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Advances in Biomathematics, Computational Biology, and Bioengineering

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: 20 April 2026 | Viewed by 4207

Special Issue Editor

Dr. Grzegorz Matyszczak

E-Mail Website
Guest Editor
Faculty of Chemistry, Warsaw University of Technology, 00-664 Warszawa, Poland
Interests: biomathematics; mathematical modeling; computation; nanomaterials; wastewater treatment; machine learning; sonochemistry

Special Issue Information

Dear Colleagues,

Various mathematical models are used to describe diverse phenomena observed at the molecular and cell levels. They are classified based on the types of variables they include, which can be discrete and/or continuous in space, time, and other quantities (such as the size of a population, concentrations of reagents, etc.). Examples include models that use artificial intelligence and cellular automata (e.g., game of life), among others. In this Special Issue, various topics will be covered, such as machine learning and artificial intelligence, nonlinear dynamics and chaos theory, oscillations, differential equations, stochastic processes, complex networks, cellular automata, and other mathematical models. Such models will be considered with regard to their applications in understanding bio-based phenomena, with key areas including (but not limited to) systems biology, cancer development dynamics, computational biology, pattern formation, biochemistry, transport processes in living organisms, bioreactors, and industrial bioprocesses. Manuscripts with both mathematical and bio-based content (at the cell and molecular level) are welcome.

Dr. Grzegorz Matyszczak
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 250 words) can be sent to the Editorial Office for assessment.

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

  • biomathematics
  • machine learning
  • artificial intelligence
  • data science
  • systems biology
  • reaction–diffusion systems
  • nonlinear dynamics
  • bioreactors
  • biochemical reaction engineering

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Published Papers (5 papers)

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Research

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21 pages, 1007 KB  
Article
DD-CC-II: Data Driven Cell–Cell Interaction Inference and Its Application to COVID-19
by Heewon Park and Satoru Miyano
Int. J. Mol. Sci. 2025, 26(20), 10170; https://doi.org/10.3390/ijms262010170 - 19 Oct 2025
Viewed by 511
Abstract
Cell–cell interactions play a pivotal role in maintaining tissue homeostasis and driving disease progression. Conventional Cell–cell interactions modeling approaches depend on ligand–receptor databases, which often fail to capture context-specific or newly emerging signaling mechanisms. To address this limitation, we propose a data-driven computational [...] Read more.
Cell–cell interactions play a pivotal role in maintaining tissue homeostasis and driving disease progression. Conventional Cell–cell interactions modeling approaches depend on ligand–receptor databases, which often fail to capture context-specific or newly emerging signaling mechanisms. To address this limitation, we propose a data-driven computational framework, data-driven cell–cell interaction inference (DD-CC-II), which employs a graph-based model using eigen-cells to represent cell groups. DD-CC-II uses eigen-cells (i.e., functional module within the cell population) to characterize cell groups and construct correlation coefficient networks to model between-group associations. Correlation coefficient networks between eigen-cells are constructed, and their statistical significance is evaluated via over-representation analysis and hypergeometric testing. Monte Carlo simulations demonstrate that DD-CC-II achieves superior performance in inferring CCIs compared with ligand–receptor-based methods. The application to whole-blood RNA-seq data from the Japan COVID-19 Task Force revealed severity stage-specific interaction patterns. Markers such as FOS, CXCL8, and HLA-A were associated with high severity, whereas IL1B, CD3D, and CCL5 were related to low severity. The systemic lupus erythematosus pathway emerged as a potential immune mechanism underlying disease severity. Overall, DD-CC-II provides a data-centric approach for mapping the cellular communication landscape, facilitating a better understanding of disease progression at the intercellular level. Full article
(This article belongs to the Special Issue Advances in Biomathematics, Computational Biology, and Bioengineering)
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18 pages, 4764 KB  
Article
Molecular Docking and Simulation Analysis of Glioblastoma Cell Surface Receptors and Their Ligands: Identification of Inhibitory Drugs Targeting Fibronectin Ligand to Potentially Halt Glioblastoma Pathogenesis
by Mohd Wajid Ali Khan, Mohammad Jahoor Alam, Subuhi Sherwani, Sultan Alouffi, Khalid Al-Motair, Saif Khan and Shahper Nazeer Khan
Int. J. Mol. Sci. 2025, 26(20), 10038; https://doi.org/10.3390/ijms262010038 - 15 Oct 2025
Viewed by 857
Abstract
Glioblastoma (GB) is an aggressive brain cancer with high microvascular proliferation. The pathological angiogenesis leads to accelerated tumour invasion and diffused infiltration into the surrounding brain tissues, with a tragically short survival rate. Various transmembrane proteins, which are embedded on the glioblastoma cancer [...] Read more.
Glioblastoma (GB) is an aggressive brain cancer with high microvascular proliferation. The pathological angiogenesis leads to accelerated tumour invasion and diffused infiltration into the surrounding brain tissues, with a tragically short survival rate. Various transmembrane proteins, which are embedded on the glioblastoma cancer cell surface, interact with diverse extracellular ligands/molecules present in the tumor micro-environment. These ligands play a crucial role in the development, progression, and therapeutic resistance. In the present study, we systematically screened multiple transmembrane protein receptors, and their extracellular ligands involved/implicated in GB cancer cell progression. Additionally, we analyzed the homotypic and heterotypic protein associations within glioblastoma cancer cells to better understand their role in tumor development. Ten well-known and clinically approved GB cancer drugs were selected and retrieved from online databases for molecular docking analyses with extracellular proteins. Among the different ligands analyzed, computational analysis revealed a strong interaction between fibronectin (PDB ID: 3VI4) and the majority of GB surface receptors. Furthermore, molecular docking studies between GB-approved drugs and fibronectin demonstrated the strongest binding interaction with Irinotecan, followed by Etoposide, Vincristine, etc. In conclusion, identification of ligand-drugs interactions provides valuable insights into the mechanisms underlying GB cancer cell development and potential avenues for therapeutic inhibition strategies. Our study demonstrated that Irinotecan, Etoposide, and Vincristine exhibit strong binding interactions with fibronectin, effectively disrupting its interaction with surface receptor(s). Since fibronectin receptor interactions play a crucial role in GB tumor progression, these findings suggest that targeting fibronectin could present a promising strategy to inhibit GB cell proliferation and invasion. Full article
(This article belongs to the Special Issue Advances in Biomathematics, Computational Biology, and Bioengineering)
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28 pages, 10704 KB  
Article
NanoDeNovo: De Novo Design of Anti-Poliovirus I Sabin Strain Nanobodies by Semi-Automated Computational Pipeline
by Danil D. Kotelnikov, Katerina S. Tatarinova and Dmitry D. Zhdanov
Int. J. Mol. Sci. 2025, 26(19), 9262; https://doi.org/10.3390/ijms26199262 - 23 Sep 2025
Viewed by 1128
Abstract
Despite global vaccination efforts, poliomyelitis continues to cause paralytic cases, highlighting the need for alternative therapeutic approaches. Nanobodies offer significant advantages over conventional antibodies due to their small size, stability, and low immunogenicity, yet few have been developed specifically against poliovirus. This study [...] Read more.
Despite global vaccination efforts, poliomyelitis continues to cause paralytic cases, highlighting the need for alternative therapeutic approaches. Nanobodies offer significant advantages over conventional antibodies due to their small size, stability, and low immunogenicity, yet few have been developed specifically against poliovirus. This study presents a fully computational pipeline for de novo design of nanobodies targeting Virus Protein 3 (VP3) of the Poliovirus I Sabin strain. Our integrated approach employed Ig-VAE for scaffold generation, ProteinMPNN and RFantibody for sequence design, tFold-Ab/Ag for structure prediction, multi-platform molecular docking (Rfantibody, Rosetta3, ClusPro2, ReplicaDock 2.0), molecular dynamics simulations, and humanization tools. The pipeline identified three humanized nanobodies (scFv-0389-304-6H, scFv-0389-459-5H, and scFv-0743-166-7/H) that demonstrated strong binding to VP3 with binding free energies of −37.66 ± 10.35, −40.11 ± 20.01, and −48.62 ± 11.21 kcal/mol, respectively. All designs exhibited favorable physicochemical properties and high solubility. Notably, nanobodies humanized prior to CDR-loop design (scFv-0743-166-7/H) showed superior stability, binding affinity, and structural similarity to experimentally validated nanobodies. This work demonstrates the feasibility of a fully computational approach for designing promising nanobodies against viral targets, providing an alternative to traditional methods with potential applications in drug design. Full article
(This article belongs to the Special Issue Advances in Biomathematics, Computational Biology, and Bioengineering)
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35 pages, 9112 KB  
Article
Enhanced Methodology for Peptide Tertiary Structure Prediction Using GRSA and Bio-Inspired Algorithm
by Diego A. Soto-Monterrubio, Hernán Peraza-Vázquez, Adrián F. Peña-Delgado and José G. González-Hernández
Int. J. Mol. Sci. 2025, 26(15), 7484; https://doi.org/10.3390/ijms26157484 - 2 Aug 2025
Viewed by 816
Abstract
Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, [...] Read more.
Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids. Full article
(This article belongs to the Special Issue Advances in Biomathematics, Computational Biology, and Bioengineering)
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Review

