10th Anniversary of Computation—Computational Biology

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 29664

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina 8, Moscow 119333, Russia
Interests: data-driven modeling; system identification; mathematical immunology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Plant and Environmental Sciences, Weizmann Institute of Science, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel
Interests: genomics; evolution; ancient DNA; population genetics; evolutionary biology

E-Mail Website
Guest Editor
Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
Interests: computational systems biology; bioinformatics; metabolomics; dynamic modelling; synthetic biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The Special Issue "10th Anniversary of Computation—Computational Biology" is a collection of papers that focus on the use of computational methods to analyze biological data and to understand biological systems at various levels. The papers cover a wide range of topics, including gene expression analysis, protein structure prediction, drug design, and systems biology. 

The Special Issue includes papers that use different computational approaches, such as machine learning, data mining, network analysis, and optimization. The papers also cover a wide range of biological applications, such as cancer research, drug discovery, metabolic engineering, and microbiome analysis.

The Special Issue aims to provide a platform for researchers to share their latest findings and insights in the field of computational biology. The papers in the Special Issue are expected to provide new methods, tools, and insights that can help accelerate research in the field of computational biology and ultimately lead to new breakthroughs in biomedical research.

Prof. Dr. Gennady Bocharov
Dr. Fabrizio Mafessoni
Prof. Dr. Rainer Breitling
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. Computation 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 1800 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

  • computational biology
  • systems biology
  • bioinformatics
  • data analysis
  • mathematical modeling
  • genomics
  • pharmacology

Published Papers (18 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

19 pages, 4992 KiB  
Article
Intraplatelet Calcium Signaling Regulates Thrombus Growth under Flow: Insights from a Multiscale Model
by Anass Bouchnita and Vitaly Volpert
Computation 2024, 12(5), 99; https://doi.org/10.3390/computation12050099 - 12 May 2024
Viewed by 243
Abstract
In injured arteries, platelets adhere to the subendothelium and initiate the coagulation process. They recruit other platelets and form a plug that stops blood leakage. The formation of the platelet plug depends on platelet activation, a process that is regulated by intracellular calcium [...] Read more.
In injured arteries, platelets adhere to the subendothelium and initiate the coagulation process. They recruit other platelets and form a plug that stops blood leakage. The formation of the platelet plug depends on platelet activation, a process that is regulated by intracellular calcium signaling. Using an improved version of a previous multiscale model, we study the effects of changes in calcium signaling on thrombus growth. This model utilizes the immersed boundary method to capture the interplay between platelets and the flow. Each platelet can attach to other platelets, become activated, express proteins on its surface, detach, and/or become non-adhesive. Platelet activation is captured through a specific calcium signaling model that is solved at the intracellular level, which considers calcium activation by agonists and contacts. Simulations reveal a contact-dependent activation threshold necessary for the formation of the thrombus core. Next, we evaluate the effect of knocking out the P2Y and PAR receptor families. Further, we show that blocking P2Y receptors reduces platelet numbers in the shell while slightly increasing the core size. An analysis of the contribution of P2Y and PAR activation to intraplatelet calcium signaling reveals that each of the ADP and thrombin agonists promotes the activation of platelets in different regions of the thrombus. Finally, the model predicts that the heterogeneity in platelet size reduces the overall number of platelets recruited by the thrombus. The presented framework can be readily used to study the effect of antiplatelet therapy under different physiological and pathological blood flow, platelet count, and activation conditions. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

14 pages, 1733 KiB  
Article
Physically Informed Deep Learning Technique for Estimating Blood Flow Parameters in Four-Vessel Junction after the Fontan Procedure
by Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
Computation 2024, 12(3), 41; https://doi.org/10.3390/computation12030041 - 25 Feb 2024
Viewed by 1510
Abstract
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of [...] Read more.
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh generation technique based on the parameterization of the junction’s geometry, coupled with an advanced physically regularized neural network architecture. Synthetic datasets are generated through stationary 3D Navier–Stokes simulations within immobile boundaries, offering a precise alternative to resource-intensive computations. A comparative analysis of standard grid sampling and Latin hypercube sampling data generation methods is conducted, resulting in datasets comprising 1.1×104 and 5×103 samples, respectively. The following two families of feed-forward neural networks (FFNNs) are then compared: the conventional “black-box” approach using mean squared error (MSE) and a physically informed FFNN employing a physically regularized loss function (PRLF), incorporating mass conservation law. The study demonstrates that combining PRLF with Latin hypercube sampling enables the rapid minimization of relative error (RE) when using a smaller dataset, achieving a relative error value of 6% on the test set. This approach offers a viable alternative to resource-intensive simulations, showcasing potential applications in patient-specific 1D network models of hemodynamics. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

