PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

A study on efficiency of 3D partial differential diffusive model of presynaptic processes

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The verification of efficiency of the diffusive-type model of neurotransmitter transport dynamics inside the presynaptic bouton, including the spatial aspect of the transport, is the main objective of the simulations described in this paper. Finite element and finite difference methods were applied to solve the model numerically. The bouton was represented by a 3D tetrahedral mesh. Some biological parameters have been taken from scientific literature whereas some other remain unknown. Therefore the simulations were performed for various values of the unknown parameters. Such an approach allowed us to assess the proper order of magnitude of those parameters by comparison of the dynamics of the process implied by the model with the observed post-synaptic potentials. The effect of synaptic depression has been captured in the simulations. The presented approach allowed us to verify to what extent the presynaptic transport can be explained by the diffusive mechanism. Furthermore, the sensitivity of the model to release rate and the synthesis rate is considered. Moreover, the algebraic models of the diminishment of released neurotransmitter amounts were proposed and tested, showing a good accuracy. Various geometrical aspects of the dynamics were studied. In particular, the influence of the geometry of the synthesis region was examined. In the paper, the aspects of numerical simulations are studied as well. Namely, the quality of the generated mesh was discussed. The results of the simulations were compared with the results of measurements and observations and will be later contrasted with more complex models.
Twórcy
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automation, Computer Science and Biomedical Engineering, Chair of Applied Computer Science, Mickiewicza Ave. 30, 30-059 Cracow, Poland
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automation, Computer Science and Biomedical Engineering, Chair of Applied Computer Science, Mickiewicza Ave. 30, 30-059 Cracow, Poland
autor
  • Jagiellonian University, Faculty of Mathematics and Computer Science, Institue of Computer Science and Computational Mathematics, Lojasiewicza 6, 30-348 Cracow, Poland
Bibliografia
  • [1] Alabi AR, Tsien RW. Synaptic vesicle pools and dynamics. Cold Spring Harb Perspect Biol 2012;4:a013680.
  • [2] Aristizabal F, Glavinovic MI. Simulation and parameter estimation of dynamics of synaptic depression. Biol Cybern 2004;90:3–18.
  • [3] Banasiak J, Falkiewicz A, Namayanja P. Asymptotic state lumping in transport and diffusion problems on networks with applications to population problems. Math Models Methods Appl Sci 2016;26(2).
  • [4] Bayat M, Lawrence J, Stone E. Data driven models of short-term synaptic plasticity. Front Comput Neurosci 2018;12 (32).
  • [5] Bielecki A. A model of human activity automatization as a basis of artificial intelligence systems. IEEE Trans Auton Mental Dev 2014;6(3):169–82.
  • [6] Bielecki A. Models of neurons and perceptrons: selected problems and challenges. Springer Series: Studies in Computational Intelligence, vol. 770. Switzerland: Cham; 2019.
  • [7] Bielecki A, Bielecka M, Bielecki P. Conditioned anxiety mechanism as a basis for a procedure of control module of an autonomous robot. Lect Notes Artif Intell 2017;10246:390–8.
  • [8] Bielecki A, Gierdziewicz M, Kalita P. Three-dimensional model of signal processing in the presynaptic bouton of the neuron. Lect Notes Artif Intell 2018;10841:3–14.
  • [9] Bielecki A, Gierdziewicz M, Kalita P, Szostek K. Construction of a 3d geometric model of a presynaptic bouton for use in modeling of neurotransmitter flow. Lect Notes Comput Sci 2016;9972:377–86.
  • [10] Bielecki A, Kalita P. Model of neurotransmitter fast transport in axon terminal of presynaptic neuron. J Math Biol 2008;56:559–76.
  • [11] Bielecki A, Kalita P. Dynamical properties of the reaction– diffusion type model of fast synaptic transport. J Math Anal Appl 2012;393:329–40.
  • [12] Bielecki A, Kalita P, Lewandowski M, Siwek B. Numerical simulation for a neurotransmitter transport model in the axon terminal of a presynaptic neuron. Biol Cybern 2010;102:489–502.
  • [13] Bielecki A, Kalita P, Lewandowski M, Skomorowski M. Compartment model of neuropeptide synaptic transport with impulse control. Biol Cybern 2008;99:443–58.
  • [14] Bobrowski A. Boundary conditions in evolutionary equations in biology. Lect Notes Math 2015;2126:47–92.
  • [15] Bobrowski A, Morawska K. From a pde model to an ode model of dynamics of synaptic depression. Discr Contin Dyn Syst Ser B 2012;17:2313–27.
  • [16] Bui L, Glavinovic M. Synaptic activity slows vesicular replenishment at excitatory synapses of rat hippocampus. Cognit Neurodyn 2013;7:105–20.
  • [17] Bui L, Glavinovic M. Is replenishment of the readily releasable pool associated with vesicular movement? Cognit Neurodyn 2014;8:99–110.
  • [18] Bui L, Glavinovic M. Temperature dependence of vesicular dynamics at excitatory synapses of rat hippocampus. Cognit Neurodyn 2014;8:277–86.
  • [19] Ciarlet PG. The finite element method for elliptic problems. Amsterdam: North Holland; 1978.
