Skip to main content
SpringerLink
Log in

Computational Models of Astrocyte Function at Glutamatergic Synapses

  • Protocol
  • First Online:
New Technologies for Glutamate Interaction

Part of the book series: Neuromethods ((NM,volume 2780))

Abstract

At tripartite synapses, astrocytes are in close contact with neurons and contribute to various functions, from synaptic transmission, maintenance of ion homeostasis, and glutamate uptake to metabolism. However, disentangling the precise contribution of astrocytes to those phenomena and the underlying biochemical mechanisms is remarkably challenging. This notably results from their highly ramified morphology, the nanoscopic size of the majority of astrocyte processes, and the poorly understood information encoded by their spatiotemporally diverse calcium signals. This book chapter presents selected computational models of the involvement of astrocytes in glutamatergic transmission. The goal of this chapter is to present representative models of astrocyte function in conjunction with the biological questions they can investigate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Araque A, Parpura V, Sanzgiri RP et al (1999) Tripartite synapses: glia, the unacknowledged partner. Trends Neurosci 22:208–215

    Article  CAS  PubMed  Google Scholar 

  2. Semyanov A, Henneberger C, Agarwal A (2020) Making sense of astrocytic calcium signals — from acquisition to interpretation. Nat Rev Neurosci 21:551–564

    Article  CAS  PubMed  Google Scholar 

  3. Verkhratsky A, Nedergaard M (2018) Physiology of astroglia. Physiol Rev 98:151

    Article  Google Scholar 

  4. Arizono M, Nägerl UV (2022) Deciphering the functional nano-anatomy of the tripartite synapse using stimulated emission depletion microscopy. Glia 70:607–618

    Article  PubMed  Google Scholar 

  5. Oschmann F, Berry H, Obermayer K et al (2018) From in silico astrocyte cell models to neuron-astrocyte network models: a review. Brain Res Bull 136:76–84

    Article  CAS  PubMed  Google Scholar 

  6. Denizot A, Berry H, Venugopal S (2020) Intracellular calcium signals in astrocytes, computational modeling of. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer New York, New York, pp 1–12

    Google Scholar 

  7. Manninen T, Havela R, Linne M-L (2018) Computational models for calcium-mediated astrocyte functions. Front Comput Neurosci 12:14

    Article  PubMed  PubMed Central  Google Scholar 

  8. Manninen T, Havela R, Linne M-L (2017) Reproducibility and comparability of computational models for astrocyte calcium excitability. Front Neuroinform 11:11

    Article  PubMed  PubMed Central  Google Scholar 

  9. González J, Pinzón A, Angarita-Rodríguez A et al (2020) Advances in astrocyte computational models: from metabolic reconstructions to multi-omic approaches. Front Neuroinform 14:35

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sejnowski TJ, Koch C, Churchland PS (1988) Computational neuroscience. Science 241:1299–1306

    Article  CAS  PubMed  Google Scholar 

  11. FitzHugh R (1955) Mathematical models of threshold phenomena in the nerve membrane. Bull Math Biophys 17:257–278

    Article  Google Scholar 

  12. Covelo A, Badoual A, Denizot A (2022) Reinforcing interdisciplinary collaborations to unravel the astrocyte “calcium code”. J Mol Neurosci 72:1443–1455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Giugliano M, Negrello M, Linaro D (eds) (2022) Computational modelling of the brain: modelling approaches to cells, circuits and networks. Springer International Publishing, Cham

    Google Scholar 

  14. De Schutter E (ed) (2009) Computational modeling methods for neuroscientists. The MIT Press, Cambridge

    Google Scholar 

  15. De Pittà M, Berry H (eds) (2019) Computational Glioscience. Springer International Publishing, Cham

    Google Scholar 

  16. Eriksson O, Bhalla US, Blackwell KT et al (2022) Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. elife 11:e69013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Crook S (2013) Model reproducibility: overview. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, New York, pp 1–3

    Google Scholar 

  18. Benureau FCY, Rougier NP (2018) Re-run, repeat, reproduce, reuse, replicate: transforming code into scientific contributions. Front Neuroinform 11:69

