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Mathematical, Computational and Numerical Modeling of Molecular Biomedicine
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Mathematical, Computational and Numerical Modeling of Molecular Biomedicine

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 16101

Special Issue Editor

Dr. Mohammad Rahimi-Gorji

E-Mail Website
Guest Editor
1. Laboratory of Experimental Surgery, Department of Human Structure and Repair, Ghent University, 9000 Ghent, Belgium
2. IBiTech-Biommeda, Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
Interests: computational fluid dynamics (CFD); targeted drug delivery; cancer research; aerosol; fluid dynamics; biofluids

Special Issue Information

Dear Colleagues,

Each year, many in vivo experiments are being performed for various scientific and educational purposes. However, the use of laboratory animals has been challenged due to ethical, economic, and scientific concerns. Moreover, the legislation on the use of laboratory animals has become more stringent over the past years and a lot of progress has been made in the field of alternative methods to animal testing, and many valuable assays have been developed. One of the alternative methods is computational and numerical simulation. Enhancing this method has become more and more attractive, andrequired, in different scientific fields because of its widespread application and its great potential benefits to the research community. It has obtained significant interest in both the medical and engineering communities because of its noninvasive character. This Special Issue aims to explore and highlight the latest pertinent and innovative studies related to mathematical, computational, and numerical methods of molecular biomedicine. Some related topics that can be included in this Special Issue are as follows:

  • Computational fluid dynamics (CFD) in biomedical applications;
  • Computational and mathematical simulation in molecular science;
  • Mathematical approaches in the biomolecular field;
  • Drug delivery and molecular systems;
  • Fluid dynamics, heat transfer, and molecular science;
  • Thermal molecular science;
  • Numerical methods in biomedicine;
  • Nanomedicine;
  • Biomaterials;
  • Biomechanics;
  • Analytical methods in medical applications;
  • Multidisciplinary prolems between engineering and medicine;
  • Aerosol and particle technology in biomolecular and biomedicine sciences.

Dr. Mohammad Rahimi-Gorji
Guest Editor

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • computational fluid dynamics (CFD)
  • molecular science
  • biomedicine
  • mathematical modeling
  • numerical and analytical methods
  • biomedical applications
  • biomaterials
  • nanomedicine
  • biomechanics
  • aerosol and particle technology in biomolecular and biomedicine sciences

Published Papers (5 papers)

