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Conference Paper 

Deciphering Astroglial Dynamics and Interactions Through 
Multi-Scale Computational Modeling in Multiple Sclerosis 
Evolution

Chrysoula Tsimperi1,*, Konstantinos Michmizos2 and Leontios Hadjileontiadis1

1 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
2 Computational Brain Lab, Department of Computer Science, Rutgers University, Piscataway, NJ, United States.

* Corresponding Author Email: xrysa97.tsiberi@gmail.com 

ABSTRACT

Multiple sclerosis (MS) is a neurodegenerative disease affecting millions worldwide, highlighting the complex relationship 
between the immune system and the central nervous system. Astrocytes are recognized as significant contributors to the disease’s 
pathogenesis. In this work, a biophysically realistic astrocytic model was created to investigate astrocytes' role in MS develop-
ment, focusing on their impact on axonal conduction and enhanced sodium channel facilitation in demyelinated axons. Through 
the advancement of comprehension about the involvement of astrocytes in the pathophysiology of MS, this study explores the 
processes underlying the disease. The study also examines the morphology of astrocytes and its influence on cellular activity, 
providing insights into cell instability drivers and the interaction between morphological changes and functional modifications. 
This approach aims to understand the complex connections between cellular characteristics and physiological attributes, en-
hancing our understanding of multiple sclerosis and potentially developing groundbreaking therapies. 

Keywords—Astrocytes, Conduction velocity, In-Silico, Multiple sclerosis.

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

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103 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

INTRODUCTION

Astrocytes are key contributors to multiple sclerosis 
(MS) lesions, playing a crucial role in maintaining neural 
homeostasis and preventing neural tissue damage.1 They 
exhibit dual roles, responding adaptively or non-adaptively 
to the severity of injury.2,3 Their intricate morphology and 
adaptive responses are central to MS lesion development.4,5 
However, this dual nature, providing both protection and 
potential hindrance, makes them complex therapeutic 
targets.6 In this study, our primary objective is to delve 
into the role of astrocytes in the development of MS le-
sions, adopting a comprehensive approach through three 
distinct parts. The first part explores how astrocytes 
influence axonal conduction, with implications for MS-
related functional deficits. The second part examines how 
astrocytes facilitate sodium channels in demyelinated 
axons, shedding light on potential MS pathophysiology 
mechanisms. The third part seeks to correlate the loss 
and recovery of astrocytes in the cerebral cortex with 
myelin loss due to conduction block in new MS lesions. Our 
approach involves creating biologically realistic models 
of two distinct astrocytic states, one representing physi-
ological conditions and the other mimicking pathological 
scenarios. These models serve as the foundation for our 
study, enabling us to expand our understanding of various 
factors, including demyelinated and remyelinated axon 
conductance, the role of ions as signaling molecules (such 
as Ca2+, Na+, and K+), and the impact of inflammatory cy-
tokines like IL1β/6 and TNFα . Our overarching goal is to 
develop comprehensive computational representations 
of astrocytes that encompass both their physiological 
and pathological behaviors within the neocortex area. 
By developing biophysically realistic models and incor-
porating empirical data, we aim to accurately capture 
astrocyte morphology and functionality while exploring 
their behavior across different scales.

METHODS

In this study, we aimed to unravel the intricate ma-
chinery underlying astroglial pathophysiology in MS by 
addressing the challenge of their complex, sponge-like 
morphology. This work systematically assessed the multi-
scale morphology of astroglia to create a realistic multi-
compartment cell model for biophysical interrogation 

within the NEURON computational environment. As a 
proof of concept, we simulated two neocortex astrocytes 
in a virtual environment, subjecting them to a series of 
imaging experiments. This allowed us to reveal crucial 
aspects of astroglial pathophysiology that are challeng-
ing to access through empirical methods. These findings 
encompassed spatiotemporal dynamics of intracellular K+ 

and Na+ redistribution, essential Ca2+ buffering properties, 
as well as the effects of demyelinated and remyelinated 
axon conductance and the influence of inflammatory cy-
tokines such as IL1β/6 and TNFα. We aimed to create a 
modeling approach that faithfully replicates the intricate 
morphology of astrocytes across multiple scales while 
retaining the full capabilities of biophysical simulations 
provided by NEURON.

