Stochastic modelling for systems biology

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessaPublished titlesAlgorithms in Bioinformatics: A Practical Exactly Solvable Models of BiologicalIntroductionInvasionWing-Kin SungSergei V Petrovskii and Bai-Lian LiBioinformatics: A Practical ApproachGene Expression Studies UsingShui Qing reAffymetrix MicroarraysBiological ComputationHinrich gohlmann and willem tallonEhud Lamm and ron ungerGlycome Informatics: Methods andBiological Sequence Analysis UsingApplicationsthe segAn C++ LibKiyoko F Aoki-KinoshitaAndreas Gogol-Doring and Knut ReinertHandbook of hidden mar koy modelsCancer Modelling and simulationin BioinformaticsLuigi preziosiMartin GalleryCancer Systems Biologyntroduction to BioinformaticsEdwin WangAnna tramontanoCell Mechanics: From Single ScaleIntroduction to Bio-OntologiesBased Models to Multiscale ModelingPeter N, robinson and sebastian bauerArnaud Chauviere, Luigi Preziosi,ntroduction to Computationaland claude verdierProteomicsClustering in Bioinformatics and drugGolan yonaDiscoveryIntroduction to proteins: structureJohn D MacCuish and Norah e MacCuish Function and MotionCombinatorial Pattern MatchingAmit Kesse/ and nir Ben-TalAlgorithms in Computational BiologyAn Introduction to Systems Biology:Using Perl and RDesign Principles of Biological CircuitsGabriel valienteUri AlonComputational Biology: A StatisticalKinetic Modelling in Systems BiologyMechanics PerspectiveOleg demin and gor goryaninRalf BlosseyKnowledge Discovery in ProteomicsComputational Hydrodynamics ofgor Jurisica and Dennis WigleCapsules and Biological CellsMeta-analysis and combiningC PozrikidisInformation in Genetics and GenomicsComputational Neuroscience:Rudy guerra and darlene R. goldsteinA Comprehensive ApproachMethods in medical informatics.Jianfeng FengFundamentals of healthcareData Analysis Tools for DNA Microarrays Programming in Perl, Python, and RubySorin DraghiciJules bermanDifferential Equations and Mathematical Modeling and simulation of capsulesBiology, Second Editionand Biological cellsD.S. Jones. M.. Plank, and B D SleemanC PozrikidisDynamics of Biological SystemsNiche Modeling: Predictions fromMichael smallStatistical distributionsEngineering Genetic CircuitsDavid stockwellChris J. MyersK11715 FM indd 310/3/1110:33AMPublished Titles(continued)Normal Mode Analysis: Theory andStatistics and Data Analysis forApplications to Biological and Chemical Microarrays Using R and BioconductorSystemsSecond editionQiang Cui and /vet BaharSorin DraghiciOptimal Control Applied to BiologicalStochastic Modelling for SystemsModelsBiology, Second EditionSuzanne lenhart and John t workmanDarren. wilkinsonPattern Discovery in BioinformaticsStructural Bioinformatics: An AlgorithmicTheory AlgorithmsApproachLaxmi paridaForbes burkowskiPython for BioinformaticsThe Ten Most wanted solutions inSebastian bassiProtein bioinformaticsSpatial EcologyAnna tramontanoStephen Cantrell, Chris Cosner, andShigui RuanSpatiotemporal Patterns in Ecologyand Epidemiology: Theory, Modelsand simulationHorst Malchow, Sergei V Petrovski, andEzio venturinoK11715 FM indd 410/3/1110:33AMChapman hall/ CRC Mathematical and Computational Biology SeriesStochastic Modellingfor Systems BiologySECOND EDITIONDarren. WilkinsonCRC) CRC PressBoca Raton London New YorkCRC Press is an imprint of theTaylor Francis Group, an informa businessa chapman hall bookK11715 FM indd 510/3/1110:33AMCRC PressTaylor Francis Group6000 Broken Sound parkway NW, Suite 300Boca raton, Fl 33487-2742o 2012 by Taylor Francis Group, LLCCRC Press is an imprint of Taylor Francis Group, an Informa businessNo claim to original U.S. Government worksVersion date: 2011926International Standard Book Number-13: 978-1-4398-3776-4(eBook-PDFThis book contains information obtained from authentic and highly regarded sources. Reasonable effortshave been made to publish reliable data and information, but the author and publisher cannot assumeresponsibility for the validity of all materials or the consequences of their use. The authors and publishershave attempted to trace the copyright holders of all material reproduced in this publication and apologize tocopyright holders if permission to publish in this form has not been obtained. If any copyright material hasnot been acknowledged please write and let us know so we may rectify in any future reprintExcept as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter inventedincluding photocopying, microfilming, and recording, or in any information storage or retrieval systemwithout written permission from the publishersForpermissiontophotocopyorusematerialelectronicallyfromthisworkpleaseaccesswww.copyrightcom(http://www.copyright.com/)orcontacttheCopyrightClearanceCenter,Inc.