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Research

26 pages, 2950 KiB  
Article
Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach
by Malgorzata J. McEvoy, Emilia Sinderewicz, Leo Creedon, Marion McAfee, Agnieszka W. Jonczyk, Katarzyna K. Piotrowska-Tomala and Dariusz J. Skarzynski
Int. J. Mol. Sci. 2021, 22(9), 4888; https://doi.org/10.3390/ijms22094888 - 5 May 2021
Cited by 7 | Viewed by 2131
Abstract
In this paper, newly discovered mechanisms of atresia and cell death processes in bovine ovarian follicles are investigated. For this purpose the mRNA expression of receptor interacting protein kinases 1 and 3 (RIPK1 and RIPK3) of the granulosa and theca cells [...] Read more.
In this paper, newly discovered mechanisms of atresia and cell death processes in bovine ovarian follicles are investigated. For this purpose the mRNA expression of receptor interacting protein kinases 1 and 3 (RIPK1 and RIPK3) of the granulosa and theca cells derived from healthy and atretic follicles are studied. The follicles were assigned as either healthy or atretic based on the estradiol to progesterone ratio. A statistically significant difference was recorded for the mRNA expression of a RIPK1 and RIPK3 between granulosa cells from healthy and atretic follicles. To further investigate this result a systems biology approach was used. The genes playing roles in necroptosis, apoptosis and atresia were chosen and a network was created based on human genes annotated by the IMEx database in Cytoscape to identify hubs and bottle-necks. Moreover, correlation networks were built in the Cluepedia plug-in. The networks were created separately for terms describing apoptosis and programmed cell death. We demonstrate that necroptosis (RIPK—dependent cell death pathway) is an alternative mechanism responsible for death of bovine granulosa and theca cells. We conclude that both apoptosis and necroptosis occur in the granulosa cells of dominant follicles undergoing luteinisation and in the theca cells from newly selected follicles. Full article
(This article belongs to the Special Issue Mathematical, Computational and Numerical Modeling of Molecular Biomedicine)
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24 pages, 7206 KiB  
Article
In Silico Study on Tumor-Size-Dependent Thermal Profiles inside an Anthropomorphic Female Breast Phantom Subjected to Multi-Dipole Antenna Array
by Piotr Gas, Arkadiusz Miaskowski and Mahendran Subramanian
Int. J. Mol. Sci. 2020, 21(22), 8597; https://doi.org/10.3390/ijms21228597 - 14 Nov 2020
Cited by 15 | Viewed by 3049
Abstract
Electromagnetic hyperthermia as a potent adjuvant for conventional cancer therapies can be considered valuable in modern oncology, as its task is to thermally destroy cancer cells exposed to high-frequency electromagnetic fields. Hyperthermia treatment planning based on computer in silico simulations has the potential [...] Read more.
Electromagnetic hyperthermia as a potent adjuvant for conventional cancer therapies can be considered valuable in modern oncology, as its task is to thermally destroy cancer cells exposed to high-frequency electromagnetic fields. Hyperthermia treatment planning based on computer in silico simulations has the potential to improve the localized heating of breast tissues through the use of the phased-array dipole applicators. Herein, we intended to improve our understanding of temperature estimation in an anatomically accurate female breast phantom embedded with a tumor, particularly when it is exposed to an eight-element dipole antenna matrix surrounding the breast tissues. The Maxwell equations coupled with the modified Pennes’ bioheat equation was solved in the modelled breast tissues using the finite-difference time-domain (FDTD) engine. The microwave (MW) applicators around the object were modelled with shortened half-wavelength dipole antennas operating at the same 1 GHz frequency, but with different input power and phases for the dipole sources. The total input power of an eight-dipole antenna matrix was set at 8 W so that the temperature in the breast tumor did not exceed 42 °C. Finding the optimal setting for each dipole antenna from the matrix was our primary objective. Such a procedure should form the basis of any successful hyperthermia treatment planning. We applied the algorithm of multi for multi-objective optimization for the power and phases for the dipole sources in terms of maximizing the specific absorption rate (SAR) parameter inside the breast tumor while minimizing this parameter in the healthy tissues. Electro-thermal simulations were performed for tumors of different radii to confirm the reliable operation of the given optimization procedure. In the next step, thermal profiles for tumors of various sizes were calculated for the optimal parameters of dipole sources. The computed results showed that larger tumors heated better than smaller tumors; however, the procedure worked well regardless of the tumor size. This verifies the effectiveness of the applied optimization method, regardless of the various stages of breast tumor development. Full article
(This article belongs to the Special Issue Mathematical, Computational and Numerical Modeling of Molecular Biomedicine)
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23 pages, 1860 KiB  
Article
A Stochastic Petri Net-Based Model of the Involvement of Interleukin 18 in Atherosclerosis
by Dorota Formanowicz, Agnieszka Rybarczyk, Marcin Radom, Krzysztof Tanaś and Piotr Formanowicz
Int. J. Mol. Sci. 2020, 21(22), 8574; https://doi.org/10.3390/ijms21228574 - 13 Nov 2020
Cited by 10 | Viewed by 1969
Abstract
Interleukin 18 (IL-18) is a proinflammatory and proatherogenic cytokine with pleiotropic properties, which is involved in T and NK cell maturation and the synthesis of other inflammatory cytokines and cell adhesion molecules. It plays a significant role in orchestrating the cytokine cascade, accelerates [...] Read more.
Interleukin 18 (IL-18) is a proinflammatory and proatherogenic cytokine with pleiotropic properties, which is involved in T and NK cell maturation and the synthesis of other inflammatory cytokines and cell adhesion molecules. It plays a significant role in orchestrating the cytokine cascade, accelerates atherosclerosis and influences plaque vulnerability. To investigate the influence of IL-18 cytokine on atherosclerosis development, a stochastic Petri net model was built and then analyzed. First, MCT-sets and t-clusters were generated, then knockout and simulation-based analysis was conducted. The application of systems approach that was used in this research enabled an in-depth analysis of the studied phenomenon. Our results gave us better insight into the studied phenomenon and allow revealing that activation of macrophages by the classical pathway and IL-18-MyD88 signaling axis is crucial for the modeled process. Full article
(This article belongs to the Special Issue Mathematical, Computational and Numerical Modeling of Molecular Biomedicine)
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16 pages, 2547 KiB  
Article
AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks
by Yongbeom Kwon, Woong-Hee Shin, Junsu Ko and Juyong Lee
Int. J. Mol. Sci. 2020, 21(22), 8424; https://doi.org/10.3390/ijms21228424 - 10 Nov 2020
Cited by 57 | Viewed by 5455
Abstract
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to [...] Read more.
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important. Full article
(This article belongs to the Special Issue Mathematical, Computational and Numerical Modeling of Molecular Biomedicine)
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34 pages, 3053 KiB  
Article
A Role of Inflammation and Immunity in Essential Hypertension—Modeled and Analyzed Using Petri Nets
by Dorota Formanowicz, Agnieszka Rybarczyk, Marcin Radom and Piotr Formanowicz
Int. J. Mol. Sci. 2020, 21(9), 3348; https://doi.org/10.3390/ijms21093348 - 9 May 2020
Cited by 12 | Viewed by 2640
Abstract
Recent studies have shown that the innate and adaptive immune system, together with low-grade inflammation, may play an important role in essential hypertension. In this work, to verify the importance of selected factors for the development of essential hypertension, we created a Petri [...] Read more.
Recent studies have shown that the innate and adaptive immune system, together with low-grade inflammation, may play an important role in essential hypertension. In this work, to verify the importance of selected factors for the development of essential hypertension, we created a Petri net-based model and analyzed it. The analysis was based mainly on t-invariants, knockouts of selected fragments of the net and its simulations. The blockade of the renin-angiotensin (RAA) system revealed that the most significant effect on the emergence of essential hypertension has RAA activation. This blockade affects: (1) the formation of angiotensin II, (2) inflammatory process (by influencing C-reactive protein (CRP)), (3) the initiation of blood coagulation, (4) bradykinin generation via the kallikrein-kinin system, (5) activation of lymphocytes in hypertension, (6) the participation of TNF alpha in the activation of the acute phase response, and (7) activation of NADPH oxidase—a key enzyme of oxidative stress. On the other hand, we found that the blockade of the activation of the RAA system may not eliminate hypertension that can occur due to disturbances associated with the osmotically independent binding of Na in the interstitium. Moreover, we revealed that inflammation alone is not enough to trigger primary hypertension, but it can coexist with it. We believe that our research may contribute to a better understanding of the pathology of hypertension. It can help identify potential subprocesses, which blocking will allow better control of essential hypertension. Full article
(This article belongs to the Special Issue Mathematical, Computational and Numerical Modeling of Molecular Biomedicine)
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