Computational Glioscience PDF
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Springer Series in Computational Neuroscience Maurizio De Pittà Hugues Berry Editors Computational Glioscience Springer Series in Computational Neuroscience Series editors Alain Destexhe, Computationnelles, UNIC, Gif-sur-Yvette, France Romain Brette, Institut de la Vision, Paris, France Computational Neuroscience gathers monographs and edited volumes on all aspects of computational neuroscience, including theoretical and mathematical neuroscience,biophysicsofthebrain,modelsofneuronsandneuralnetworks,and methods of data analysis (e.g. information theory). The editors welcome suggestions and projects for inclusion in the series. About the Editors Alain Destexhe is Research Director at the Centre National de la Recherche Scientifique(CNRS),France,andRomainBretteisResearchDirectorattheInstitut National de la Santé et de la Recherche Médicale (INSERM), France. More information about this series at http://www.springer.com/series/8164 à Maurizio De Pitt Hugues Berry (cid:129) Editors Computational Glioscience 123 Editors Maurizio DePittà Hugues Berry Group ofMathematical, Computational INRIA Rhône-Alpes andExperimental Neuroscience UniversitédeLyon Basque Centerfor AppliedMathematics Villeurbanne, Lyon,France Bilbao, Biscay,Spain ISSN 2197-1900 ISSN 2197-1919 (electronic) SpringerSeries inComputational Neuroscience ISBN978-3-030-00815-4 ISBN978-3-030-00817-8 (eBook) https://doi.org/10.1007/978-3-030-00817-8 LibraryofCongressControlNumber:2018954856 ©SpringerNatureSwitzerlandAG2019 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Thepast30yearshavewitnessedanappraisaloftheroleofglialcells,frompassive bystandersofneuronstoactiveplayersininformationprocessingandstorageofthe brain. This possibility has spurred an increasing number of experimental and technologicaladvancestoharnessthecomplexityofglialcells,whoselargevariety is suggested to reflect an equally large diversity of functions. If experimental excitement surfs this new wave of glial research, theoretical efforts to characterize what glia do lag behind. Both the novice and the experienced theoretician may indeedbejeopardizedbythemorphologicalandfunctionalcomplexityofglialcells whichencompassawholerichnessofdynamicalinteractionswithneurons,ranging from molecular and cellular signaling to network activity and behavior. The overwhelming experimental details often make difficult to identify the appropriate level of modeling for a particular problem. On the other hand, anyone who approaches the field may also be baffled to find out that some of the most funda- mentalquestionsthatwecanaskforneurons,forexample,whatistheinput–output characteristic of a neuron, not only remain to be answered for glial cells, but may evennotbeproperlyposedinthecaseofthesecells.Inthisbook,weconsidersome oftheseopenquestionsandpresentalongpossiblemodelingapproachesthatcould yield insight into the function of glial cells in the brain. In a panorama where most of the theoretical analysis and computational approaches in neuroscience are oriented to study what neurons do or we could do with them, we emblematically entitled this book Computational Glioscience. Clearly, there is not such a thing as “glioscience” inasmuch as the study of glia is just a field of neuroscience, be it either experimental or computational. Moreover, theveryconceptofgliosciencemaybedetrimentalfortheadvancementofthefield, giventherecognitionoftheimportantfunctionsplayedbyglialcellsinassociation with neurons. Yet, we purportedly argue that “glioscience,” as a possible subdis- cipline of neuroscience, has its own reason to be defined, for the challenges that it bears, tothe experimentalistand to the theoretician, inunraveling glia complexity. In this perspective, we refer to “computational glioscience” as to the whole spec- trum of theoretical approaches and computational methods put forth to model and v vi Preface simulate glial signaling in the brain, at its multiple