  • [20] Delaunay B. Sur la sphère vide. Otdelenie Matematicheskikh i Estestvennykh Nauk 1934;6:793–800.
  • [21] Denker A, Rizzoli SO. Synaptic vesicle pools: an update. Front Synaptic Neurosci 2010;2:135.
  • [22] Han F, Wang Z, Fan H. Determine neuronal tuning curves by exploring optimum firing rate distribution for information efficiency. Front Comput Neurosci 2017;11(10).
  • [23] Hillman H. Two problems with cell biology – and what should be done about them. Biologist 2010;57(1):40–4.
  • [24] Joensuu M, Padmanabhan P, Durisic N, Bademosi AT, CooperWilliams E, Morrow IC, et al. Subdiffractional tracking of internalized molecules reveals heterogeneous motion states of synaptic vesicles. J Cell Biol 2016;215:277–92.
  • [25] Keener J, Sneyd J. Mathematical physiology. Berlin, Heidelberg, New York: Springer; 1998.
  • [26] Khanmohammadi M, Darkner S, Nava N, Nyengaard JR, Wegener G, Popoli M, et al. 3d analysis of synaptic vesicle density and distribution after acute foot-shock stress by using serial section transmission electron microscopy. J Microscopy 2017;265:101–10.
  • [27] Kim E, Owen B, Holmes WR, Grover LM. Decreased afferent excitability contributes to synaptic depression during high-frequency stimulation in hippocampal area ca1. J Neurophysiol 2012;108(7):1965–76.
  • [28] Knödel MM, Geiger R, Ge L, Bucher D, Grillo A, Wittum G, et al. Synaptic bouton properties are tuned to best fit the prevailing firing pattern. Front Comput Neurosci 2014;8 (101).
  • [29] Lourakis MIA. A brief description of the levenberg- marquardt algorithm implemented by levmar, technical report. Institute of Computer Science, Foundation for Research and Technology – Hellas; 2005.
  • [30] Miller G, Talmor D, Teng SH, Walkington N, Wang H. Control volume meshes using sphere packing: generation, refinement and coarsening. Proceedings of the Fifth International Meshing Roundtable 1996;47–61.
  • [31] Quarteroni A. Numerical models for differential problems. Berlin, Heidelberg, New York: Springer; 2014.
  • [32] Rizzoli SO, Betz WJ. Synaptic vesicle pools. Nat Rev Neurosci 2005;6:57–69.
  • [33] Saleewong T, Srikiatkhachorn A, Maneepark M, Chonwerayuth A, Bongsebandhu-Phubhakdi S. Quantyfying altered long-term potential in the ca1 hippocampus. J Integr Neurosci 2012;11:243–64.
  • [34] Salin-Pascual RJ, Jimenez-Anguiano A. Vesamicol, an acetylcholine uptake blocker in presynaptic vesicles, suppresses rapid eye movement (rem) sleep in the rat. Psychopharmacology 1995;121:485–7.
  • [35] Si H. A quality tetrahedral mesh generator and 3d delaunay trianulator, version 1.4, user manual. Berlin: Weierstrass Institute for Applied Analysis and Stochastics (WIAS); 2006.
  • [36] Si H. Tetgen, a delaunay-based quality tetrahedral mesh generator. ACM Trans Math Softw 2015;41.
  • [37] Szule JA, Harlow ML, Jung JH, De-Miguel FF, Marshall RM, McMahan UJ. Regulation of synaptic vesicle docking by different classes of macromolecules in active zone material. PLoS ONE 2012;7(3):e33333.
  • [38] Tadeusiewicz R. The study of the mechanism of symphase summation based on the model of human internal ear. Biocybern Biomed Eng 1983–1984 1984;3–4:81–92.
  • [39] Tadeusiewicz R. Problems of biocybernetics. Warszawa: PWN; 1994.
  • [40] Tadeusiewicz R. Theoretical neurocybernetics. Warszawa: Wydawnictwa Uniwersytetu Warszawskiego; 2009.
  • [41] Trabka W, Tadeusiewicz R. Simulational research concerning the destabilization in neuron-like networks. Biocybern Biomed Eng 1988;8(1–4):145–55.
  • [42] von Gersdorff H, Matthews G. Inhibition of endocytosis by elevated internal calcium in a synaptic terminal. Nature 1994;370:652–5.
  • [43] Wang Y, Manis PB. Short-term synaptic depression and recovery at the mature mammalian endbulb of held synapse in mice. J Neurophysiol 2008;100(3):1255–64.
  • [44] Wilhelm BG, Mandad S, Truckenbrodt S, Kröhnert K, Schäfer C, Rammner B, et al. Composition of isolated synaptic boutons reveals the amounts of vesicle trafficking proteins. Science 2014;344(6187):1023–8.
  • [45] Zucker RS, Regehr WG. Short-term synaptic plasticity. Annu Rev Physiol 2002;64:355–405.
Uwagi
EN
The calculations for this paper were supported by the Academic Computer Center in Cracow under grant ‘‘neuron2018’’.
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2f6dd489-026d-40a6-81f9-e6ee826b6e74
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.