Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease

Kutumova, Elena; Kiselev, Ilya; Sharipov, Ruslan N.; Лифшиц, Г. И.; Kolpakov, Fedor A.

doi:10.3389/fphys.2021.746300

Cited by 8 publications

(27 citation statements)

References 110 publications

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“…This is a modular agent-based mathematical model of the human cardiovascular and renal systems ( 20 ). We performed the model calibration to find an equilibrium state within the normal vital sign ranges for a healthy adult.…”

Section: Use Casesmentioning

confidence: 99%

BioUML—towards a universal research platform

Kolpakov

Akberdin

Sharipov

2022

Nucleic Acids Research

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BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

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Section: Use Casesmentioning

confidence: 99%

BioUML—towards a universal research platform

Kolpakov

Akberdin

Sharipov

2022

Nucleic Acids Research

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“…BioUML [ 106 ] is an integrated web-based platform for systems biology and data analysis [ 107 ], which has been successfully tested for modeling biological systems [ 108 , 109 ]. It allows to translate PBPK models from text formats (e.g., Berkeley Madonna) into SBML and SBGN standards, making these models accessible to a wide range of scientists and providing a quick-start guide to work with them [ 89 ].…”

Section: Main Pbpk Modeling Softwarementioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova

Akberdin

Kiselev

et al. 2022

IJMS

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Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

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“…Previously created predictive mathematical models either focus on the renal/body fluid system ( Uttamsingh et al, 1985 ; Karaaslan et al, 2005 ; Hallow et al, 2014 ; Karaaslan et al, 2014 ; Hallow and Gebremichael, 2017 ) based on the model by Guyton et al (1972) , or simulate cardiovascular and pulmonary physiology ( Ottesen et al, 2004 ; Proshin and Solodyannikov, 2006 ; Paeme et al, 2011 ; Rosalina et al, 2019 ). Using this, we developed a modular agent-based model of blood pressure regulation ( Kutumova et al, 2021 ) that describes the cardiovascular and renal systems in great detail. The main advantage of the model is that it is thoroughly calibrated.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of antihypertensive therapy

et al. 2022

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Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model’s ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling.

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Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease

Cited by 8 publications

References 110 publications

BioUML—towards a universal research platform

BioUML—towards a universal research platform

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Mathematical modeling of antihypertensive therapy

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