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Calcium Buffering in Astrocytes and Its Relevance for Experimental Data Interpretation and Computational Modeling.

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  1. [1] § Physiological Ca2+ Buffering in Organic Matter › Kinetics, Affinity, and Capacity ↔ calciumbuffering.m, lines 96–133 · score 0.58 · Binding occupancy, fast buffer, slow buffer, native, fluorescence, Kd

Paper

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The authors' code

MATLAB · 136 lines · 4.8 KB · GPL-3.0 · 1 match

  1. %% Author of the code: Kerstin Lenk
  2. % Seleton of the code produced with Perplexity, April 2026
  3. % Related publication:
  4. % Calcium buffering in astrocytes and its relevance for experimental data interpretation and computational modelling
  5. % Kerstin Lenk*, Andre Zeug, Franziska E. Müller
  7. %% Calcium buffer schematic figure
  8. % Panel A: same Kd, different kon/koff
  9. % Panel B: different Kd vs EC50 combinations and their effect on Ca2+ transient
  11. clear; close all; clc;
  13. %% ------------------------------------------------------------
  14. %% Panel A: Same Kd, different kinetic speeds
  16. %% Parameters
  17. t_span = [0 15]; % simulate 20 seconds
  18. K_d = 50; % uM, low affinity (same for both buffers)
  19. B_total = 30; % µM, total buffer concentration
  20. ca_baseline = 0.084; % µM, baseline Ca2+; Shigetomi et al. 2016
  22. %% Buffer kinetics (same Kd, different kon)
  23. % Fast buffer
  24. kon_fast = 50; % µM^-1 s^-1
  25. koff_fast = K_d * kon_fast;
  27. % Slow buffer
  28. kon_slow = 0.1; % µM^-1 s^-1
  29. koff_slow = K_d * kon_slow;
  31. %% Oscillatory Ca2+ input
  32. A = 1.0; % µM, amplitude
  33. tau_r = 0.03; % s, rise time
  34. tau_d = 0.45; % s, decay time
  35. f = 0.2; % Hz
  37. ca_input = @(t) ...
  38. max(0, A*(exp(-(mod(t,1/f))/tau_d) - exp(-(mod(t,1/f))/tau_r)));
  40. %% ODE systems
  41. % 1. No buffer
  42. ode_none = @(t,y) ca_input(t) - 2*(y(1)-ca_baseline);
  44. % 2. Fast buffer
  45. ode_fast = @(t,y) [ca_input(t) - 4*(y(1)-ca_baseline) ...
  46. - (kon_fast*y(1)*(B_total-y(2)) - koff_fast*y(2));
  47. kon_fast*y(1)*(B_total-y(2)) - koff_fast*y(2)
  48. ];
  50. % 3. Slow buffer
  51. ode_slow = @(t,y) [ca_input(t) - 2*(y(1)-ca_baseline) ...
  52. - (kon_slow*y(1)*(B_total-y(2)) - koff_slow*y(2));
  53. kon_slow*y(1)*(B_total-y(2)) - koff_slow*y(2)
  54. ];
  56. %% Solve
  57. [t1,y1] = ode45(ode_none, t_span, ca_baseline);
  58. [t2,y2] = ode45(ode_fast, t_span, [ca_baseline; 0]);
  59. [t3,y3] = ode45(ode_slow, t_span, [ca_baseline; 0]);
  61. %% ------------------------------------------------------------
  62. %% Panel B: Different Kd vs EC50 combinations
  63. % Two indicator scenarios:
  64. % 1) strong binder but fluorescence responds later (Kd << EC50)
