Simulating of human physiological supersystems: modeling of kidney and bladder functions
A quantitative model describing the functions of human kidney and bladder is created. The model is realized and tested as an autonomous C# software module (SM) functioning under given dynamic input characteristics. Finally, SM will be incorporated into our specialized general software capable of sim...
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pp_isofts_kiev_ua-article-5932024-04-26T21:18:21Z Simulating of human physiological supersystems: modeling of kidney and bladder functions Симулятор фізіологічних надсистем людини: моделювання функцій нирок та січового міхура Grygoryan, R.D. Degoda, A.G. Lyudovyk, T.V. Yurchak, O.I. physical health; kidney; bladder; physiological mechanisms; quantitative model; simulator UDC 517.958:57 +519.711.3 + 612.51.001 фізичне здоров’я, нирка; сечовий міхур; фізіологічні механізми; кількісна модель; симулятор УДК 517.958:57 +519.711.3 + 612.51.001 A quantitative model describing the functions of human kidney and bladder is created. The model is realized and tested as an autonomous C# software module (SM) functioning under given dynamic input characteristics. Finally, SM will be incorporated into our specialized general software capable of simulating the main modes of human integrative physiology, namely, interactions of physiological super-system (PSS). The model of the kidney describes mechanisms of blood filtration in Bowman’s capsule, reabsorption in collecting tubules, as well as the central renin-angiotensin system mechanism. The model of the bladder describes the dynamics of its filling and periodic emptying. Each act of bladder emptying is initiated by a signal generated by the brain in response to afferent impulse patterns from the bladder’s mechanoreceptors. Models have been tested using algorithms that design scenarios, including simulation of either short-time or long-time (hours or days) observations. Input data include different combinations of pressure in renal afferent arterioles, osmotic, and oncotic blood pressures. Output data includes dynamics of primary urine, final urine, bladder volume, urine pressure, mechanoreceptors’ activity, renin production velocity, blood renin concentration, angiotensin2 production velocity, and blood angiotensin2 concentration, as well as blood albumin and sodium concentrations. Both student-medics and physiologists interested in providing theoretical research can be users of SM.Prombles in programming 2023; 4: 56-64 Створено кількісну модель, що описує функції нирок і сечового міхура людини. Модель реалізовано та протестовано як автономний програмний модуль C# (SM), що функціонує при заданих динамічних вхідних характеристиках. Пізніше SM буде включено в наше спеціалізоване програмне забезпечення, що буде здатне моделювати основні режими інтегративної фізіології людини, а саме взаємодії фізіологічної суперсистеми (PSS). Модель нирок описує механізми фільтрації крові в капсулі Боумена, реабсорбції в збиральних канальцях, а також механізм центральної ренін-ангіотензинової системи. Модель сечового міхура описує динаміку його наповнення та періодичного випорожнення. Кожен акт випорожнення сечового міхура ініціюється сигналом, який генерується мозком у відповідь на аферентні імпульси від механорецепторів сечового міхура. Моделі були перевірені з використанням алгоритмів, які розробляють сценарії, в тому числі симуляцію короткочасних або тривалих (години або дні) спостережень. Вхідні дані включають різні комбінації тиску крові в ниркових аферентних артеріолах, осмотичний і онкотичний тиски крові. Вихідні дані включають динаміку первинної сечі, кінцевої сечі, об’єму сечового міхура, тиску сечі, активності механорецепторів, швидкості продукції реніну, концентрації реніну в крові, швидкості продукції ангіотензину2, концентрації ангіотензину2 в крові, а також концентрації альбуміну та натрію в крові. Користувачами СМ можуть бути як студенти-медики, так і фізіологи, зацікавлені у проведенні теоретичних досліджень.Prombles in programming 2023; 4: 56-64 Інститут програмних систем НАН України 2023-12-18 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/593 10.15407/pp2023.04.056 PROBLEMS IN PROGRAMMING; No 4 (2023); 56-64 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 4 (2023); 56-64 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 4 (2023); 56-64 1727-4907 10.15407/pp2023.04 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/593/642 Copyright (c) 2023 PROBLEMS IN PROGRAMMING |
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Problems in programming |
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physical health kidney bladder physiological mechanisms quantitative model simulator UDC 517.958:57 +519.711.3 + 612.51.001 |