A. Significance of Morphology in MS

Brain astroglia has a distinct morphology compared 
to nerve cells due to their complex system of nanoscopic 
processes that fill tissue volume between branches.3,7 

They are often seen as a cloudy structure around thicker 
branches and do not overlap in tissue domains.8,9 In MS 
lesions, astroglia plays complex roles, influencing inflam-
mation and neuronal repair. The nervous system influences 
the shapeshifting properties of reactive astrocytes, which 
can be influenced by damage severity. Traumatic brain 
injuries can increase GFAP levels, leading to cell-body 
hypertrophy and hot spots of cell proliferation (Figure 
1). The presence of astrocytes near focal lesions can lead 
to “palisades” and decreased astrogliosis hallmarks.3,10,11          

To develop effective therapies targeting astrocytes, a 
deeper understanding of their subtypes and functions 
is essential. Super-resolution imaging techniques hold 
promise in unraveling astrocyte behavior in MS.12 Due 
to the varying cellular mechanisms and morphological 
features of astroglia, it is important to develop a model 
that can explore astroglial functions under pathological 
conditions.

B. Data Selection

In this study, we employed a multifaceted approach to 
investigate the role of cortical astrocytes in the context 
of neocortical lesions associated with MS. Using datasets 
from mouse models, we harnessed advanced imaging 

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 104

techniques to visualize the intricate morphological changes 
of astrocytes within the cortex when neocortical lesions 
are present. For this work, it was selected two datasets 
of astrocytes (physiology and pathophysiology) from 
the neocortex area from the NEUROMORPHO database 
(Tables 1 and 2) for the development of an interactive 
realistic model.13

FIGURE 1. Function of the Astrocyte from Homeostasis to 
Pathology.

TABLE 1. Measurements of physiology cell.

Measurements Data

Soma Surface 67.05 µm2

Number of Stems 13

Number of Bifurcations 742
Number of Branches* 1497

Overall Width 44.67 µm
Overall Height 52.97 µm
Overall Depth 51.07 µm

Average Diameter 0.51 µm
Total Length 8281.73 µm

Total Surface** 11237.8 µm2

Total Volume** 2672.6 µm3

Max Euclidean Distance 41.74 µm
Max Path Distance 63.29 µm
Max Branch Order 27

Average Contraction 0.87

Total Fragmentation 7466
Partition Asymmetry 0.65
Average Rall’s Ratio 1.9

Average Bifurcation Angle Local 65.21°
Average Bifurcation Angle Remote 77.33°

Fractal Dimension 1.1
* Rows highlighted in blue represent the number of branches. 
** Rows highlighted in red correspond to surface area and volume 
parameters, which are utilized in calculating the surface-to-volume 
ratio (SVR).

TABLE 2. Measurements of pathology cell.

Measurements Data

Soma Surface 126.98 µm2

Number of Stems 6

Number of Bifurcations 267
Number of Branches* 540

Overall Width 24.57 µm
Overall Height 67.02 µm
Overall Depth 51.54 µm

Average Diameter 0.64 µm
Total Length 3160.88 µm

Total Surface** 5575.83 µm2

Total Volume** 2820.8 µm3

Max Euclidean Distance 46.74 µm
Max Path Distance 62.2 µm
Max Branch Order 26

Average Contraction 0.83
Total Fragmentation 3764
Partition Asymmetry 0.62
Average Rall’s Ratio 2.08

Average Bifurcation Angle Local 69.86°
Average Bifurcation Angle Remote 75.01°

Fractal Dimension 1.11
* Rows highlighted in blue represent the number of branches. 
** Rows highlighted in red correspond to surface area and volume 
parameters, which are utilized in calculating the surface-to-volume 
ratio (SVR).

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105 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

C. Incorporation of Astrocyte Mechanisms

This section discusses the versatility of models built, 
emphasizing their ability to incorporate numerous NEU-
RON-enabled channel and transporter kinetic mechanisms 
validated through experiments and simulations. Formal 
descriptions of these algorithms are accessible through 
the extensive NEURON database, SenseLab. The model 
includes various channel current and diffusion-reaction 
mechanisms tailored to this study. These mechanisms 
encompass the Kir4.1 potassium current, intracellular K+ 

and Na+ diffusion, the demyelination and remyelination 
axon conductance mechanism, and K+/Na+ extrusion.14, 15 
Gap junction mechanisms are also incorporated, offering 
options for current leakage or diffuse escape.8 Also, we 
delve into the simulation algorithms that govern intracel-
lular Ca2+ dynamics in MSASTRO, including the diffusion 
of Ca2+ among compartments of different sizes. These 
algorithms draw from NEURON Book 24 and are adapted 
from the modified cadifus.mod file.16