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fellin T (2009) Communication between neurons and astrocytes: relevance to the modulation of synaptic and network activity. J Neurochem 108:533–544

    Article  CAS  PubMed  Google Scholar 

  20. Adamsky A, Goshen I (2018) Astrocytes in memory function: pioneering findings and future directions. Neuroscience 370:14–26

    Article  CAS  PubMed  Google Scholar 

  21. Araque A, Carmignoto G, Haydon PG et al (2014) Gliotransmitters travel in time and space. Neuron 81:728–739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Savtchouk I, Volterra A (2018) Gliotransmission: beyond black-and-white. J Neurosci 38:14–25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fiacco TA, McCarthy KD (2018) Multiple lines of evidence indicate that gliotransmission does not occur under physiological conditions. J Neurosci 38:3–13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kofuji P, Araque A (2021) G-protein-coupled receptors in astrocyte–neuron communication. Neuroscience 456:71–84

    Article  CAS  PubMed  Google Scholar 

  25. González-Arias C, Perea G (2019) Gliotransmission at tripartite synapses. In: De Pittà M, Berry H (eds) Computational glioscience. Springer International Publishing, Cham, pp 213–226

    Chapter  Google Scholar 

  26. Gordleeva SY, Ermolaeva AV, Kastalskiy IA et al (2019) Astrocyte as spatiotemporal integrating detector of neuronal activity. Front Physiol 10:294

    Article  PubMed  PubMed Central  Google Scholar 

  27. De Pittà M, Goldberg M, Volman V et al (2009) Glutamate regulation of calcium and IP3 oscillating and pulsating dynamics in astrocytes. J Biol Phys 35:383–411

    Article  PubMed  PubMed Central  Google Scholar 

  28. Nadkarni S, Jung P, Levine H (2008) Astrocytes optimize the synaptic transmission of information. PLoS Comput Biol 4:e1000088

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dupont G, Combettes L, Bird GS et al (2011) Calcium oscillations. Cold Spring Harb Perspect Biol 3:a004226

    Article  PubMed  PubMed Central  Google Scholar 

  30. Li Y-X, Rinzel J (1994) Equations for InsP3 receptor-mediated [Ca2+]i oscillations derived from a detailed kinetic model: a Hodgkin-Huxley like formalism. J Theor Biol 166:461–473

    Article  CAS  PubMed  Google Scholar 

  31. Dupont G, Sneyd J (2017) Recent developments in models of calcium signalling. Curr Opin Syst Biol 3:15–22

    Article  Google Scholar 

  32. Siekmann I, Cao P, Sneyd J et al (2019) Data-driven modelling of the inositol trisphosphate receptor (IP3R) and its role in calcium-induced calcium release (CICR). In: De Pittà M, Berry H (eds) Computational glioscience. Springer International Publishing, Cham, pp 39–68

    Chapter  Google Scholar 

  33. Vuillaume R, Lorenzo J, Binczak S et al (2021) A computational study on synaptic plasticity regulation and information processing in neuron-astrocyte networks. Neural Comput 33:1970–1992

    Article  PubMed  Google Scholar 

  34. Lenk K, Satuvuori E, Lallouette J et al (2020) A computational model of interactions between neuronal and astrocytic networks: the role of astrocytes in the stability of the neuronal firing rate. Front Comput Neurosci 13:92

    Article  PubMed  PubMed Central  Google Scholar 

  35. Liu J, McDaid L, Araque A et al (2019) GABA regulation of burst firing in hippocampal astrocyte neural circuit: a biophysical model. Front Cell Neurosci 13:335

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gordleeva SY, Lebedev SA, Rumyantseva MA et al (2018) Astrocyte as a detector of synchronous events of a neural network. JETP Lett 107:440–445

    Article  CAS  Google Scholar 

  37. Gordleeva SY, Stasenko SV, Semyanov AV et al (2012) Bi-directional astrocytic regulation of neuronal activity within a network. Front Comput Neurosci 6:92

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bertram R, Sherman A, Stanley EF (1996) Single-domain/bound calcium hypothesis of transmitter release and facilitation. J Neurophysiol 75:1919–1931

