlighttheoftenimplicitchoicesthatmodellersmakeinchoosingaformalism. Recent research effort on process algebras for biomolecular processes, e.g. [CGH06, CVOG06, CH08, Car08], has focussed on defining alternative seman- tics, such as discrete-state (stochastic) or continuous-state (ODE) semantics. These provide important links with the work where mathematical representa- tionsareuseddirectlyandestablishavalidfoundationforprocessalgebramod- els. Based on these semantics, analysis may be carried out by model-checking, C.Priamietal.(Eds.):Trans.onComput.Syst.Biol.XI,LNBI5750,pp.1–25,2009. (cid:2)c Springer-VerlagBerlinHeidelberg2009 2 M. Calder and J. Hillston stochastic simulation based on Gillespie’s algorithm or ODE simulations. Our emphasis in this paper is different. Here we consider the forms of abstraction supported by process algebra and how the abstraction and the process algebra chosen affect the expressiveness of the model with respect to the biologicalpro- cesses,aswellasthe typeandcomplexityoftheanalysisthatcanbeperformed. Wefocusononeofthemostimportanttypesofinteractionbetweenmolecular components: chemical reactions. In chemical notation, these may be first order reactions,forexampleAdegradestoB:A−k→1 B,orsecondorderreactions,forex- ampleAandBcombinetoformC orC andD:A+B−k→2 C,orA+B−k→3 C+D. Typically, k1...k3 are rate constants for kinetic laws (e.g. mass action). A fundamental aspect of the abstraction used in modelling is the nature of the process mapping. In the literature on process algebras for systems biology we find predominantly the molecule-as-process [RSS01, Car08] abstraction, but thespecies-as-processandreaction-as-process mappingshavealsobeenproposed [CGH06,CH08,BP08].The distinctionbetweenthe firsttwocanbe understood by appealing to ecology: the former is essentially individuals-based, whereas the latter is population-based. We note that this distinction is less common in distributed computing system modelling, the origins of process algebra, where population-based models are rarely considered. Further stylistic differentiation was identified in [CGH06] where the concepts of reagent-centric and pathway-centric models are introduced, in the context of population-based modelling. Reagent-centric models map all reagents in a re- action to processes, whose variation reflect decrease through consumption and increase through product formation (consumers and producers). Reagents such as modifiers that do not vary species amounts can also be modelled in this ap- proach.Reagent-centricmodelsprovideafine-grained,distributedviewofasys- tem.Pathway-centricmodelsprovideamoreabstractviewofasystem,tracking serialisations of events, which are then composed concurrently. Here, processes vary according to their biologicalstate rather than their quantity. Whereas in a reagent-centricapproachtheprocessesmaybemoleculesormolecularspecies,in thepathway-centricapproachtheprocessesaremoleculesorsub-pathways.Thus the interactions between processes are between flows of events corresponding to producers, i.e. components on the left hand sides of a reactions. Mostmodellingapproachesmapchemical reactions toevents inastraightfor- wardway,andmap(possiblyasubsetof)thechemical componentstoprocesses. Bortolussi and Policriti’s work on sCCP, using the reaction-as-process abstrac- tion,isanexceptiontothis.Whenchemicalcomponentsaremappedtoprocesses within the reagent-centric approach there is a further choice: between associat- ing processes with all components or only with the reagents on the left hand side of equations, i.e. those reagents that are the reactants of the reaction. To distinguish these two cases, we call the former reagent-centric and the latter reactant-centric. This modelling choice is often influenced by the form of syn- chronisation available within the algebra: binary or multi-way. If we have only the former, then only the reactant-centric approach is possible and we are left
The list of books you might like