D. Generating Complete Astrocyte Morphology

The study’s methods involved setting up the NEURON 
environment, generating astrocyte stem trees through 
various options, and simulating the nanoscopic processes 
within the MSASTRO system.7 Stem trees were selected 
from libraries, generated with endfoot structures, or loaded 
from reconstructed files. Nanoscopic process geometry 
was determined using default statistics or built-in tools. 
Parameters for membrane conductance and dendritic 
geometry were adjusted for accurate simulations. The 
resulting astrocyte models were compared to empirical 
data, and their morphology was refined to achieve align-
ment. Computer simulations were used to analyze sodium 
uptake mechanisms, focusing on the electrochemical 
properties of astrocytes and the Na+, K⁺-ATPase (Figure 2).

High-affinity Ca2+ indicators were employed to trans-
late fluorescence signals into intracellular Ca2+ dynamics, 
requiring in-silico modeling of Ca2+ entry, diffusion, and 
buffering mechanisms. The clustering of Ca2+ channels 
was studied to reveal spatial dynamics and the role of 
channel clusters in Ca2+ signaling (Figure 3).

Architectural characteristics of astroglia were investi-
gated through the examination of tissue volume fraction 

FIGURE 2. Summed distribution of astrocyte intermediate forms 
binding Na+ and K+ versus membrane potential.

FIGURE 3. The visualization of the internal dynamics of Ca2+ 
in a cell is done through the use of dendrites. The black circle 
shows the area of interest, while the right shows the dendrites’ 
d1 and d2.

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(VF) and surface-to-volume ratios (SVR), providing insights 
into morphology and interactions with the surrounding 
environment (Figure 4). Computational modeling of cy-
tokine signaling was conducted, focusing on interactions 
between microglial cytokines and their effects on astroglial 
behavior. Using an ordinary differential equation (ODE) 
model (Equation 1), the research explored the effects of 
autocrine/paracrine microglial cytokine interactions, 
particularly those involved MS, such as TNFα, IL-1β, and 
IL-6. We used a classic S-systems model formulation 
to simulate the expression dynamics of each cytokine 
(Figure 5a).

where Cx = Cx(t) is the expression of cytokine x (TNFα, 
IL-1β, IL-6) that is produced at rate kx upon activation by 
cytokine Ci at time = t − τd,ix. Thus the delay term τd,ix is time 
between the activation of Ci and its subsequent activation 
of Cx. The activation of Cx depends on Ci according to a Hill 
function characterized by half-maximal activation constant 
Kix and cooperativity coefficient nix. Similarly, inhibitory 
cytokine Cj reduces Cx production with time delay τd,jx ac-
cording to a decreasing sigmoidal function characterized 
by Kjx and njx. The degradation of Cx occurred with both 
concentration-dependent and concentration-independent 
components determined by rate constants γx and γss,x, 
respectively. The concentration-independent degradation 
term encompassed the initial value of cytokine x, which 
was set to Css,x = 0.1 for all cytokines, and a degradation 
constant that was set to maintain a constant steady state 
17 in the absence of stimulation.

Lastly, the study explored the requirements for effective 
conduction within astroglia particularly the influence of 
internodal distance on conduction velocity, and assessed 
the impact of parameters like Na+ and K+ channel density 

(1)

(4)

(5)

(2)

(3)

FIGURE 4. NEURON-based astrocyte model: determining 
volumetric quantities.

FIGURE 5. Network model and mathematical simulation of 
complex signaling dynamics cytokines. (a) The literature-based 
network model depicts the activation and inhibition of cytokine 
production. (b) The results of our calibrated model are shown 
along with a saturating stimulus of LPS = 1000 & t = 0.

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107 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

on conduction. In astroglia, ion dynamics is a relatively 
slow process and the simulation trial normally requires 
needs at least 100 seconds.