13 pages, 4648 KiB  
Article
Data-Driven Anisotropic Biomembrane Simulation Based on the Laplace Stretch
by Alexey Liogky and Victoria Salamatova
Computation 2024, 12(3), 39; https://doi.org/10.3390/computation12030039 - 22 Feb 2024
Viewed by 1123
Abstract
Data-driven simulations are gaining popularity in mechanics of biomaterials since they do not require explicit form of constitutive relations. Data-driven modeling based on neural networks lacks interpretability. In this study, we propose an interpretable data-driven finite element modeling for hyperelastic materials. This approach [...] Read more.
Data-driven simulations are gaining popularity in mechanics of biomaterials since they do not require explicit form of constitutive relations. Data-driven modeling based on neural networks lacks interpretability. In this study, we propose an interpretable data-driven finite element modeling for hyperelastic materials. This approach employs the Laplace stretch as the strain measure and utilizes response functions to define constitutive equations. To validate the proposed method, we apply it to inflation of anisotropic membranes on the basis of synthetic data for porcine skin represented by Holzapfel-Gasser-Ogden model. Our results demonstrate applicability of the method and show good agreement with reference displacements, although some discrepancies are observed in the stress calculations. Despite these discrepancies, the proposed method demonstrates its potential usefulness for simulation of hyperelastic biomaterials. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

13 pages, 317 KiB  
Article
Mathematical Modeling of Cell Growth via Inverse Problem and Computational Approach
by Ivanna Andrusyak, Oksana Brodyak, Petro Pukach and Myroslava Vovk
Computation 2024, 12(2), 26; https://doi.org/10.3390/computation12020026 - 3 Feb 2024
Viewed by 1455
Abstract
A simple cell population growth model is proposed, where cells are assumed to have a physiological structure (e.g., a model describing cancer cell maturation, where cells are structured by maturation stage, size, or mass). The main question is whether we can guarantee, using [...] Read more.
A simple cell population growth model is proposed, where cells are assumed to have a physiological structure (e.g., a model describing cancer cell maturation, where cells are structured by maturation stage, size, or mass). The main question is whether we can guarantee, using the death rate as a control mechanism, that the total number of cells or the total cell biomass has prescribed dynamics, which may be applied to modeling the effect of chemotherapeutic agents on malignant cells. Such types of models are usually described by partial differential equations (PDE). The population dynamics are modeled by an inverse problem for PDE in our paper. The main idea is to reduce this model to a simplified integral equation that can be more easily studied by various analytical and numerical methods. Our results were obtained using the characteristics method. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

22 pages, 4834 KiB  
Article
Computer Aided Structure-Based Drug Design of Novel SARS-CoV-2 Main Protease Inhibitors: Molecular Docking and Molecular Dynamics Study
by Dmitry S. Kolybalov, Evgenii D. Kadtsyn and Sergey G. Arkhipov
Computation 2024, 12(1), 18; https://doi.org/10.3390/computation12010018 - 20 Jan 2024
Viewed by 1751
Abstract
Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the [...] Read more.
Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

28 pages, 3679 KiB  
Article
Design of Inhibitors That Target the Menin–Mixed-Lineage Leukemia Interaction
by Moses N. Arthur, Kristeen Bebla, Emmanuel Broni, Carolyn Ashley, Miriam Velazquez, Xianin Hua, Ravi Radhakrishnan, Samuel K. Kwofie and Whelton A. Miller III
Computation 2024, 12(1), 3; https://doi.org/10.3390/computation12010003 - 27 Dec 2023
Viewed by 1762
Abstract
The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL [...] Read more.
The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of −11.0, −10.7, −10.6, −10.2, −10.2, −9.9, −9.9, −9.9, −9.9, and −9.9 kcal/mol, respectively. To confirm the stability of the menin–ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