    Article  CAS  PubMed  Google Scholar 

  40. Kang J, Jiang L, Goldman SA et al (1998) Astrocyte-mediated potentiation of inhibitory synaptic transmission. Nat Neurosci 1:683–692

    Article  CAS  PubMed  Google Scholar 

  41. Dobrunz LE, Huang EP, Stevens CF (1997) Very short-term plasticity in hippocampal synapses. Proc Natl Acad Sci U S A 94:14843–14847

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tsodyks MV, Markram H (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci U S A 94:719–723

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nadkarni S, Jung P (2007) Modeling synaptic transmission of the tripartite synapse. Phys Biol 4:1–9

    Article  CAS  PubMed  Google Scholar 

  44. Pasti L, Volterra A, Pozzan T et al (1997) Intracellular calcium oscillations in astrocytes: a highly plastic, bidirectional form of communication between neurons and astrocytes in situ. J Neurosci 17:7817–7830

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Pasti L, Zonta M, Pozzan T et al (2001) Cytosolic calcium oscillations in astrocytes may regulate exocytotic release of glutamate. J Neurosci 21:477–484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Parpura V, Haydon PG (2000) Physiological astrocytic calcium levels stimulate glutamate release to modulate adjacent neurons. Proc Natl Acad Sci U S A 97:8629–8634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Perea G, Araque A (2007) Astrocytes potentiate transmitter release at single hippocampal synapses. Science 317:1083–1086

    Article  CAS  PubMed  Google Scholar 

  48. Fiacco TA, McCarthy KD (2004) Intracellular astrocyte calcium waves in situ increase the frequency of spontaneous AMPA receptor currents in CA1 pyramidal neurons. J Neurosci 24:722–732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Young GWD, Keizer J (1992) A single-pool inositol 1,4,5-trisphosphate-receptor-based model for agonist-stimulated oscillations in Ca 2+ concentration. Proc Natl Acad Sci U S A 89:9895–9899

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bezprozvanny L, Watras J, Ehrlich BE (1991) Bell-shaped calcium-response curves of lns(l,4,5)P3- and calcium-gated channels from endoplasmic reticulum of cerebellum. Nature 351:751–754

    Article  CAS  PubMed  Google Scholar 

  51. Rusakov DA, Stewart MG (2021) Synaptic environment and extrasynaptic glutamate signals: the quest continues. Neuropharmacology 195:108688

    Article  CAS  PubMed  Google Scholar 

  52. Ding S, Fellin T, Zhu Y et al (2007) Enhanced astrocytic Ca2+ signals contribute to neuronal excitotoxicity after status epilepticus. J Neurosci 27:10674–10684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Danbolt NC (2001) Glutamate uptake. Prog Neurobiol 65:1–105

    Article  CAS  PubMed  Google Scholar 

  54. Eulenburg V, Gomeza J (2010) Neurotransmitter transporters expressed in glial cells as regulators of synapse function. Brain Res Rev 63:103–112

    Article  CAS  PubMed  Google Scholar 

  55. Bergles DE, Diamond JS, Jahr CE (1999) Clearance of glutamate inside the synapse and beyond. Curr Opin Neurobiol 9:293–298

    Article  CAS  PubMed  Google Scholar 

  56. Anderson CM, Swanson RA (2000) Astrocyte glutamate transport: review of properties, regulation, and physiological functions. Glia 32:1–14

    Article  CAS  PubMed  Google Scholar 

  57. Rose CR, Ziemens D, Untiet V et al (2018) Molecular and cellular physiology of sodium-dependent glutamate transporters. Brain Res Bull 136:3–16

    Article  CAS  PubMed  Google Scholar 

  58. Scimemi A, Diamond JS (2013) Deriving the time course of glutamate clearance with a deconvolution analysis of astrocytic transporter currents. J Vis Exp (78):50708

    Google Scholar 

  59. Rǎdulescu AR, Todd GC, Williams CL et al (2022) Estimating the glutamate transporter surface density in distinct sub-cellular compartments of mouse hippocampal astrocytes. PLoS Comput Biol 18:e1009845

    Article  PubMed  PubMed Central  Google Scholar 

  60. Flanagan B, McDaid L, Wade J et al (2018) A computational study of astrocytic glutamate influence on post-synaptic neuronal excitability. PLoS Comput Biol 14:e1006040