RESULTS AND DISCUSSION

The results showcased that the chosen rate coefficients 
for isolated astrocytes and the current-voltage (I-V) 
relation were consistent with the physiological implica-
tions of the electrogenic sodium pump. This provided a 
fundamental understanding of sodium dynamics in these 
cells (Figure 2). No step involving binding or dissociation 
between Na+ or K+ and the astrocytes is directly influenced 
by voltage. There is a considerable indirect effect of volt-
age on the binding of Na+ or K+ to the astrocytes, owing 
to the voltage-dependent distribution of intermediates. 
Moving beyond sodium uptake, the research delved into 
the intricate world of astroglial calcium waves. While 
traditionally, slow global calcium elevations were the 
primary indicators of astroglial activity, recent advance-
ments in high-sensitivity Ca2+ imaging revealed faster and 
more localized Ca2+ signals prevalent in smaller processes 
(Figure 3). The VF, which describes the proportion of local 
tissue occupied by astrocytes, was examined to provide 
insights into astrocyte morphology. Similarly, the SVR, a 
key biophysical determinant of a cell's function, was ana-
lyzed to evaluate how astrocyte morphology aligns with 
its surrounding environment. It is not known how SVR 
ranges in neocortex astroglial cells. However, we decided 
to evaluate surface area-to-volume ratios, which can be 
considered a measure of how much the morphology of a 
cell is adapted to interact with its environment. (Tables 
1 and 2) (SVRphysiology = 4.205 μm−1 & SVRpathology = 1.977 
μm−1). This provided quantitative data that shed light on 
the physical interact interactions between astrocytes and 
their surroundings (Figure 4).

The model provided insights into how these cytokines 
may influence astroglial responses under pathological 
conditions, paving the way for a deeper understanding of 
complex cellular interactions (Figure 5). By focusing on 
incorporating relevant mechanisms into the model, the 
study aimed to explore the requirements for effective con-
duction within astroglia. Notably, experimental evidence 
suggests a low Na+ channel density within the internodal 
axolemma (2–6%), potentially acting as a mediator between 

demyelinated regions. Several simulations were conducted 
to scrutinize this possibility. In constructing the model, 
a 12-node axon was designed, with each node divided 
into regions representing demyelinated or remyelinated 
phases. The presence of new Ranvier nodes emerged in 
internodal regions during remyelination, creating short 
internodes. As the remyelination process progressed and 
the lamellae increased, the likelihood of successful Ranvier 
conduction escalated rapidly, although the conduction 
velocity remained low.

The relationship between internodal conduction time 
(ICT) and velocity exhibited a linear trend for small and 
large internodal lengths (L), with the increase in velocity 
observed only for L below 2000 μm (Figure 6). This study 
suggests the significance of internodal distance in conduc-
tion velocity and emphasizes the delicate balance between 
nodal and internodal currents for effective propagation.

CONCLUSION

In summary, this study uses advanced computational 
modeling to explore astroglial physiology and interac-
tions, providing insights into astrocyte function, calcium 
dynamics, tissue architecture, and cytokine signaling. It 
raises questions about how specific inflammatory stimuli 
influence disease outcomes and whether modulating 

FIGURE 6. NEURON-based astrocyte model: determining 
volumetric quantities.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 108

astrocyte responses could have therapeutic potential. 
The study emphasizes the need for comprehensive frame-
works like S-systems to understand cytokine interactions 
and their impact on targets, offering a promising avenue 
for deeper understanding and potential intervention in 
neurological disorders.

ACKNOWLEDGMENT

This work was based on ASTRO by the Department of 
Clinical and Experimental Epilepsy, Institute of Neurol-
ogy, University College of London. The authors thank Dr. 
Leonid Savtchenko for his inspirational support.

REFERENCES

1. Ravi, K., Paidas, M.J., Saad, A., et al. Astrocytes in rare 
neurological conditions: Morphological and functional 
considerations. J Comp Neurol. 2021;529(10):2676–2705. 
https://doi.org/10.1002/cne.25118.

2. KPonath, G., Park, C., Pitt, D. The Role of Astrocytes in 
Multiple Sclerosis. Front Immunol. 2018;9:217. https://
doi.org/10.3389/fimmu.2018.00217.

3. Henstridge, C.M., Tzioras, M., Paolicelli, R.C. Glial Con-
tribution to Excitatory and Inhibitory Synapse Loss in 
Neurodegeneration. Front Cell Neurosci. 2019;13(63). 
https://doi.org/10.3389/fncel.2019.00063.

4. Carlos, R., Gustavo, S., Gaston, K., et al. Brain atro-
phy in Multiple Sclerosis. Am J Psychiatry Neurosci. 
2015;3(3):40–49. https://doi.org/10.11648/j.
ajpn.20150303.11.

5. Correale, J., and Farez, M.F. The Role of Astrocytes in 
Multiple Sclerosis Progression. Front Neurol. 2015; 
6(180). https://doi.org/10.3389/fneur.2015.00180.

6. Wheeler, M.A. and Quintana, F.J. Regulation of As-
trocyte Functions in Multiple Sclerosis. Cold Spring 
Harb Perspect Med. 2019;9(1):a029009. https://doi.
org/10.1101/cshperspect.a029009.