28 pages, 10033 KiB  
Article
In Silico Identification of Natural Products and World-Approved Drugs Targeting the KEAP1/NRF2 Pathway Endowed with Potential Antioxidant Profile
by Simone Brogi, Ilaria Guarino, Lorenzo Flori, Hajar Sirous and Vincenzo Calderone
Computation 2023, 11(12), 255; https://doi.org/10.3390/computation11120255 - 16 Dec 2023
Cited by 1 | Viewed by 1975
Abstract
In this study, we applied a computer-based protocol to identify novel antioxidant agents that can reduce oxidative stress (OxS), which is one of the main hallmarks of several disorders, including cancer, cardiovascular disease, and neurodegenerative disorders. Accordingly, the identification of novel and safe [...] Read more.
In this study, we applied a computer-based protocol to identify novel antioxidant agents that can reduce oxidative stress (OxS), which is one of the main hallmarks of several disorders, including cancer, cardiovascular disease, and neurodegenerative disorders. Accordingly, the identification of novel and safe agents, particularly natural products, could represent a valuable strategy to prevent and slow down the cellular damage caused by OxS. Employing two chemical libraries that were properly prepared and enclosing both natural products and world-approved and investigational drugs, we performed a high-throughput docking campaign to identify potential compounds that were able to target the KEAP1 protein. This protein is the main cellular component, along with NRF2, that is involved in the activation of the antioxidant cellular pathway. Furthermore, several post-search filtering approaches were applied to improve the reliability of the computational protocol, such as the evaluation of ligand binding energies and the assessment of the ADMET profile, to provide a final set of compounds that were evaluated by molecular dynamics studies for their binding stability. By following the screening protocol mentioned above, we identified a few undisclosed natural products and drugs that showed great promise as antioxidant agents. Considering the natural products, isoxanthochymol, gingerenone A, and meranzin hydrate showed the best predicted profile for behaving as antioxidant agents, whereas, among the drugs, nedocromil, zopolrestat, and bempedoic acid could be considered for a repurposing approach to identify possible antioxidant agents. In addition, they showed satisfactory ADMET properties with a safe profile, suggesting possible long-term administration. In conclusion, the identified compounds represent a valuable starting point for the identification of novel, safe, and effective antioxidant agents to be employed in cell-based tests and in vivo studies to properly evaluate their action against OxS and the optimal dosage for exerting antioxidant effects. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

37 pages, 2321 KiB  
Article
Mathematical Model for Chemical Reactions in Electrolytes Applied to Cytochrome c Oxidase: An Electro-Osmotic Approach
by Shixin Xu, Robert Eisenberg, Zilong Song and Huaxiong Huang
Computation 2023, 11(12), 253; https://doi.org/10.3390/computation11120253 - 11 Dec 2023
Cited by 1 | Viewed by 1565
Abstract
This study introduces a mathematical model for electrolytic chemical reactions, employing an energy variation approach grounded in classical thermodynamics. Our model combines electrostatics and chemical reactions within well-defined energetic and dissipative functionals. Extending the energy variation method to open systems consisting of charge, [...] Read more.
This study introduces a mathematical model for electrolytic chemical reactions, employing an energy variation approach grounded in classical thermodynamics. Our model combines electrostatics and chemical reactions within well-defined energetic and dissipative functionals. Extending the energy variation method to open systems consisting of charge, mass, and energy inputs, this model explores energy transformation from one form to another. Electronic devices and biological channels and transporters are open systems. By applying this generalized approach, we investigate the conversion of an electrical current to a proton flow by cytochrome c oxidase, a vital mitochondrial enzyme contributing to ATP production, the ‘energetic currency of life’. This model shows how the enzyme’s structure directs currents and mass flows governed by energetic and dissipative functionals. The interplay between electron and proton flows, guided by Kirchhoff’s current law within the mitochondrial membrane and the mitochondria itself, determines the function of the systems, where electron flows are converted into proton flows and gradients. This important biological system serves as a practical example of the use of energy variation methods to deal with electrochemical reactions in open systems. We combine chemical reactions and Kirchhoff’s law in a model that is much simpler to implement than a full accounting of all the charges in a chemical system. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