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physical health kidney bladder physiological mechanisms quantitative model simulator UDC 517.958:57 +519.711.3 + 612.51.001 Grygoryan, R.D. Degoda, A.G. Lyudovyk, T.V. Yurchak, O.I. Simulating of human physiological supersystems: modeling of kidney and bladder functions |
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physical health kidney bladder physiological mechanisms quantitative model simulator UDC 517.958:57 +519.711.3 + 612.51.001 фізичне здоров’я нирка; сечовий міхур; фізіологічні механізми; кількісна модель; симулятор УДК 517.958:57 +519.711.3 + 612.51.001 |
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Article |
| author |
Grygoryan, R.D. Degoda, A.G. Lyudovyk, T.V. Yurchak, O.I. |
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Grygoryan, R.D. Degoda, A.G. Lyudovyk, T.V. Yurchak, O.I. |
| author_sort |
Grygoryan, R.D. |
| title |
Simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_short |
Simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_full |
Simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_fullStr |
Simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_full_unstemmed |
Simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_sort |
simulating of human physiological supersystems: modeling of kidney and bladder functions |
| title_alt |
Симулятор фізіологічних надсистем людини: моделювання функцій нирок та січового міхура |
| description |
A quantitative model describing the functions of human kidney and bladder is created. The model is realized and tested as an autonomous C# software module (SM) functioning under given dynamic input characteristics. Finally, SM will be incorporated into our specialized general software capable of simulating the main modes of human integrative physiology, namely, interactions of physiological super-system (PSS). The model of the kidney describes mechanisms of blood filtration in Bowman’s capsule, reabsorption in collecting tubules, as well as the central renin-angiotensin system mechanism. The model of the bladder describes the dynamics of its filling and periodic emptying. Each act of bladder emptying is initiated by a signal generated by the brain in response to afferent impulse patterns from the bladder’s mechanoreceptors. Models have been tested using algorithms that design scenarios, including simulation of either short-time or long-time (hours or days) observations. Input data include different combinations of pressure in renal afferent arterioles, osmotic, and oncotic blood pressures. Output data includes dynamics of primary urine, final urine, bladder volume, urine pressure, mechanoreceptors’ activity, renin production velocity, blood renin concentration, angiotensin2 production velocity, and blood angiotensin2 concentration, as well as blood albumin and sodium concentrations. Both student-medics and physiologists interested in providing theoretical research can be users of SM.Prombles in programming 2023; 4: 56-64 |
| publisher |
Інститут програмних систем НАН України |
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2023 |
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https://pp.isofts.kiev.ua/index.php/ojs1/article/view/593 |
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Методи та засоби комп’ютерного моделювання
56
УДК 517.958:57 +519.711.3 + 612.51.001 http://doi.org/10.15407/pp2023.04.056
R.D. Grygoryan, A.G. Degoda, T.V. Lyudovyk, O.I.Yurchak
SIMULATING OF HUMAN PHYSIOLOGICAL SUPERSYSTEMS:
MODELING OF KIDNEY AND BLADDER FUNCTIONS
A quantitative model describing the functions of human kidney and bladder is created. The model is realized
and tested as an autonomous C# software module (SM) functioning under given dynamic input characteris-
tics. Finally, SM will be incorporated into our specialized general software capable of simulating the main
modes of human integrative physiology, namely, interactions of physiological super-system (PSS). The
model of the kidney describes mechanisms of blood filtration in Bowman’s capsule, reabsorption in collect-
ing tubules, as well as the central renin-angiotensin system mechanism. The model of the bladder describes
the dynamics of its filling and periodic emptying. Each act of bladder emptying is initiated by a signal gen-
erated by the brain in response to afferent impulse patterns from the bladder’s mechanoreceptors. Models
have been tested using algorithms that design scenarios, including simulation of either short -time or long-
time (hours or days) observations. Input data include different combinations of pressure in renal afferent ar-
terioles, osmotic, and oncotic blood pressures. Output data includes dynamics of primary urine, final urine,
bladder volume, urine pressure, mechanoreceptors’ activity, renin production velocity, blood renin concen-
tration, angiotensin2 production velocity, and blood angiotensin2 concentration, as well as blood albumin
and sodium concentrations. Both student-medics and physiologists interested in providing theoretical re-
search can be users of SM.