spatial and temporal scales of interaction, be they either among other glial cells or with neurons. Organization and Approach With the exception of the initial chapter in the introductory Part I that aims at providingageneraloverviewofsomeofthekeydebatedaspectsincurrentresearch on glia, the other chapters ofthe book mostlyfocus on astrocytes, the maintype of cortical glial cells that have been behind the momentum gained by modern glia researchinthepastthreedecades.Accordingly,thebookisorganizedintofourparts. PartII,CalciumDynamics,(Chaps.2–7)isdevotedtomodelingofcalciumsignaling inastrocytes, bothat the subcellular level of individualastrocytes (Chaps. 2–6) and the intercellular level of networks of astrocytes (Chaps. 6 and 7). Focus is on the mechanism of calcium-induced calcium release from the cell’s endoplasmic reticu- lum mediated by inositol trisphosphate, whose deterministic (Chaps. 3 and 5) and stochasticaspects(Chap.4)arebothconsideredindetail.Emphasisisthengivenon possible mechanisms of stimulus encoding of astrocytes by means of their intra- cellular calcium dynamics (Chaps. 5 and 6) and how these mechanisms could account for lateral information transfer in neuron–glial networks (Chap. 7). The interaction between astrocytes and synapses is considered in Part III with Chaps. 8 and 9 providing a detailed overview of the mechanisms of interaction by gluta- matergic and purinergic gliotransmission. Chapter 10 then presents a general frameworktomodelgliotransmissionanddiscussessomeofthepotentialfunctional implicationsofgliotransmitter-mediatedregulationofsynapticplasticity.Chapter11 instead reviews some modeling approaches to study the effect of dysfunctional astrocytic regulation of synaptic transmission in the onset of brain disorders. In Part IV, further mechanisms of interaction of astrocytes with neurons other than gliotransmission are considered. These include uptake of neurotransmitters by astrocytes (Chaps. 12 and 13), astrocyte-mediated regulation of extracellular potas- sium homeostasis (Chap. 14), and metabolic coupling between astrocytes and neu- rons (Chap. 15). Finally, Part V provides an overview of computational models (Chap. 16) and practical techniques (Chaps. 17 and 18) to analyze and simulate astrocytic calcium signaling and neuron–glial interactions in general. In an attempt to ease the approach to modeling glia, both to the reader without previousexposuretoglialbiologyandtheonewithoutacomputationalbackground, weattempted,wheneverpossible,tonarrowbiologicalinformationdowntoconcise mechanisticdetails.Atthesametime,whilewedonothesitatetoemploythelevel of analysis needed to be precise and rigorous, we review technical aspects and mathematical detailsonthederivationofmodelpresentedinthebookindedicated appendices at the end of individual chapters. In these appendices, the reader may also find details on the derivation and estimation of parameters used in the simulationsofthemodelspresentedinthebook.Thecodeforsimulationspresented in Chaps. 3, 5, 7, 10, and 18 is made freely available on the online repository associated with this book: https://github.com/mdepitta/comp-glia-book. Preface vii Acknowledgements A substantial part of this book was originally conceived during a workshop on “Computational Methods and Modeling of Astrocyte Physiology and Neuron-Glia Interactions” organized by the editors within the framework of the annual meeting oftheOrganizationforComputationalNeurosciencesinthesummerof2014.Some of the contributing authors of the book were also speakers at the workshop, and others instead were invited later to join. To all of them, we express our sincere gratitudeforinvestingtheirtime,effort,andpatiencethatultimatelymadepossible theambitiousprojectofthisbooktoturnintoreality.Wearealsoextremelygrateful to a large number of colleagues at many institutions who have painstakingly read, commented on, and critically reviewed numerous versions of all the chapters. We particularly thank Benjamin Auffart, Maxwell