20 pages, 8244 KiB  
Article
Deep Reinforcement Learning for Efficient Digital Pap Smear Analysis
by Carlos Macancela, Manuel Eugenio Morocho-Cayamcela and Oscar Chang
Computation 2023, 11(12), 252; https://doi.org/10.3390/computation11120252 - 10 Dec 2023
Viewed by 1622
Abstract
In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou [...] Read more.
In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap smear samples to identify potentially cancerous cells primarily falls on pathologists—a task known to be exceptionally challenging and time-consuming. This paper proposes a solution to address the shortage of pathologists for cervical cancer screening. It leverages the OpenAI-GYM API to create a deep reinforcement learning environment utilizing liquid-based Pap smear images. By employing the Proximal Policy Optimization algorithm, autonomous agents navigate Pap smear images, identifying cells with the aid of rewards, penalties, and accumulated experiences. Furthermore, the use of a pre-trained convolutional neuronal network like Res-Net50 enhances the classification of detected cells based on their potential for malignancy. The ultimate goal of this study is to develop a highly efficient, automated Papanicolaou analysis system, ultimately reducing the need for human intervention in regions with limited pathologists. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

17 pages, 1501 KiB  
Article
Global Dynamics of a Within-Host Model for Usutu Virus
by Ibrahim Nali and Attila Dénes
Computation 2023, 11(11), 226; https://doi.org/10.3390/computation11110226 - 14 Nov 2023
Cited by 1 | Viewed by 1248
Abstract
We propose a within-host mathematical model for the dynamics of Usutu virus infection, incorporating Crowley–Martin functional response. The basic reproduction number R0 is found by applying the next-generation matrix approach. Depending on this threshold, parameter, global asymptotic stability of one of the [...] Read more.
We propose a within-host mathematical model for the dynamics of Usutu virus infection, incorporating Crowley–Martin functional response. The basic reproduction number R0 is found by applying the next-generation matrix approach. Depending on this threshold, parameter, global asymptotic stability of one of the two possible equilibria is also established via constructing appropriate Lyapunov functions and using LaSalle’s invariance principle. We present numerical simulations to illustrate the results and a sensitivity analysis of R0 was also completed. Finally, we fit the model to actual data on Usutu virus titers. Our study provides new insights into the dynamics of Usutu virus infection. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

20 pages, 390 KiB  
Article
Evaluating the Performance of Multiple Sequence Alignment Programs with Application to Genotyping SARS-CoV-2 in the Saudi Population
by Aminah Alqahtani and Meznah Almutairy
Computation 2023, 11(11), 212; https://doi.org/10.3390/computation11110212 - 1 Nov 2023
Viewed by 2134
Abstract
This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on ClustalΩ, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal [...] Read more.
This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on ClustalΩ, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than ClustalΩ in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. ClustalΩ and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

35 pages, 1670 KiB  
Article
Stability of Impaired Humoral Immunity HIV-1 Models with Active and Latent Cellular Infections
by Noura H. AlShamrani, Reham H. Halawani, Wafa Shammakh and Ahmed M. Elaiw
Computation 2023, 11(10), 207; https://doi.org/10.3390/computation11100207 - 18 Oct 2023
Viewed by 1358
Abstract
This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4+ T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected [...] Read more.
This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4+ T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected cells, HIV-1 particles, and B-cells. We make the assumption that healthy cells can become infected when exposed to: (i) HIV-1 particles resulting from viral infection (VI), (ii) (HIV-1)-latently infected cells due to latent cellular infection (CI), and (iii) (HIV-1)-actively infected cells due to active CI. In the second model, we introduce distributed time-delays. For each of these systems, we demonstrate the non-negativity and boundedness of the solutions, calculate the basic reproductive number, identify all possible equilibrium states, and establish the global asymptotic stability of these equilibria. We employ the Lyapunov method in combination with LaSalle’s invariance principle to investigate the global stability of these equilibrium points. Theoretical findings are subsequently validated through numerical simulations. Additionally, we explore the impact of B-cell impairment, time-delays, and CI on HIV-1 dynamics. Our results indicate that weakened immunity significantly contributes to disease progression. Furthermore, the presence of time-delays can markedly decrease the basic reproductive number, thereby suppressing HIV-1 replication. Conversely, the existence of latent CI spread increases the basic reproductive number, intensifying the progression of HIV-1. Consequently, neglecting latent CI spread in the HIV-1 dynamics model can lead to an underestimation of the basic reproductive number, potentially resulting in inaccurate or insufficient drug therapies for eradicating HIV-1 from the body. These findings offer valuable insights that can enhance the understanding of HIV-1 dynamics within a host. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