(ccc),222RosewoodDrive, Danvers, MA,978-750-8400. CCC is a not-for-profit organization that provides licenses andregistration for a variety of users. For organizations that have been granted a photocopy license by the CCC,a separate system of payment has been arrangedTrademark Notice: Product or corporate names may be trademarks or registered trademarks, and are usedonly for identification and explanation without intent to infringeVisit the Taylor Francis Web site athttp://www.taylorandfrancis.comand the crc press Web site athttp://www.crcpress.comContentsList of tableList of figureXIIIAuthor biographyAcknowledgementsPreface to the second editionPreface to the first editionXXYI Modelling and networks1 Introduction to biological modelling1.1 What is modelling?1.2 Aims of modelling1.3 Why is stochastic modelling necessary?1334491. 4 Chemical reactions1.5 Modelling genetic and biochemical networks101.6 Modelling higher-level systems181.7Eⅹ excises208 Further reading202 Representation of biochemical networks212. 1 Coupled chemical reactions212.2 Graphical representations212. 3 Petri nets22. 4 Stochastic process algebras2.5 Systems Biology Markup Language (SBML)362.6 SBML-shorthand412.7 Exercises2. 8 Further readingCONTENTSi Stochastic processes and simulation3 Probability models513. 1 Probabilit3.2 Discrete probability models3.3 The discrete uniform distribution703. 4 The binomial distribu713. 5 The geometric distribution3. 6 The Poisson distribution743.7 Continuous probability models3. 8 The uniform distribution3.9 The exponential distribution3.10 The normal/ Gaussian distribution3.1 The gamma distribution3. 12 Quantify3. 14 Further reading4 Stochastic simulation4 Introducti994.2 Monte Carlo integration4.3 Uniform random number generation1004.4 Trans formation method1014.5 Lookup methods1064.6 Rejection samplers1074. 7 Importance resampling4.8 The Poisson process4.9 Using the statistical programming language, R4.10 Analysis of simulation output1184.11 Exercises1204.12 Further reading5 Markov processes1235.1 Introduction1235. 2 Finite discrete time markov chains1235.3 Markov chains with continuous state-space1305. 4 Markov chains in continuous time1371.5 Diffusion processes1525.6 Exercises1665. 7 Further readinii Stochastic chemical kinetics1696 Chemical and biochemical kineticsl716.1 Classical continuous deterministic chemical kinetics171CONTENTS6.2 Molecular6. 3 Mass-action stochastic kinetics1806. 4 The gillespie algorithm1826. 5 Stochastic Petri nets (SPNS)1836.6 Structuring stochastic simulation codes1866.7 Rate constant conversion1896.8 Kolmogorov's equations and other analytic representations1946. 9 Software for simulating stochastic kinetic networks1996.10 Exercises200urther reading2007 Case studies2037.1 Introduction2037.2 Dimerisation kinetics73 Michaelis-Menten enzvme kinetics2084 An auto-regulatory genetic network2127.5 The lac operon2177. 6 Exercises2197.7 Further reading8 Beyond the Gillespie algorithm2218.1 Introduction8.2 Exact simulation method2218.3 Approximate simulation strategies8. 4 hybrid simulation strategies8.5 Exercis8. 6 Further readi245Iv Bayesian inference2479 Bayesian inference and MCMc9.1 Likelihood and Bayesian inference2499. 2 The gibbs sampler2549. 3 The Metropolis-Hastings algorithm2649. 4 Hybrid mCMc schemes2689.5 Metropolis-Hastings algorithms for Bayesian inference2699.6 Bayesian inference for latent variable models2709.7 Alternatives to memo2749. 8 Exercises2759.9 Further reading27510 Inference for stochastic kinetic models2770.1 Introduction10.2 Inference given complete data10.3 Discrete-time observations of the system state28110.4 Diffusion approximations for inference10.5 Likelihood-free method29210.6 Network inference and model comparison10. Exercises30910.8 Further reading31011 Conclusions311Appendix A SBML Models315A 1 Auto-regulatory network315A 2 Lotka-Volterra reaction system318A3 Dimerisation -kinetics model319References323Index331