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24 pages, 1988 KB  
Review
Self-Guided Molecular Simulation Methods
by Xiongwu Wu and Bernard R. Brooks
Int. J. Mol. Sci. 2025, 26(21), 10410; https://doi.org/10.3390/ijms262110410 - 26 Oct 2025
Viewed by 472
Abstract
This work reviews self-guided (SG) molecular simulation methods and illustrates the characteristics and applications of these methods through several example simulations. The main characteristic of SG methods is that past motion in simulations is used to guide future motion. Two forms of these [...] Read more.
This work reviews self-guided (SG) molecular simulation methods and illustrates the characteristics and applications of these methods through several example simulations. The main characteristic of SG methods is that past motion in simulations is used to guide future motion. Two forms of these methods are self-guided molecular dynamics (SGMD) and self-guided Langevin dynamics (SGLD). SG methods achieve an enhanced conformational search through promoting low-frequency motion. A simple local averaging scheme is used to extract low-frequency properties from past simulation trajectories to promote low-frequency motion, which significantly enhances conformational search efficiency with little overhead in computing cost. Based on a generalized Langevin equation (GLE), an SGLD-GLE simulation method is developed, which has enhanced conformational searching ability and at the same time can vigorously sample the canonical ensemble. A reformulation of the SG methods leads to a quantitative relation between the guiding parameters and the conformational distribution, which allows the SG methods to be combined with the replica exchange scheme to perform replica-exchanging self-guided simulations (RXSGMD/RXSGLD). RXSGMD/RXSGLD are much more efficient than temperature-based replica exchange methods, especially for large systems. Full article
(This article belongs to the Special Issue Advances in Biomathematics, Computational Biology, and Bioengineering)
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