16 pages, 5312 KiB  
Article
Mathematical Investigation of the Infection Dynamics of COVID-19 Using the Fractional Differential Quadrature Method
by M. Mohamed, S. M. Mabrouk and A. S. Rashed
Computation 2023, 11(10), 198; https://doi.org/10.3390/computation11100198 - 4 Oct 2023
Cited by 2 | Viewed by 1358
Abstract
In recent times, the global community has been faced with the unprecedented challenge of the coronavirus disease (COVID-19) pandemic, which has had a profound and enduring impact on both global health and the global economy. The utilization of mathematical modeling has become an [...] Read more.
In recent times, the global community has been faced with the unprecedented challenge of the coronavirus disease (COVID-19) pandemic, which has had a profound and enduring impact on both global health and the global economy. The utilization of mathematical modeling has become an essential instrument in the characterization and understanding of the dynamics associated with infectious illnesses. In this study, the utilization of the differential quadrature method (DQM) was employed in order to anticipate the characterization of the dynamics of COVID-19 through a fractional mathematical model. Uniform and non-uniform polynomial differential quadrature methods (PDQMs) and a discrete singular convolution method (DSCDQM) were employed in the examination of the dynamics of COVID-19 in vulnerable, exposed, deceased, asymptomatic, and recovered persons. An analysis was conducted to compare the methodologies used in this study, as well as the modified Euler method, in order to highlight the superior efficiency of the DQM approach in terms of code-execution times. The results demonstrated that the fractional order significantly influenced the outcomes. As the fractional order tended towards unity, the anticipated numbers of vulnerable, exposed, deceased, asymptomatic, and recovered individuals increased. During the initial week of the inquiry, there was a substantial rise in the number of individuals who contracted COVID-19, which was primarily attributed to the disease’s high transmission rate. As a result, there was an increase in the number of individuals who recovered, in tandem with the rise in the number of infected individuals. These results highlight the importance of the fractional order in influencing the dynamics of COVID-19. The utilization of the DQM approach, characterized by its proficient code-execution durations, provided significant insights into the dynamics of COVID-19 among diverse population cohorts and enhanced our comprehension of the evolution of the pandemic. The proposed method was efficient in dealing with ordinary differential equations (ODEs), partial differential equations (PDEs), and fractional differential equations (FDEs), in either linear or nonlinear forms. In addition, the stability of the DQM and its validity were verified during the present study. Moreover, the error analysis showed that DQM has better error percentages in many applications than other relevant techniques. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Graphical abstract

20 pages, 7217 KiB  
Article
A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images
by Nahida Nazir, Abid Sarwar, Baljit Singh Saini and Rafeeya Shams
Computation 2023, 11(10), 195; https://doi.org/10.3390/computation11100195 - 3 Oct 2023
Cited by 2 | Viewed by 1752
Abstract
Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance [...] Read more.
Cervical cancer poses a significant global health burden, affecting women worldwide. Timely and accurate detection is crucial for effective treatment and improved patient outcomes. The Pap smear test has long been a standard cytology screening method, enabling early cancer diagnosis. However, to enhance quantitative analysis and refine diagnostic capabilities, precise segmentation of the cervical cytoplasm and nucleus using deep learning techniques holds immense promise. This research focuses on addressing the primary challenge of achieving accurate segmentation in the presence of noisy data commonly encountered in Pap smear images. Poisson noise, a prevalent type of noise, corrupts these images, impairing the precise delineation of the cytoplasm and nucleus. Consequently, segmentation boundaries become indistinct, leading to compromised overall accuracy. To overcome these limitations, the utilization of U-Net, a deep learning architecture specifically designed for automatic segmentation, has been proposed. This approach aims to mitigate the adverse effects of Poisson noise on the digitized Pap smear slides. The evaluation of the proposed methodology involved a dataset of 110 Pap smear slides. The experimental results demonstrate that the proposed approach successfully achieves precise segmentation of the nucleus and cytoplasm in noise-free images. By preserving the boundaries of both cellular components, the method facilitates accurate feature extraction, thus contributing to improved diagnostic capabilities. Comparative analysis between noisy and noise-free images reveals the superiority of the presented approach in terms of segmentation accuracy, as measured by various metrics, including the Dice coefficient, specificity, sensitivity, and intersection over union (IoU). The findings of this study underline the potential of deep-learning-based segmentation techniques to enhance cervical cancer diagnosis and pave the way for improved quantitative analysis in this critical field of women’s health. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