    Article  PubMed  PubMed Central  Google Scholar 

  61. Héja L, Kardos J (2020) NCX activity generates spontaneous Ca2+ oscillations in the astrocytic leaflet microdomain. Cell Calcium 86:102137

    Article  PubMed  Google Scholar 

  62. De Pittà M, Brunel N (2016) Modulation of synaptic plasticity by glutamatergic gliotransmission: a modeling study. Neural Plast 2016:1–30

    Article  Google Scholar 

  63. Østby I, Øyehaug L, Einevoll GT et al (2009) Astrocytic mechanisms explaining neural-activity-induced shrinkage of extraneuronal space. PLoS Comput Biol 5:e1000272

    Article  PubMed  PubMed Central  Google Scholar 

  64. Breslin K, Wade JJ, Wong-Lin K et al (2018) Potassium and sodium microdomains in thin astroglial processes: a computational model study. PLoS Comput Biol 14:e1006151

    Article  PubMed  PubMed Central  Google Scholar 

  65. Wade JJ, Breslin K, Wong-Lin K et al (2019) Calcium microdomain formation at the perisynaptic cradle due to NCX reversal: a computational study. Front Cell Neurosci 13:185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Oschmann F, Mergenthaler K, Jungnickel E et al (2017) Spatial separation of two different pathways accounting for the generation of calcium signals in astrocytes. PLoS Comput Biol 13:e1005377

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bindocci E, Savtchouk I, Liaudet N et al (2017) Three-dimensional Ca 2+ imaging advances understanding of astrocyte biology. Science 356:eaai8185

    Article  PubMed  Google Scholar 

  68. Haydon PG, Parpura V (eds) (2009) Astrocytes in (patho)physiology of the nervous system. Springer US, Boston

    Google Scholar 

  69. Elul R (1967) Fixed charge in the cell membrane. J Physiol 189:351–365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Srinivasan R, Huang BS, Venugopal S et al (2015) Ca2+ signaling in astrocytes from Ip3r2−/− mice in brain slices and during startle responses in vivo. Nat Neurosci 18:708–717

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Patrushev I, Gavrilov N, Turlapov V et al (2013) Subcellular location of astrocytic calcium stores favors extrasynaptic neuron–astrocyte communication. Cell Calcium 54:343–349

    Article  CAS  PubMed  Google Scholar 

  72. Ziemens D, Oschmann F, Gerkau NJ et al (2019) Heterogeneity of activity-induced sodium transients between astrocytes of the mouse hippocampus and neocortex: mechanisms and consequences. J Neurosci 39:2620–2634

    Article  PubMed  PubMed Central  Google Scholar 

  73. Tsacopoulos M, Magistretti PJ (1996) Metabolic coupling between glia and neurons. J Neurosci 16:877–885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Dienel GA (2012) Brain lactate metabolism: the discoveries and the controversies. J Cereb Blood Flow Metab 32:1107–1138

    Article  CAS  PubMed  Google Scholar 

  75. Magistretti PJ, Allaman I (2015) A cellular perspective on brain energy metabolism and functional imaging. Neuron 86:883–901

    Article  CAS  PubMed  Google Scholar 

  76. Jolivet R (2009) Deciphering neuron-glia compartmentalization in cortical energy metabolism. Front Neuroenerg 1:4

    Article  Google Scholar 

  77. Mangia S, Simpson IA, Vannucci SJ et al (2009) The in vivo neuron-to-astrocyte lactate shuttle in human brain: evidence from modeling of measured lactate levels during visual stimulation. J Neurochem 109:55–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Aubert A, Pellerin L, Magistretti PJ et al (2007) A coherent neurobiological framework for functional neuroimaging provided by a model integrating compartmentalized energy metabolism. Proc Natl Acad Sci U S A 104:4188–4193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Simpson IA, Carruthers A, Vannucci SJ (2007) Supply and demand in cerebral energy metabolism: the role of nutrient transporters. J Cereb Blood Flow Metab 27:1766–1791

    Article  CAS  PubMed  Google Scholar 

  80. Cloutier M, Bolger FB, Lowry JP et al (2009) An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements. J Comput Neurosci 27:391–414