7. Savtchenko, L.P., Bard, L., Jensen, T.P., et al. Disentan-
gling astroglial physiology with a realistic cell model 
in silico. Nat Commun. 2018;9(1):3554. https://doi.
org/10.1038/s41467-018-05896-w. Erratum in: Nat 
Commun. 2019; 10(1):5062. https://doi.org/10.1038/
s41467-019-12712-6.

8. Batiuk, M.Y., Martirosyan, A., Wahis, J., et al. Identifica-
tion of region-specific astrocyte subtypes at single cell 
resolution. Nat Commun. 2020;11(1):1220. https://
doi.org/10.1038/s41467-019-14198-8. 

9. Rusakov, D.A., Bard, L., Stewart, M.G., et al. Diversity of 
astroglial functions alludes to subcellular specialisa-
tion. Trends Neurosci. 2014;37(4):228-42. https://doi.
org/10.1016/j.tins.2014.02.008.

10. Savtchenko, L.P. and Rusakov, D.A. Regulation of rhythm 
genesis by volume-limited, astroglia-like signals in 
neural networks. Philos Trans R Soc Lond B Biol Sci. 
2014; 369(1654):20130614. https://doi.org/10.1098/
rstb.2013.0614.

11. Schiweck., J., Eickholt, B.J., Murk, K. Important Shape-
shifter: Mechanisms Allowing Astrocytes to Respond 
to the Changing Nervous System During Development, 
Injury and Disease. Front Cell Neurosci. 2018;12(261). 
https://doi.org/10.3389/fncel.2018.00261.

12. Zhou, B., Zuo, Y.X., Jiang, R.T. Astrocyte morphology: 
Diversity, plasticity, and role in neurological diseases. 
CNS Neurosci Ther. 2019;25(6):665–673. https://doi.
org/10.1111/cns.13123.

13. Clavreul, S., Abdeladim, L., Hernández-Garzón, E., et al. 
Cortical astrocytes develop in a plastic manner at both 
clonal and cellular levels. Nat Commun. 2019; 10(1):4884. 
https://doi.org/10.1038/s41467-019-12791-5.

14. Fleidervish, I., Lasser-Ross, N., Gutnick, M., et al. Na+ 

imaging reveals little difference in action potential–
evoked Na+ influx between axon and soma. Nat Neu-
rosci. 2010;13(7):852–860. https://doi.org/10.1038/
nn.2574.

15. Hines, M. and Shrager, P. A computational test of the 
requirements for conduction in demyelinated axons. 
Restor Neurol Neurosci. 1991;3(2):81–93. https://doi.
org/10.3233/RNN-1991-3205.

16. Carnevale, N.T. and Hines, M.L. The NEURON Book; 
Cambridge UP: Cambridge, UK; 2006.

17. Furchtgott, L.A., Chow, C.C., Periwal, V. A Model of Liver 
Regeneration. Biophys J. 2009;96(10):3926–3935. 
https://doi.org/10.1016/j.bpj.2009.01.061.

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.1002/cne.25118
https://doi.org/10.3389/fimmu.2018.00217
https://doi.org/10.3389/fimmu.2018.00217
https://doi.org/10.3389/fncel.2019.00063
https://doi.org/10.11648/j.ajpn.20150303.11
https://doi.org/10.11648/j.ajpn.20150303.11
https://doi.org/10.3389/fneur.2015.00180
https://doi.org/10.1101/cshperspect.a029009
https://doi.org/10.1101/cshperspect.a029009
https://doi.org/10.1038/s41467-018-05896-w
https://doi.org/10.1038/s41467-018-05896-w
https://doi.org/10.1038/s41467-019-12712-6
https://doi.org/10.1038/s41467-019-12712-6
https://doi.org/10.1038/s41467-019-14198-8
https://doi.org/10.1038/s41467-019-14198-8
https://doi.org/10.1016/j.tins.2014.02.008
https://doi.org/10.1016/j.tins.2014.02.008
https://doi.org/10.1098/rstb.2013.0614
https://doi.org/10.1098/rstb.2013.0614
https://doi.org/10.3389/fncel.2018.00261
https://doi.org/10.1111/cns.13123
https://doi.org/10.1111/cns.13123
https://doi.org/10.1038/s41467-019-12791-5
https://doi.org/10.1038/nn.2574
https://doi.org/10.1038/nn.2574
https://doi.org/10.3233/RNN-1991-3205
https://doi.org/10.3233/RNN-1991-3205
https://doi.org/10.1016/j.bpj.2009.01.061

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