  65. % 2) weaker binder but fluorescence responds earlier (Kd > EC50)
  66. %
  67. % Binding changes the Ca2+ transient; fluorescence is computed with a Hill curve:
  68. % Fnorm = Ca^n / (EC50^n + Ca^n)
  70. nH = 2.5; % Hill coefficient for fluorescence readout
  72. ind(1).name = 'Kd << EC50';
  73. ind(1).Kd = 0.15; % uM
  74. ind(1).EC50 = 0.80; % uM
  75. ind(1).kon = 35; % 1/(uM*s)
  76. ind(1).koff = ind(1).Kd * ind(1).kon;
  78. ind(2).name = 'Kd > EC50';
  79. ind(2).Kd = 0.90; % uM
  80. ind(2).EC50 = 0.35; % uM
  81. ind(2).kon = 20; % 1/(uM*s)
  82. ind(2).koff = ind(2).Kd * ind(2).kon;
  84. colorsB = [0.55 0.10 0.70;
  85. 0.00 0.60 0.60];
  87. %% Dose-response curves for panel B
  88. Ca_scan = logspace(-2, 1, 500); % 0.01 to 10 uM
  89. theta1 = Ca_scan ./ (ind(1).Kd + Ca_scan); % occupancy proxy for Kd
  90. theta2 = Ca_scan ./ (ind(2).Kd + Ca_scan);
  92. Fscan1 = (Ca_scan.^nH) ./ (ind(1).EC50^nH + Ca_scan.^nH);
  93. Fscan2 = (Ca_scan.^nH) ./ (ind(2).EC50^nH + Ca_scan.^nH);
  95. %% ------------------------------------------------------------
  96. %% Plot
  97. figure('Color','w','Position',[100 100 1500 600]);
  99. tiledlayout(1,2,'Padding','compact','TileSpacing','compact');
  101. % A: free Ca transient, same Kd different kinetics
  102. nexttile;
  103. plot(t1, y1, 'k--', 'LineWidth', 1.5); hold on;
  104. plot(t2, y2(:,1), 'r', 'LineWidth', 2.5);
  105. plot(t3, y3(:,1), 'b', 'LineWidth', 2.5);
  106. xlabel('Time (s)', 'FontSize', 16);
  107. ylabel('Free Ca^{2+} (\muM)', 'FontSize', 16);
  108. title('(A) Same K_d, different k_{on}/k_{off}', 'FontSize', 16);
  109. legend('Native transient', 'Fast buffer', 'Slow buffer', 'Location','southeast', 'FontSize', 12);
  110. box off;
  112. % B: Kd occupancy vs EC50 fluorescence curves
  113. nexttile;
  114. % yyaxis left
  115. semilogx(Ca_scan, theta1, '-', 'Color', colorsB(1,:), 'LineWidth', 2.5); hold on;
  116. xline(ind(1).Kd, ':', 'Color', colorsB(1,:), 'LineWidth', 1.);
  117. xline(ind(1).EC50, '--', 'Color', colorsB(1,:), 'LineWidth', 1.);
  119. semilogx(Ca_scan, theta2, '-', 'Color', colorsB(2,:), 'LineWidth', 2.5);
  120. ylabel('Binding occupancy', 'FontSize', 16);
  121. ylim([0 1]);
  122. xline(ind(2).Kd, ':', 'Color', colorsB(2,:), 'LineWidth', 1.);
  123. xline(ind(2).EC50, '--', 'Color', colorsB(2,:), 'LineWidth', 1.);
  124. hold off;
  126. xlabel('[Ca^{2+}] (\muM)', 'FontSize', 16);
  127. title('(B) K_d versus EC_{50}', 'FontSize', 16);
  128. legend({'K_d<<EC_{50} case: Binding occ.', ...
  129. 'K_d<<EC_{50} case: K_d','K_d<<EC_{50} case: EC_{50}', ...
  130. 'K_d>EC_{50} case: Binding occ.',...
  131. 'K_d>EC_{50} case: K_d', 'K_d>EC_{50} case: EC_{50}'}, ...
  132. 'Location','southeast', 'FontSize', 12);
  133. box off;
  135. %% Optional export
  136. exportgraphics(gcf, 'buffer_Kd_EC50_schematic.png', 'Resolution', 600);