    Article  PubMed  Google Scholar 

  81. Gjedde A (2007) 4.5 coupling of brain function to metabolism: evaluation of energy requirements. In: Lajtha A, Gibson GE, Dienel GA (eds) Handbook of neurochemistry and molecular neurobiology: brain energetics. Integration of molecular and cellular processes. Springer US, Boston, pp 343–400

    Chapter  Google Scholar 

  82. Hyder F, Patel AB, Gjedde A et al (2006) Neuronal–glial glucose oxidation and glutamatergic–GABAergic function. J Cereb Blood Flow Metab 26:865–877

    Article  CAS  PubMed  Google Scholar 

  83. Barros LF, Courjaret R, Jakoby P et al (2009) Preferential transport and metabolism of glucose in Bergmann glia over Purkinje cells: a multiphoton study of cerebellar slices. Glia 57:962–970

    Article  CAS  PubMed  Google Scholar 

  84. Nehlig A, Wittendorp-Rechenmann E, Dao Lam C (2004) Selective uptake of [14C]2-deoxyglucose by neurons and astrocytes: high-resolution microautoradiographic imaging by cellular 14C-trajectography combined with immunohistochemistry. J Cereb Blood Flow Metab 24:1004–1014

    Article  CAS  PubMed  Google Scholar 

  85. Mason S (2017) Lactate shuttles in neuroenergetics—homeostasis, allostasis and beyond. Front Neurosci 11:43

    Article  PubMed  PubMed Central  Google Scholar 

  86. Patsatzis DG, Tingas E-A, Goussis DA et al (2019) Computational singular perturbation analysis of brain lactate metabolism. PLoS One 14:e0226094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Tadi M, Allaman I, Lengacher S et al (2015) Learning-induced gene expression in the hippocampus reveals a role of neuron -astrocyte metabolic coupling in long term memory. PLoS One 10:e0141568

    Article  PubMed  PubMed Central  Google Scholar 

  88. Wang D, Pascual JM, Yang H et al (2006) A mouse model for Glut-1 haploinsufficiency. Hum Mol Genet 15:1169–1179

    Article  CAS  PubMed  Google Scholar 

  89. Herrero-Mendez A, Almeida A, Fernández E et al (2009) The bioenergetic and antioxidant status of neurons is controlled by continuous degradation of a key glycolytic enzyme by APC/C–Cdh1. Nat Cell Biol 11:747–752

    Article  CAS  PubMed  Google Scholar 

  90. Rusakov DA (2015) Disentangling calcium-driven astrocyte physiology. Nat Rev Neurosci 16:226–233

    Article  CAS  PubMed  Google Scholar 

  91. Tønnesen J, Katona G, Rózsa B et al (2014) Spine neck plasticity regulates compartmentalization of synapses. Nat Neurosci 17:678–685

    Article  PubMed  Google Scholar 

  92. Holcman D, Korkotian E, Segal M (2005) Calcium dynamics in dendritic spines, modeling and experiments. Cell Calcium 37:467–475

    Article  CAS  PubMed  Google Scholar 

  93. Obashi K, Taraska JW, Okabe S (2021) The role of molecular diffusion within dendritic spines in synaptic function. J Gen Physiol 153:e202012814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Cresswell-Clay E, Crock N, Tabak J et al (2018) A compartmental model to investigate local and global Ca2+ dynamics in astrocytes. Front Comput Neurosci 12:94

    Article  PubMed  PubMed Central  Google Scholar 

  95. Savtchenko LP, Bard L, Jensen TP et al (2018) Disentangling astroglial physiology with a realistic cell model in silico. Nat Commun 9:3554

    Article  PubMed  PubMed Central  Google Scholar 

  96. Denizot A, Arizono M, Nägerl UV et al (2019) Simulation of calcium signaling in fine astrocytic processes: effect of spatial properties on spontaneous activity. PLoS Comput Biol 15:e1006795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Burrage K, Burrage PM, Leier A et al (2011) Stochastic simulation for spatial modelling of dynamic processes in a living cell. In: Koeppl H, Setti G, di Bernardo M et al (eds) Design and analysis of biomolecular circuits: engineering approaches to systems and synthetic biology. Springer, New York, pp 43–62