20 pages, 3839 KiB  
Article
MPC Controllers in SIIR Epidemic Models
by Nikita Kosyanov, Elena Gubar and Vladislav Taynitskiy
Computation 2023, 11(9), 173; https://doi.org/10.3390/computation11090173 - 4 Sep 2023
Viewed by 1015
Abstract
Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important [...] Read more.
Infectious diseases are one of the most important problems of the modern world, for example, the periodic outbreaks of coronavirus infections caused by COVID-19, influenza, and many other respiratory diseases have significantly affected the economics of many countries. Hence, it is therefore important to minimize the economic damage, which includes both loss of work and treatment costs, quarantine costs, etc. Recent studies have presented many different models describing the dynamics of virus spread, which help to analyze the epidemic outbreaks. In the current work we focus on finding solutions that are robust to noise and take into account the dynamics of future changes in the process. We extend previous results by using a nonlinear model-predictive-control (MPC) controller to find effective controls. MPC is a computational mathematical method used in dynamically controlled systems with observations to find effective controls. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

11 pages, 2846 KiB  
Article
Genomic Phylogeny Using the MaxwellTM Classifier Based on Burrows–Wheeler Transform
by Jacques Demongeot, Joël Gardes, Christophe Maldivi, Denis Boisset, Kenza Boufama and Imène Touzouti
Computation 2023, 11(8), 158; https://doi.org/10.3390/computation11080158 - 11 Aug 2023
Cited by 1 | Viewed by 1087
Abstract
Background: In present genomes, current relics of a circular RNA appear which could have played a central role as a primitive catalyst of the peptide genesis. Methods: Using a proximity measure to this circular RNA and the distance, a new unsupervised classifier called [...] Read more.
Background: In present genomes, current relics of a circular RNA appear which could have played a central role as a primitive catalyst of the peptide genesis. Methods: Using a proximity measure to this circular RNA and the distance, a new unsupervised classifier called MaxwellTM has been constructed based on the Burrows–Wheeler transform algorithm. Results: By applying the classifier to numerous genomes from various realms (Bacteria, Archaea, Vegetables and Animals), we obtain phylogenetic trees that are coherent with biological trees based on pure evolutionary arguments. Discussion: We discuss the role of the combinatorial operators responsible for the evolution of the genome of many species. Conclusions: We opened up possibilities for understanding the mechanisms of a primitive factory of peptides represented by an RNA ring. We showed that this ring was able to transmit some of its sub-sequences in the sequences of genes involved in the mechanisms of the current ribosomal production of proteins. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

30 pages, 492 KiB  
Article
Computation of the Exact Forms of Waves for a Set of Differential Equations Associated with the SEIR Model of Epidemics
by Nikolay K. Vitanov and Zlatinka I. Dimitrova
Computation 2023, 11(7), 129; https://doi.org/10.3390/computation11070129 - 2 Jul 2023
Cited by 2 | Viewed by 3654
Abstract
We studied obtaining exact solutions to a set of equations related to the SEIR (Susceptible-Exposed-Infectious-Recovered) model of epidemic spread. These solutions may be used to model epidemic waves. We transformed the SEIR model into a differential equation that contained an exponential nonlinearity. This [...] Read more.
We studied obtaining exact solutions to a set of equations related to the SEIR (Susceptible-Exposed-Infectious-Recovered) model of epidemic spread. These solutions may be used to model epidemic waves. We transformed the SEIR model into a differential equation that contained an exponential nonlinearity. This equation was then approximated by a set of differential equations which contained polynomial nonlinearities. We solved several equations from the set using the Simple Equations Method (SEsM). In doing so, we obtained many new exact solutions to the corresponding equations. Several of these solutions can describe the evolution of epidemic waves that affect a small percentage of individuals in the population. Such waves have frequently been observed in the COVID-19 pandemic in recent years. The discussion shows that SEsM is an effective methodology for computing exact solutions to nonlinear differential equations. The exact solutions obtained can help us to understand the evolution of various processes in the modeled systems. In the specific case of the SEIR model, some of the exact solutions can help us to better understand the evolution of the quantities connected to the epidemic waves. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Figure 1

