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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: 30 September 2025 | Viewed by 539

Special Issue Editor


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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

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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 (1 paper)

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Research

35 pages, 9112 KiB  
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 381
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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