    Chapter  Google Scholar 

  98. Smith S, Grima R (2019) Spatial stochastic intracellular kinetics: a review of modelling approaches. Bull Math Biol 81:2960–3009

    Article  PubMed  Google Scholar 

  99. Andrews SS (2018) Particle-based stochastic simulators. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, New York, pp 1–5

    Google Scholar 

  100. Blackwell KT (2013) Approaches and tools for modeling signaling pathways and calcium dynamics in neurons. J Neurosci Methods 220:131–140

    Article  CAS  PubMed  Google Scholar 

  101. Hines M, Carnevale T, McDougal RA (2019) NEURON simulation environment. In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, New York, pp 1–7

    Google Scholar 

  102. Hepburn I, Chen W, Wils S et al (2012) STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies. BMC Syst Biol 6:36

    Article  PubMed  PubMed Central  Google Scholar 

  103. Arizono M, Inavalli VVGK, Bancelin S et al (2021) Super-resolution shadow imaging reveals local remodeling of astrocytic microstructures and brain extracellular space after osmotic challenge. Glia 69:1605–1613

    Article  CAS  PubMed  Google Scholar 

  104. Denizot A, Arizono M, Nägerl UV et al (2022) Control of Ca 2+ signals by astrocyte nanoscale morphology at tripartite synapses. Glia 70:2378–2391

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Arizono M, Inavalli VVGK, Panatier A et al (2020) Structural basis of astrocytic Ca2+ signals at tripartite synapses. Nat Commun 11:1906

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Denizot A, Castillo MFV, Puchenkov P et al (2022) The endoplasmic reticulum in perisynaptic astrocytic processes: shape, distribution and effect on calcium activity. https://www.biorxiv.org/content/10.1101/2022.02.28.482292v1

  107. Oberheim NA, Wang X, Goldman S et al (2006) Astrocytic complexity distinguishes the human brain. Trends Neurosci 29:547–553

    Article  CAS  PubMed  Google Scholar 

  108. Eroglu C, Barres BA (2010) Regulation of synaptic connectivity by glia. Nature 468:223–231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Pirttimaki TM, Sims RE, Saunders G et al (2017) Astrocyte-mediated neuronal synchronization properties revealed by false gliotransmitter release. J Neurosci 37:9859–9870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Szabó Z, Héja L, Szalay G et al (2017) Extensive astrocyte synchronization advances neuronal coupling in slow wave activity in vivo. Sci Rep 7:6018

    Article  PubMed  PubMed Central  Google Scholar 

  111. Bushong EA, Martone ME, Jones YZ et al (2002) Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains. J Neurosci 22:183–192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Evans WH, Martin PEM (2002) Gap junctions: structure and function (Review). Mol Membr Biol 19:121–136

    Article  CAS  PubMed  Google Scholar 

  113. Giaume C, Venance L (1998) Intercellular calcium signaling and gap junctional communication in astrocytes. Glia 24:50–64

    Article  CAS  PubMed  Google Scholar 

  114. Verisokin AY, Verveyko DV, Postnov DE et al (2021) Modeling of astrocyte networks: toward realistic topology and dynamics. Front Cell Neurosci 15:645068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Li L, Zhou J, Sun H et al (2020) A computational model to investigate GABA-activated astrocyte modulation of neuronal excitation. Comput Math Methods Med 2020:e8750167

    Article  Google Scholar 

  116. Ullah G, Jung P, Cornellbell A (2006) Anti-phase calcium oscillations in astrocytes via inositol (1, 4, 5)-trisphosphate regeneration. Cell Calcium 39:197–208

    Article  CAS  PubMed  Google Scholar 

  117. Wu Y-W, Tang X, Arizono M et al (2014) Spatiotemporal calcium dynamics in single astrocytes and its modulation by neuronal activity. Cell Calcium 55:119–129

    Article  CAS  PubMed  Google Scholar 

  118. Fritschi L, Lenk K (2021) Parameter inference for an astrocyte model using machine learning approaches. http://biorxiv.org/lookup/doi/10.1101/2023.05.16.540982