21 pages, 4559 KiB  
Article
Application of Graph Theory and Automata Modeling for the Study of the Evolution of Metabolic Pathways with Glycolysis and Krebs Cycle as Case Studies
by Carlos De Las Morenas Mateos and Rafael Lahoz-Beltra
Computation 2023, 11(6), 107; https://doi.org/10.3390/computation11060107 - 28 May 2023
Cited by 1 | Viewed by 2063
Abstract
Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into [...] Read more.
Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into one or more final products. In a biochemical reaction, the transformation of one metabolite into the next requires a class of proteins called enzymes that are responsible for catalyzing the reaction. Whether by applying differential equations or automata theory, it is not easy to explain how the evolution of metabolic networks could have taken place within living organisms. Obviously, in the past, the assembly of biochemical reactions into a metabolic network depended on the independent evolution of the enzymes involved in the isolated biochemical reactions. In this work, a simulation model is presented where enzymes are modeled as automata, and their evolution is simulated with a genetic algorithm. This protocol is applied to the evolution of glycolysis and the Krebs cycle, two of the most important metabolic networks for the survival of organisms. The results obtained show how Darwinian evolution is able to optimize a biological network, such as in the case of glycolysis and Krebs metabolic networks. Full article
(This article belongs to the Special Issue 10th Anniversary of Computation—Computational Biology)
Show Figures

Graphical abstract

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Stochastic modeling in immunology based on a stage-dependent model with non-Markov constraints for individuals
Authors: Pertsev N.V.; Loginov K.K.
Affiliation: Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences
Abstract: Several stochastic models in immunology are presented. The variables of the models are integer random variables that denote the quantity of individuals (cells and virions) and sets of unique types of individuals that take into account the current state and history of stay of individuals in some stages of their development. A probabilistic description of onestage stochastic model and a numerical simulation algorithm are formulated. The results of numerical simulation of the dynamics of two populations within a two-compartment system and the results of numerical modeling of dynamics of HIV-1 infection in the lymph node are given.

Title: Determination of cellulose fibrils structure and organization in the soil acidobacterium using synchrotron radiation as exemplified by cylindrical symmetry diffraction
Authors: Vlad Kovalenko
Affiliation: N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119991, Moscow
Abstract: In stressful conditions, bacterial cells use various ways to save genetic material in order to continue dividing upon returning to a favorable environment. For the soil bacterium acidobacterium, such a method is the construction of a cellulose chamber. This paper presents a detailed structural study of this cellulose chamber using synchrotron radiation. The diffraction pattern from the 45-day culture of the bacterium acidobacterium was composed of symmetrical radial streaks intersecting in the center of the detector. Such scattering is an example of diffraction from a structure with cylindrical symmetry, according to our assumption, these are cellulose fibers that form a protective chamber envelope and bind neighboring cells into a single cluster. According to the intensity distribution depending on the azimuth angle, the distribution of the deflection angle of individual cellulose microfibers relative to the main direction was calculated. In addition, we have managed to calculate the diameter of a single microfiber forming the basis of the cell membrane and intercellular cluster by modelling the intensity decline along the streaks.

Title: A biomechanical model for joint functioning of neck and shoulder
Authors: A. Yurova; Yu. Vassilevski; A. Gladkov; E. Kalinskiy; A. Lychagin
Affiliation: Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina str., 8, 119333 Moscow, Russia
Abstract: We present a biomechanical model of the joint functioning of shoulder and neck sections which includes anatomically correct geometries and appropriate parameters. Motion capture experiments for a patient were carried out for movements in both sections and tensions which appear in muscles during these movements were calculated.

Title: Data-driven anisotropic biomembrane simulation based on the Laplace stretch
Authors: Alexey Liogky; Victoria Salamatova
Affiliation: Sirius University, Russia
Abstract: Data-driven approaches are gaining popularity in the field of computational mechanics, particularly in hyperelasticity. These approaches, which use neural networks and other data-driven techniques, offer numerous advantages, including the ability to address the non-uniqueness of model parameters. However, the lack of interpretability associated with neural networks raises concerns. In this study, we propose an interpretable data-driven approach to hyperelasticity, which is based on finite element forward simulations. This approach employs Laplace stretch as the strain measure and utilizes response functions as constitutive equations. To validate the method, we apply it to anisotropic membranes using synthetic data derived from the Holzapfel-GasserOgden model. Our results demonstrate the applicability of the method and show good agreement with reference displacements, although some discrepancies are observed in the stress calculations. Despite these discrepancies, our proposed method demonstrates its potential usefulness in the field of hyperelasticity.

Back to TopTop