Multiscale Modeling and Control of Biomedical Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 3411

Special Issue Editors


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Guest Editor
Department of Physical Sciences, MacEwan University, Edmonton, AB T5J 4S2, Canada
Interests: theoretical physics; biophysics; computational physics
Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand
Interests: multiscale modeling; computational physiology; biomechanics

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Cullen College of Engineering, The University of Houston, 3517 Cullen Blvd, Houston, TX 77204, USA
Interests: cardiovascular medicine; drug delivery system design; systems and control

Special Issue Information

Dear Colleagues,

The multiscale modeling and control of biomedical systems is an interdisciplinary field that involves the integration of mathematical modeling, physical and chemical principles, and physiological and biological knowledge to study and manipulate complex biological systems at different spatial and temporal scales. It encompasses a wide range of biomedical applications, including, but not limited to, physiological systems, cellular and molecular systems, neural systems, and diseases.

The approach involves the development of mathematical and computational models that capture the behavior of biological systems at different scales, ranging from molecular and cellular levels to tissue, organ, and whole-body levels. These models, which can be deterministic or stochastic, may incorporate diverse biological phenomena, such as biochemical reactions, transport processes, cellular interactions, and physiological responses, and can provide quantitative predictions of system behaviors under different conditions.

The goal is to provide a quantitative framework for describing the dynamics and interactions of different components within a biological system, and to generate testable hypotheses that can be validated experimentally with broad applications in biomedical research and clinical practice. These models provide powerful tools to advance our understanding of the behavior of complex biological systems and design interventions and therapies to improve human health.

Dr. Vahid Rezania
Dr. Harvey Ho
Dr. Yuncheng Du
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiscale modeling
  • control
  • mathematical modeling
  • biomedical systems
  • computational biology
  • pharmacokinetics
  • bioinformatics
  • tissue engineering
  • therapeutic interventions

Published Papers (3 papers)

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Research

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17 pages, 2996 KiB  
Article
A Linear Fit for Atomic Force Microscopy Nanoindentation Experiments on Soft Samples
by Stylianos Vasileios Kontomaris, Anna Malamou, Andreas Zachariades and Andreas Stylianou
Processes 2024, 12(4), 843; https://doi.org/10.3390/pr12040843 - 22 Apr 2024
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Abstract
Atomic Force Microscopy (AFM) nanoindentation is a powerful technique for determining the mechanical properties of soft samples at the nanoscale. The Hertz model is typically used for data processing when employing spherical indenters for small indentation depths (h) compared to the [...] Read more.
Atomic Force Microscopy (AFM) nanoindentation is a powerful technique for determining the mechanical properties of soft samples at the nanoscale. The Hertz model is typically used for data processing when employing spherical indenters for small indentation depths (h) compared to the radius of the tip (R). When dealing with larger indentation depths, Sneddon’s equations can be used instead. In such cases, the fitting procedure becomes more intricate. Nevertheless, as the h/R ratio increases, the force–indentation curves tend to become linear. In this paper the potential of using the linear segment of the curve (for h > R) to determine Young’s modulus is explored. Force–indentation data from mouse and human lung tissues were utilized, and Young’s modulus was calculated using both conventional and linear approximation methods. The linear approximation proved to be accurate in all cases. Gaussian functions were applied to the results obtained from both classic Sneddon’s equations and the simplified approach, resulting in identical distribution means. Moreover, the simplified approach was notably unaffected by contact point determination. The linear segment of the force–indentation curve in deep spherical indentations can accurately determine the Young’s modulus of soft materials at the nanoscale. Full article
(This article belongs to the Special Issue Multiscale Modeling and Control of Biomedical Systems)
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26 pages, 30020 KiB  
Article
Stochastic Modeling and Simulation of Filament Aggregation in Alzheimer’s Disease
by Vaghawan Prasad Ojha, Shantia Yarahmadian and Madhav Om
Processes 2024, 12(1), 157; https://doi.org/10.3390/pr12010157 - 9 Jan 2024
Viewed by 967
Abstract
Alzheimer’s disease has been a serious problem for humankind, one without a promising cure for a long time now, and researchers around the world have been working to better understand this disease mathematically, biologically and computationally so that a better cure can be [...] Read more.
Alzheimer’s disease has been a serious problem for humankind, one without a promising cure for a long time now, and researchers around the world have been working to better understand this disease mathematically, biologically and computationally so that a better cure can be developed and finally humanity can get some relief from this disease. In this study, we try to understand the progression of Alzheimer’s disease by modeling the progression of amyloid-beta aggregation, leading to the formation of filaments using the stochastic method. In a noble approach, we treat the progression of filaments as a random chemical reaction process and apply the Monte Carlo simulation of the kinetics to simulate the progression of filaments of lengths up to 8. By modeling the progression of disease as a progression of filaments and treating this process as a stochastic process, we aim to understand the inherent randomness and complex spatial–temporal features and the convergence of filament propagation process. We also analyze different reaction events and observe the events such as primary as well as secondary elongation, aggregations and fragmentation using different propensities for different possible reactions. We also introduce the random switching of the propensity at random time, which further changes the convergence of the overall dynamics. Our findings show that the stochastic modeling can be utilized to understand the progression of amyloid-beta aggregation, which eventually leads to larger plaques and the development of Alzheimer disease in the patients. This method can be generalized for protein aggregation in any disease, which includes both the primary and secondary aggregation and fragmentation of proteins. Full article
(This article belongs to the Special Issue Multiscale Modeling and Control of Biomedical Systems)
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Review

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23 pages, 3945 KiB  
Review
Elasticity and Viscoelasticity Imaging Based on Small Particles Exposed to External Forces
by Hasan Koruk and Antonios N. Pouliopoulos
Processes 2023, 11(12), 3402; https://doi.org/10.3390/pr11123402 - 11 Dec 2023
Cited by 1 | Viewed by 1011
Abstract
Particle-mediated elasticity/viscoelasticity imaging has the potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density and viscosity. Here, we investigated elasticity imaging based on small particles located within the tissue and at [...] Read more.
Particle-mediated elasticity/viscoelasticity imaging has the potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density and viscosity. Here, we investigated elasticity imaging based on small particles located within the tissue and at the tissue interface exposed to static/dynamic external loads. First, we discuss elasticity/viscoelasticity imaging methods based on the use of particles (bubbles and rigid spheres) placed within the tissue. Elasticity/viscoelasticity imaging techniques based on the use of particles (bubbles, rigid, and soft spheres) located at the tissue interface are then presented. Based on new advances, we updated some of the models for the responses of the particles placed within the tissue and at the tissue interface available in the literature. Finally, we compared the mathematical models for the particles located within the tissue and at the tissue interface and evaluated the elasticity/viscoelasticity imaging methods based on the use of small particles. This review summarized the methods for measuring the elasticity and viscosity of material using particles exposed to external forces. Remote viscoelasticity imaging can be used to improve material characterization in both medical and industrial applications and will have a direct impact on our understanding of tissue properties or material defects. Full article
(This article belongs to the Special Issue Multiscale Modeling and Control of Biomedical Systems)
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