calciumbuffering.m at commit 20b654a, under GPL-3.0 · at the source

Overview

Authors: Kerstin Lenk1,2, Andre Zeug3, Franziska E Müller3
ORCID iDs: Kerstin Lenk, Franziska E Müller
  1. Institute of Neural Engineering, Graz University of Technology, Graz, Austria
  2. BioTechMed, Graz, Austria
  3. Hannover Medical School, Institute of Neurophysiology, Cellular Neurophysiology, Hannover, Germany
Institutions: Graz University of Technology (Austria); BioTechMed-Graz (Austria); Medizinische Hochschule Hannover (Germany)
Journal: Journal of neurochemistry, volume 170, issue 6, article e70470
Dates: received 1 September 2025; accepted 6 May 2026; published online 4 June 2026; in print June 2026
Type: Review · Language: English
License: CC BY
Identifiers: DOI 10.1111/jnc.70470 · PMID 42244178 · PMCID PMC13238400 · OpenAlex W7163710125
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Evoked potentials
Keywords: astrocytes, buffering, calcium, computational modeling
MeSH: Astrocytes*, Calcium*, Calcium Signaling*, Computer Simulation*, Animals, Buffers, Endoplasmic Reticulum, Humans (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Federal Ministry of Research, Technology and Space (13N17513); Deutsche Forschungsgemeinschaft (ZE994); Medizinischen Hochschule Hannover; Förderstiftung MHH plus; Austrian Science Fund FWF (10.55776/PIN7030123)
Citations: cited by 1 paper (Europe PMC); 165 references in the paper

Abstract

Astrocytic Ca2+ signaling is essential for maintaining physiological brain function, including the modulation of synaptic transmission, neurovascular coupling, and ion homeostasis. However, the spatiotemporal dynamics of astrocytic Ca2+ activity are highly sensitive to Ca2+ buffering, which shapes the amplitude, duration, and spread of cytosolic and organellar signals. These buffers include endogenous components such as cytosolic Ca2+ binding proteins, as well as organelles like the endoplasmic reticulum acting as Ca2+ stores. Additionally, exogenous buffers are introduced in experiments, including chelators, synthetic dyes, and genetically encoded Ca2+ indicators. Both types of buffers can profoundly alter experimental observations, making it challenging to accurately interpret Ca2+ dynamics. Computational modeling offers a powerful approach to separate these effects, enabling systematic exploration of how the buffering capacity of specific system components influences astrocytic intracellular and intercellular signaling. By incorporating experimental data with realistic biophysical buffering parameters, models can make predictions that are difficult to achieve empirically and help identify key parameters that shape astrocytic Ca2+ physiology. In this review, we discuss how buffering components influence astrocyte Ca2+ activity and their integration into modeling predictions. Future advances in computational modeling, combined with extensive experimental data, will be crucial for enhancing our understanding of astrocytic Ca2+ regulation and elucidating its role in health and disease.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

kerstinlenk/calciumbuffering

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20b654a5c9f8c881ddfd1b30e9ac389963962e43, 11 May 2026
Languages: MATLAB (1)
Size: 3 files, 1 script
Software Heritage: not archived
Found in: the text, “Kinetics, Affinity, and Capacity”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/kerstinlenk/calciumbuffering

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data Availability Statement

The authors have nothing to report.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 8 MeSH terms, 5 funders, 164 references.

Cite

This paper

Lenk, K., Zeug, A., & Müller, F. E. (2026). Calcium Buffering in Astrocytes and Its Relevance for Experimental Data Interpretation and Computational Modeling. Journal of neurochemistry, 170(6), e70470. https://doi.org/10.1111/jnc.70470

BibTeX

@article{lenk2026calcium,
author = {Lenk, Kerstin and Zeug, Andre and Müller, Franziska E},
title = {{Calcium Buffering in Astrocytes and Its Relevance for Experimental Data Interpretation and Computational Modeling}},
journal = {Journal of neurochemistry},
year = {2026},
month = jun,
volume = {170},
number = {6},
pages = {e70470},
publisher = {Wiley},
issn = {0022-3042},
doi = {10.1111/jnc.70470},
url = {https://doi.org/10.1111/jnc.70470},
pmid = {42244178},
pmcid = {PMC13238400}
}

RIS

TY - JOUR
AU - Lenk, Kerstin
AU - Zeug, Andre
AU - Müller, Franziska E
TI - Calcium Buffering in Astrocytes and Its Relevance for Experimental Data Interpretation and Computational Modeling
T2 - Journal of neurochemistry
J2 - J Neurochem
PY - 2026
DA - 2026/06/01
VL - 170
IS - 6
SP - e70470
SN - 0022-3042
PB - Wiley
DO - 10.1111/jnc.70470
UR - https://doi.org/10.1111/jnc.70470
LA - en
ER -

CSL-JSON

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"given": "Franziska E"
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"container-title-short": "J Neurochem",
"volume": "170",
"issue": "6",
"page": "e70470",
"DOI": "10.1111/jnc.70470",
"PMID": "42244178",
"PMCID": "PMC13238400",
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