Keywords: physical health, kidney, bladder, physiological mechanisms, quantitative model, simulator.
Introduction
Human organs and certain anatomical-
functional systems (AFS) form very complex
functional systems known as physiological
super-systems (PSS). The general concept of
human PSS [1-3] explained deep cellular
mechanisms that determine cells interaction
for providing of every AFS’s actually optimal
parameters. However, traditional empiric
physiology possesses not by research tech-
nologies capable of establishing the main
quantitative laws ruling the functionalities of
PSS. Potentially, mathematical models could
help in solving this problem. But traditional
methodology of modeling is focused on creat-
ing models of individual organs functioning
under known input disturbances. Such models
are not suitable for the theoretical study of
integrative physiology. To fill this methodo-
logical gap in, we need models that take into
account both cell-scale autonomous mecha-
nisms and multi-scale multicellular regulatory
mechanisms. Moreover, the methodology
itself must contain a new and explicit under-
standing of the rules for the coexistence of
populations (colonies) of specialized cells.
Namely, such approach to the modeling and
computer simulating of human physiology is
proposed in [4-8]. Their main novelty is in
combining of multi-level physiological mech-
anisms for explaining of organism-scale adap-
tive physiological responses to environmental
alterations. In fact, this approach also creates
potentials for explaining mechanisms that
determine the dynamic multi-parametric
shape of human physical health (HPH). Such
a theoretical fundament is extreme necessary
for the individualization of the medical as-
sessment of HPH.
Special model of the thermoregulatory
system (MT) recently was presented in [8].
The main reason for its creation was that MT
should be compatible with our other models.
For solving our problems in the frame of hu-
man PSS, it is sufficient to have an MT con-
taining three body compartments: a core (it
represents muscle, subcutaneous tissue, and
bone), skin, and blood.
Working versions of autonomous C#
software modules (SM) help us during the
tuning of main model constants and testing
© R.D. Grygoryan, A.G. Degoda, T.V. Lyudovyk, O.I.Yurchak, 2023
ISSN 1727-4907. Проблеми програмування. 2023. №4
Методи та засоби комп’ютерного моделювання
57
special regimes of the model functioning au-
tonomously. Such simulations imitate tradi-
tional physiological investigations in which
the input-output relationships of the isolated
organ have been established using animal-
based experiments.
The goal of this article is to present
our latest development – a complex model
describing main functions of kidneys and a
bladder.
Mathematical model of kidneys
Different models of kidney function
have been proposed to clarify the mechanism
and peculiarities of human kidneys function-
ing under physiological conditions and in
certain diseases. Perhaps, within the frame-
work of this article, it is sufficient to cite the
following publications [9-15].
1. Physiological background
Kidneys represent a specific organ
which has its complex structure and provides
multiple functions. Nephrons are structural-
functional units of every kidney. A nephron in
turn is composed of a Bowman's cap-
sule which surrounds the glomerular capillary
loops and participates in the filtration of blood
from the glomerular capillaries. Bowman's
capsule also creates a urinary space through
which filtrate (primary urine) can enter the
nephron and pass to the proximal convoluted
tubule. The latter provides reabsorption of
about 99% of water and solved chemicals like
glucose, sodium, potassium, magnesium,
chlorine ions. Through convoluted tubule the
final concentrated urine enters and accumu-
lates in the bladder, from where it is evacuat-
ed as it becomes critically full. So, the main
kidney function is in controlling of blood
chemical composition and total blood volume.
As the latter is one of the main determiners of
long-term mean arterial pressure, kidneys
play essential role in both circulation and
blood chemical composition. The latter is
essential for providing due velocities of cell
metabolism.
The model of CVS is presented in
[6,7], thus we omit its description in detail.
Perhaps, it is sufficient to note that our CVS-
model currently is the most complex model,
including mechanisms of circulation’s both
acute and long-term control.
The most distinguishing sign of every
PSS is that it functionally integrates multiple
AFS. The extreme necessity in novel models
is conditioned with the practical impossibility
to provide quantitative empirical research of
complex and multi-level mechanisms govern-
ing the interaction of multiple HPSS in physi-
ological or pathological conditions. At the
same time, it is clear that often complex pa-
thologies appear because of inadequate func-
tioning one or more functional chains of PSS.
So, an alternative – simulative research of
human PSS is highly encouraged.
In parallel with the crating of novel
models, several models created in the frame of
the traditional methodology can be re-vised in
the light of the PSS concept. Our current pub-
lication, presenting such an approach, illus-
trates both the modeling technology and the
main problems arising under its application to
models published earlier [6,7]. These publica-
tions were chosen because AFSs of this PSS
represent organs and systems that are external
providers of cellular metabolism. As to its in-
tracellular optimizers, briefly described in [1]
(only the energy aspect), we intend to create a
special model that has to include also those
nuclear mechanisms that are under cytoplasm-
released adaptation factors. Namely, these fac-
tors increase/decrease the expression of genes
in specific sites of DNA.
Special software does provide both
tunings of models and all procedures ac-
companying a wide range of their simulation
research. Such work is too big to be made
within a usual short-term research project and
described in a usual research article. There-
fore, we are publishing each interim devel-
opmental result that can also be an autono-
mous unit-software. The final purpose of this
multi-stage developmental project is to pro-
vide physiologists-researchers with modern
computer-based information technology with
dual goals. The first goal is simulation re-
search of human certain complex PSSs. The
second goal is to provide professors and med-
ical students with a modern specialized visu-
alization technology capable of helping future
medics to better understand the physiological
basis of non-trivial pathologies.
Методи та засоби комп’ютерного моделювання
58
2. A model of blood filtration in
glomeruli of a nephron
Schematically, the functional model of
kidneys is shown in fig.1.
Fig.1. Functional model of kidneys
The equation for the blood filtration dynamics
in the glomeruli of the nephron causally re-
lates the velocity of filtration rate )(tvk to the
total (hydrostatic, oncotic, and osmotic) pres-
sure )(tP in Bowman's capsule as:
−
=
)()(,0
)()(,)()()(
tPtP
tPtPtvtP
dt
tdv
pu
pukkk
,
)()()()( tPtPtPtP onosh +−= ,
where k - is an approximation constant.
A model of sodium reabsorption in the
collecting tubules of the nephron is described
by a system of differential equations that take
into account urine volume ( uV ) and sodium
concentrations in corresponding sections of
tubules ( NaC , pc
NaC , ct
NaC ):
pc
NapcreabpcabNa
pc CqNaNaC
dt
dNa
−−+= ,,1 ,
ct
u q
dt
dV
= ,
,ct
Nact
u Cq
dt
dNa
=
Hormonal control of the reabsorption
dynamics, based on concentrations of albumin
( Al ) and antidiuretic hormone ( Adh ), is de-
scribed by the following system of equations:
reabdcreabdc NaAlSNa *)( =
,AlDCK
dt
dAl
Al
KA
NaAl −=
,)( *ct
reab
ct
reab qAdhSq =
,AdhDCK
dt
dAdh
Adh
u
NaAdh −=
,reab
pc
reab
ct
reab WWW +=
.pc
Nareab
pc
reabpc CWNa =
The normalized response of the reab-
sorption rate to the hormone concentration is
described by the function n
n
xb
xaxS
+
=)( .
3. A model of the central renin-
angiotensin system
Despite the natural activation of the
central renin-angiotensin system (CRAS)
mechanism plays only a secondary role in the
main function of the kidneys, the fact that the
receptor link of CRAS is located precisely in
the afferent arterioles of the kidneys deserves
our special attention to CRAS. It is one of the
powerful hormonal mechanisms of long-term
regulation of mean arterial pressure (MAP).
The physiology of CRAS begins with
the secretion of renin ( )(tR ) in the kidneys
and ends with the formation of the vasoactive
agent angitonensin2 ( )(tAII ) in the liver.
When modeling, we omit some intermediate
transformations. Using the following system
of equations, we describe the relationship
between the perfusion pressure aa
T
aa PP − in
the kidney afferent arteries and )(tR :
−
−−
=
aa
T
aaut
aa
T
aautaa
T
aaR
PPR
PPRPP
dt
dR
,
,)(
In the second differential equation,
which includes the amount of )(tAII in the
blood concentration of renin, II
utIA is the rate
of angitonensin2 molecules of disintegration.
−
−−
= TII
TIIT
RA
II
RtRA
RtRARR
dt
dA
utI
utI
)(,
)(,)(
.
Методи та засоби комп’ютерного моделювання
59
In these equations, parameters R ,
aa
TP , utR , RA , and TR are approximation
constants.
The last three equations take into ac-
count the effects of angitonensin2 on vascular
parameters in the CVS model:
,)0()( II
iii AUtU −=
,)0()( II
iii ADtD +=
))(),(),(()( tUtDtVtr iiii = ,
where i are approximation constants for each
vascular compartment.
4. A model of bladder’s filling-
emptying
A special model for simulations of the
periodic dynamics of bladder’s filling and
emptying is created. In this model, the volume
of urine, that gradually accumulates in the
bladder and creates pressure in it, is the input
variable. This volume is also an input variable
of bladder’s mechanoreceptors, which have a
sensitivity threshold and a saturation level.
Therefore, when the afferent pulse rate is ap-
proaching to the saturation level, a signal born
in the brain is sent in a downward direction to
the bladder to create a muscular effort for
urine expelling.
For the formal description of these
physiological acts following additional varia-
bles are introduced: 1) bt conditional time
within the filling-emptying cycle; 2) bV blad-
der volume; 3) bP bladder pressure and vol-
ume; 4) bD stiffness of the bladder; 5) br blad-
der outlet valve resistance; 6) bq flow of urine;
7) bmR activity of bladder mechanoreceptors;
8) bP additional pressure.
−−
= minminmaxmax
maxmax
)(,)(
)(,)(
bbbbbbb
bbb
bb VtVtrrr
PtPrtr ,
bct
b qq
dt
dV
−= ,
+
−
=
−
−
S
bb
T
bm
S
bb
tPR
tPR
b
T
bm
bm PtPR
PtP
e
e
tPR
tR
b
T
bm
b
T
bm
)(,
)(,1
1
1
)(,0
)(
))(((
))(((
,
−
=
bbbbbbbb
bbb
bb VtVtDVtV
VtV
tP
0)(),()0)((
0)(,0
)( ,
)(/)()( bbbbbb trtPtq = ,
where T
bmR , max
br , min
br , maxP , min
bV , , S
bP ,
bV0 , and - are approximation constants.
Main simulation results
In the frame of this article, we consid-
er test simulations of the autonomous kidney-
bladder model. In intact organism, values of
input variables are provided by the organs that
are directly or indirectly interacting with kid-
neys.
A simulation of the autonomous kid-
ney-bladder model requires previous setting
of both model constants and values of those
variables that normally have been provided by
other organs. Special window created for
providing the tunings for a simulation is
shown in fig.2. Once set, the input values are
still working for multiple simulations until the
user is interested in simulating of effects
caused by new input data combination. This
article does not provide the reader by simula-
tion data reflecting effects of concrete diseas-
es. All simulations shown below have a goal
to demonstrate the fact that our models and
the software technology, despite being an
interim product yet, are a promising scientific
tool. After a proper modification, the pro-
posed software can be used for mining of ad-
ditional information concerning functions of
kidneys and bladder.
Figures 3-6 illustrate dynamics of a
group of physiological characteristics during
6-hours simulation in human rest condition in
horizontal position.
As shown in fig.3, urine formation de-
pends on differences between primary urine
and water reabsorption.
Методи та засоби комп’ютерного моделювання
60
Fig.2. User interface fragment: special window for setting initial data necessary for processing
the kidney model.
Fig.3. Dynamics of primary urine (Pro-urine) water reabsorption and final urine during six-hours
observation (simulation data).
Fig.4. collects dynamics of bladder
volume, bladder pressure, blood oncotic, and
osmotic pressures. Bladder volume and pres-
sure almost linearly increase with time until
bladder mechanoreceptors (their dynamics see
in fig. 5) reach a critical level for generating a
brain efferent signal (additional pressure to
bladder filling pressure) and opening of blad-
der’s output sphincter. Just after this signal,
bladder volume and pressure almost linearly
decrease to their initial values. This is a simu-
lation of a single cycle of bladder filling-
emptying. According to simulation algorithms,
such cycles can occur many times if the obser-
vation time is bigger than we simulated. Pay
attention that in fig.5 the second variable (ac-
tivity of osmotic receptors) is still at low and
practically stable level. Certainly, in case of
real and variable composition of blood salts,
osmotic receptors will have appropriate activi-
ty and the reflex will play a proper role in arte-
rial pressure, blood circulation, and in kidneys
function. Fig.6 collects three variables: blood
concentrations of albumin, sodium, as well as a
conventional variable characterizing the ener-
gy status of kidneys. It is well known that the
Методи та засоби комп’ютерного моделювання
61
sodium reabsorption in collecting tubules is an
active ion transport against concentration gra-
dients. This is a work provided by use of ATP
molecules. Kidneys, despite their relatively
little mas, are very “expensive” organ requir-
ing about 20% of ATP synthesized in organ-
ism. Namely, this circumstance does play an
essential role in efficiency of organ’s function
under general deficiency of energy sources like
carbohydrates and oxygen. These aspects of
organism-scale functioning cannot be theoreti-
cally analyzed otherwise than using proper
models that describe functions of organs de-
pending on their current energy status.
Fig.4. Dynamics of bladder volume and pressure, blood osmotic and oncotic pressures
during six-hours observation (simulation data).
Fig.5. Dynamics of brain osmoreceptors and bladder mechanoreceptors during six-hours
observation (simulation data).
Методи та засоби комп’ютерного моделювання
62
Fig.6. Dynamics of blood albumin, and sodium concentrations, as well as kidneys energy
status six-hours observation (simulation data).
Fig.7. Dynamics of several hemodynamic variables (heart rate, systolic and diastolic pres-
sures, mean pressure in renal arteries) during six-hours observation (simulation data).
Методи та засоби комп’ютерного моделювання
63
Certainly, our simulator yields much
more output data concerning not only kidney-
bladder functions but also relating to blood
circulation parameters, activities of barore-
ceptors, chemoreceptors, and dynamics of
main endocrine hormones. Most hormones
modulate not only the state of CVS but also
the state of those body structures that concern
the functionality of kidneys. An example of
additional simulation data contains the fig. 7.
It presents dynamics of several hemodynamic
variables (heart rate, systolic and diastolic
pressures, and the mean pressure in renal ar-
teries) during six-hour observation. We do not
present more such additional data for two
main reasons. The first one is already men-
tioned above – the limited paper volume. The
second reason is concerned with the “raw”
state of the model. Values of several constants
and variables are included in the model in
conventional units only. We plan to advance
the final model when all component models
will be created and integrated into the com-
plex simulator of human PSS.
Conclusions
In order to extend the potentials of the
PC-based simulator of the human physiologi-
cal super-system (PSS), a special quantitative
model of the human kidneys and a bladder is
created and mainly tested. The kidneys model
describes: 1) the mechanism of blood filtra-
tion in Bowman’s capsule and dynamics of
primary urine formation; 2) the mechanism of
water and sodium reabsorption in conducting
tubules; 3) the mechanism of bladder dynamic
filling and periodic emptying using afferent
patterns of bladder’s mechanoreceptors; 4) the
central renin-angiotensin-aldosterone (CRAS)
mechanism which is one of main determiners
of long-term MAP. Algorithms provide de-
signing of scenarios including simulation of
either short-time or long-time (hours or days)
observations. Test simulations presented in
the article covering six-hours observation of
kidney-bladder function. Adequateness of
models gives us an opportunity to incorporate
them into special software-modeling research
tool with a final goal to provide theoretical
investigations of human PSS.
In a near future, this simulator is to
be widened by two more models: 1) of
mechanisms controlling lung ventilation; 2)
of mechanisms controlling liver-pancreas
interaction. These additional mechanisms
specifically modulate circulation and cells
metabolism.
References
1. Grygoryan R.D., Sagach V.F. The con-
cept of physiological super-systems: New
stage of integrative physiology. Int. J.
Physiol. and Pathophysiology, 2018:
9,2,169-180.
2. Grygoryan RD. The optimal circulation:
cells contribution to arterial pressure.
N.Y.: Nova Science,2017: 287p. ISBN
978-1-53612-295-4.
3. Grygoryan RD. The Optimal Coexistence
of Cells: How Could Human Cells Create
The Integrative Physiology. J. of Human
Physiol. 2019,1 (01):8-28. DOI
10.30564/jhp.v1i1.1386.
4. Grygoryan R.D. Problem-oriented com-
puter simulators for solving of theoretical
and applied tasks of human physiology.
Problems of programming. 2017, №3,
102-111.
5. Grygoryan R.D., Yurchak O.I., Degoda
A.G., Lyudovyk T.V. Specialized soft-
ware for simulating the multiple control
and modulations of human hemodynam-
ics. Prombles in programming. 2021; 2:
42-53.
DOI: https://doi.org/10.15407/pp2021.02.
042.
6. Grygoryan R.D., Degoda A.G.,
Lyudovyk T.V., Yurchak O.I. Simula-
tions of human hemodynamic responses
to blood temperature and volume chang-
es. Prombles in programming. 2023; 1:
19-29.
DOI: https://doi.org/10.15407/pp2023.01.
019
8. Grygoryan R.D. Modeling of mecha-
nisms providing the overall control of
human circulation. Advances in Human
Physiology Research, 2022,4,5 – 21,
https://doi.org/10.30564/ahpr.v4i1.4763.
Grygoryan R.D., Degoda A.G.,
Lyudovyk T.V., Yurchak O.I. Simulating
of human physiological supersystems: in-
teractions of cardiovascular, thermoregu-
Методи та засоби комп’ютерного моделювання
64
latory and respiratory systems. Problems
of programming. 2023, №3, Р. 81-90.
http://doi.org/10.15407/pp2023.03.81.
9. Zaritski R. M. Models of complex dy-
namics in glomerular filtration rate.
Ph.D. dissertation, State University of
New York, Buffalo, New York,1999.
10. Thomas S.R. Mathematical models for
kidney function focusing on clinical in-
terest. Morphologie, 2019, 103, Issue
343, , 161-168.
https://doi.org/10.1016/j.morpho.2019.
10.043.
11. Sgouralis I, Layton AT. Mathematical
modeling of renal hemodynamics in
physiology and pathophysiology. Math
Biosci. 2015;264:8-20.
doi:10.1016/j.mbs.2015 .02.016.
12. Cupples WA, Braam B. Assessment of
renal autoregulation. Am J Physiol Renal
Physiol. 2007;292(4):F1105–23.
13. Williamson GA, Loutzenhiser R, Wang
X, Griffin K, Bidani AK. Systolic and
mean blood pressures and afferent arteri-
olar myogenic response dynamics: a
modeling approach. Am J Physiol Regul
Integr Comp Phys-
iol. 2008;295(5):R1502–511.
14. Ryu H, Layton AT. Effect of tubular in-
homogeneities on feedback-mediated dy-
namics of a model of a thick ascending
limb. Math Med Biol. 2013;30(3):191–
212.
15. Weinstein, A. M. Mathematical models of
renal fluid and electrolyte transport: ac-
knowledging our uncertainty. American
Journal of Physiology - Renal Physiolo-
gy. 2003, 284, 871–884.
Received: 10.08.2023
About the authors:
Grygoryan Rafik
Department chief, PhD, D-r in biology
Publications number in Ukraine journals -154
Publications number in English journals -49.
Hirsch index – 11
http://orcid.org/0000-0001-8762-733X.
Degoda Anna,
Senior scientist, PhD.
Publications number in Ukraine journals – 17.
Publications number in English journals -1.
Hirsch index – 3.
http://orcid.org/0000-0001-6364-5568.
Lyudovyk Tetyana,
Senior scientist, PhD.
Publications number in Ukraine journals – 32.
Publications number in English journals -17.
Hirsch index – 5.
https://orcid.org/0000-0003-0209-2001.
Yurchak Oksana,
Leading software engineer.
Publications number in Ukraine journals – 6.
Publications number in English journals - 0.
Hirsch index –0.
https://orcid.org/0000-0003-3941-1555.
Place of work:
Institute of software systems
of Ukraine National Academy of Sciences
03187, Кyїv,
Acad. Glushkov avenue, 40,
Phone.: 526 5169.
Е-mail:rgrygoryan@gmail.com,
anna@silverlinecrm.com,
tetyana.lyudovyk@gmail.com,
daravatan@gmail.com
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