Gillett, Joules Lallouette, Marja-Leena Linne, Roger Min, David Ropers, Mirko Santello, Mark Sherwood, Alexander Skupin, James Sneyd, Rüdiger Thul, Yulia Tomifeeva, and Vladislav Volman. We received also significant additional advice from Alfonso Araque, David Attwell, Nicolas Brunel, Renaud Jolivet, Stéphane Oliet, Aude Panatier, Vladimir Parpura,MishaTsodyks,andAndreaVolterra.Wewouldliketodedicatethisbook tothememoryofEshelBen-Jacobwhoseexcitementforgliaignitedourintereston thetopic.MDPwishestoacknowledgethesupportoftheJuniorLeaderFellowship Program by “la Caixa” Banking Foundation (LCF/BQ/LI18/11630006), as well as thesupportbytheBasqueGovernmentthroughtheBERC2018–2021programand by the Spanish Ministry of Science, Innovation and Universities: BCAM Severo Ochoa accreditation SEV-2017-0718. During the writing of this book MDP was also supported by the European Commission by an Alain Bensoussan Fellowship from the European Research Consortium for Informatics and Mathematics, and by an International Outgoing Fellowship from the Marie Skłodowska-Curie Actions. Finally, we apologize to anyone we have inadvertently omitted from these lists. Bilbao, Bizkaia, Spain Maurizio De Pittà Lyon, France Hugues Berry November 2018 Contents Part I Introduction 1 A Neuron–Glial Perspective for Computational Neuroscience. . . . . 3 Maurizio De Pittà and Hugues Berry Part II Calcium Dynamics 2 Data-Driven Modelling of the Inositol Trisphosphate Receptor (IP R) and its Role in Calcium-Induced 3 Calcium Release (CICR). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Ivo Siekmann, Pengxing Cao, James Sneyd and Edmund J. Crampin 3 Intracellular Calcium Dynamics: Biophysical and Simplified Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Yulia Timofeeva 4 Modeling of Stochastic Ca2+ Signals . . . . . . . . . . . . . . . . . . . . . . . . 91 Sten Rüdiger and Jianwei Shuai 5 G Protein-Coupled Receptor-Mediated Calcium Signaling in Astrocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Maurizio De Pittà, Eshel Ben-Jacob and Hugues Berry 6 Emergence of Regular and Complex Calcium Oscillations by Inositol 1,4,5-Trisphosphate Signaling in Astrocytes . . . . . . . . . 151 Valeri Matrosov, Susan Gordleeva, Natalia Boldyreva, Eshel Ben-Jacob, Victor Kazantsev and Maurizio De Pittà ix x Contents 7 Astrocyte Networks and Intercellular Calcium Propagation . . . . . . 177 Jules Lallouette, Maurizio De Pittà and Hugues Berry Part III Tripartite Synapse and Regulation of Network Activity 8 Gliotransmission at Tripartite Synapses . . . . . . . . . . . . . . . . . . . . . 213 Candela González-Arias and Gertrudis Perea 9 Purinergic Signaling at Tripartite Synapses . . . . . . . . . . . . . . . . . . 227 Anup Pillai and Suhita Nadkarni 10 Gliotransmitter Exocytosis and Its Consequences on Synaptic Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Maurizio De Pittà 11 Computational Models of Pathophysiological Glial Activation in CNS Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Vladislav Volman and Maxim Bazhenov Part IV Homeostasis and Metabolic Coupling 12 The Role of Astrocytes in Neurotransmitter Uptake and Brain Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Annalisa Scimemi 13 Glutamate Uptake by Astrocytic Transporters . . . . . . . . . . . . . . . . 329 KonstantinMergenthaler,FranziskaOschmannandKlausObermeyer 14 Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Geir Halnes, Klas H. Pettersen, Leiv Øyehaug, Marie E. Rognes and Gaute T. Einevoll 15 Constraint-Based Modeling of Metabolic Interactions in and Between Astrocytes and Neurons. . . . . . . . . . . . . . . . . . . . . 393 Tunahan Çakır Part V Computational Tools to Analyze and Model Astrocyte Experiments 16 Computational Models of Astrocytes and Astrocyte–Neuron Interactions: Characterization, Reproducibility, and Future Perspectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 Tiina Manninen, Riikka Havela and Marja-Leena Linne
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