Table Of ContentLecture 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
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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