  119. Genocchi B, Ahtiainen A, Barros MT et al (2021) Astrocytic control in in vitro and simulated neuron-astrocyte networks. In: Proceedings of the eight annual ACM international conference on nanoscale computing and communication. ACM, Virtual Event Italy, pp 1–7

    Google Scholar 

  120. Lenk K, Genocchi B, Barros MT et al (2021) Larger connection radius increases hub astrocyte number in a 3-D neuron–astrocyte network model. IEEE Trans Mol Biol Multi-Scale Commun 7:83–88

    Article  Google Scholar 

  121. Genocchi B, Lenk K, Hyttinen J (2020) Influence of astrocytic gap junction coupling on in silico neuronal network activity. In: Henriques J, Neves N, de Carvalho P (eds) XV Mediterranean conference on medical and biological engineering and computing – MEDICON 2019. Springer International Publishing, Cham, pp 480–487

    Chapter  Google Scholar 

  122. Sacerdote L, Giraudo MT (2013) Stochastic integrate and fire models: a review on mathematical methods and their applications. In: Bachar M, Batzel J, Ditlevsen S (eds) Stochastic biomathematical models: with applications to neuronal modeling. Springer, Berlin, Heidelberg, pp 99–148

    Chapter  Google Scholar 

  123. Blohm G, Kording KP, Schrater PR (2020) A how-to-model guide for neuroscience. eNeuro 7. https://doi.org/10.1523/ENEURO.0352-19.2019

  124. Churchland PS, Sejnowski TJ (2016) Blending computational and experimental neuroscience. Nat Rev Neurosci 17:667–668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Levenstein D, Alvarez VA, Amarasingham A et al (2023) On the role of theory and modeling in neuroscience. J Neurosci 43:1074–1088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Wang X-J, Hu H, Huang C et al (2020) Computational neuroscience: a frontier of the 21st century. Natl Sci Rev 7:1418–1422

    Article  PubMed  PubMed Central  Google Scholar 

  127. Gerstner W, Sprekeler H, Deco G (2012) Theory and simulation in neuroscience. Science 338:60–65

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

K.L.s research was partially conducted while visiting the Okinawa Institute of Science and Technology (OIST) through the Theoretical Sciences Visiting Program (TSVP). A. D.s work was funded by the Okinawa Institute of Science and Technology Graduate University and by JSPS (Japan Society for the Promotion of Science) Postdoctoral Fellowship for Research in Japan (Standard, P21733). S.N. would like to acknowledge Shweta Shrotri, IISER Pune, for her help with Figs. 1 and 4.

Author information

Author notes

  1. Kerstin Lenk and Audrey Denizot contributed equally to this work.

Authors and Affiliations

  1. Institute of Neural Engineering, Graz University of Technology, Graz, Austria

    Kerstin Lenk

  2. BioTechMed, Graz, Austria

    Kerstin Lenk

  3. Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kerstin Lenk

  4. Okinawa Institute of Science and Technology, Computational Neuroscience Unit, Onna-Son, Japan

    Audrey Denizot

  5. AIstroSight, Inria, Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France

    Audrey Denizot

  6. University of Lyon, LIRIS UMR5205, Villeurbanne, France

    Audrey Denizot

  7. Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

    Barbara Genocchi

  8. Department of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland

    Ippa Seppälä

  9. Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, USA

    Marsa Taheri

  10. Department of Biology, Indian Institute of Science Education and Research Pune, Pune, Maharashtra, India

    Suhita Nadkarni

Authors
  1. Kerstin Lenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Audrey Denizot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Barbara Genocchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ippa Seppälä
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marsa Taheri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suhita Nadkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Science Park of the UPV/EHU, Achucarro Basque Center for Neuroscience, Leioa, Spain

    Maria Kukley

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Lenk, K., Denizot, A., Genocchi, B., Seppälä, I., Taheri, M., Nadkarni, S. (2024). Computational Models of Astrocyte Function at Glutamatergic Synapses. In: Kukley, M. (eds) New Technologies for Glutamate Interaction. Neuromethods, vol 2780. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3742-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3742-5_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3741-8

  • Online ISBN: 978-1-0716-3742-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation