Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network

Dmitry D. Postnov, Donald J. Marsh, Dmitry E. Postnov, Thomas Hartig Braunstein, Niels-Henrik Holstein-Rathlou, Erik Andreas Martens, Olga Sosnovtseva

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Abstract

Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (mu CT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.
Original languageEnglish
Article numbere1004922
JournalP L o S Computational Biology (Online)
Volume12
Issue number7
Number of pages28
ISSN1553-7358
DOIs
Publication statusPublished - 2016
Externally publishedYes

Cite this

Postnov, D. D., Marsh, D. J., Postnov, D. E., Braunstein, T. H., Holstein-Rathlou, N-H., Martens, E. A., & Sosnovtseva, O. (2016). Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network. P L o S Computational Biology (Online), 12(7), [e1004922]. https://doi.org/10.1371/journal.pcbi.1004922
Postnov, Dmitry D. ; Marsh, Donald J. ; Postnov, Dmitry E. ; Braunstein, Thomas Hartig ; Holstein-Rathlou, Niels-Henrik ; Martens, Erik Andreas ; Sosnovtseva, Olga. / Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network. In: P L o S Computational Biology (Online). 2016 ; Vol. 12, No. 7.
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abstract = "Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (mu CT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.",
author = "Postnov, {Dmitry D.} and Marsh, {Donald J.} and Postnov, {Dmitry E.} and Braunstein, {Thomas Hartig} and Niels-Henrik Holstein-Rathlou and Martens, {Erik Andreas} and Olga Sosnovtseva",
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Postnov, DD, Marsh, DJ, Postnov, DE, Braunstein, TH, Holstein-Rathlou, N-H, Martens, EA & Sosnovtseva, O 2016, 'Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network', P L o S Computational Biology (Online), vol. 12, no. 7, e1004922. https://doi.org/10.1371/journal.pcbi.1004922

Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network. / Postnov, Dmitry D.; Marsh, Donald J.; Postnov, Dmitry E.; Braunstein, Thomas Hartig; Holstein-Rathlou, Niels-Henrik; Martens, Erik Andreas; Sosnovtseva, Olga.

In: P L o S Computational Biology (Online), Vol. 12, No. 7, e1004922, 2016.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Modeling of Kidney Hemodynamics: Probability-Based Topology of an Arterial Network

AU - Postnov, Dmitry D.

AU - Marsh, Donald J.

AU - Postnov, Dmitry E.

AU - Braunstein, Thomas Hartig

AU - Holstein-Rathlou, Niels-Henrik

AU - Martens, Erik Andreas

AU - Sosnovtseva, Olga

PY - 2016

Y1 - 2016

N2 - Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (mu CT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.

AB - Through regulation of the extracellular fluid volume, the kidneys provide important long-term regulation of blood pressure. At the level of the individual functional unit (the nephron), pressure and flow control involves two different mechanisms that both produce oscillations. The nephrons are arranged in a complex branching structure that delivers blood to each nephron and, at the same time, provides a basis for an interaction between adjacent nephrons. The functional consequences of this interaction are not understood, and at present it is not possible to address this question experimentally. We provide experimental data and a new modeling approach to clarify this problem. To resolve details of microvascular structure, we collected 3D data from more than 150 afferent arterioles in an optically cleared rat kidney. Using these results together with published micro-computed tomography (mu CT) data we develop an algorithm for generating the renal arterial network. We then introduce a mathematical model describing blood flow dynamics and nephron to nephron interaction in the network. The model includes an implementation of electrical signal propagation along a vascular wall. Simulation results show that the renal arterial architecture plays an important role in maintaining adequate pressure levels and the self-sustained dynamics of nephrons.

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