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Biophysica
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Advances in Computational Biophysics
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Advances in Computational Biophysics

A special issue of Biophysica (ISSN 2673-4125).

Deadline for manuscript submissions: 20 December 2026 | Viewed by 8299

Special Issue Editor

Dr. Aman Ullah

E-Mail Website
Guest Editor
The School of Systems Biology and Bioinformatics and Computational Biology, George Mason University, Fairfax, Manassas, VA 22030, USA
Interests: calcium dynamics; Markov chain models; cell signaling pathways; systems biology; computational modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in computational biophysics explore methodologies that integrate computational models with biological systems. This Special Issue welcomes research on molecular dynamics simulations, machine learning applications in biophysics, the multiscale modelling of biomolecules, and novel computational approaches to studying protein interactions, cellular processes, and complex biological networks. Contributions integrating experimental validation with computational insights are particularly valued. This Special Issue aims to highlight recent advancements that drive forward the predictive and analytical capabilities of biophysics, fostering interdisciplinary collaboration and technological innovation. We encourage submissions that enhance our understanding of biomolecular dynamics, drug discovery, and systems biology through innovative computational techniques.

Dr. Aman Ullah
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. Biophysica is an international peer-reviewed open access semimonthly 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 1200 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 biophysics
  • molecular dynamics simulations
  • machine learning
  • biomolecular dynamics
  • systems biology

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Published Papers (5 papers)

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Research

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18 pages, 8110 KB  
Article
Organelle-Specific Molecular Remodeling in Mouse Brain Microvessels After Ischemic Stroke
by Sumedha Inukollu, Shimantika Maikap, Alexandra Lucaciu, Prathyusha Yamarthi, Anil Annamneedi and Rajkumar Vutukuri
Biophysica 2026, 6(2), 33; https://doi.org/10.3390/biophysica6020033 - 14 Apr 2026
Viewed by 459
Abstract
Ischemic stroke induces complex molecular responses that disrupt subcellular organelles’ function and contribute to brain injury, yet the temporal changes of organelle-specific transcriptomic remodeling remain to be investigated. In this study, we performed in silico analysis of publicly available transcriptomic data from isolated [...] Read more.
Ischemic stroke induces complex molecular responses that disrupt subcellular organelles’ function and contribute to brain injury, yet the temporal changes of organelle-specific transcriptomic remodeling remain to be investigated. In this study, we performed in silico analysis of publicly available transcriptomic data from isolated brain microvessels of transient middle cerebral artery occlusion (tMCAO) mouse model. Using in silico approaches, we analyzed differential gene expression at 24 h (acute phase) and 7 d (intermediate phase) post-stroke, focusing on mitochondria, endoplasmic reticulum (ER), and Golgi apparatus. Functional enrichment (Gene Ontology, KEGG) and protein–protein interaction network analyses were performed. Our analysis of the data revealed that at 24 h post-stroke, all three organelles exhibited marked transcriptional remodeling, where mitochondrial pathways showed disrupted metabolic and redox regulation; ER pathways indicated activation of biosynthetic processes, stress signaling, and ferroptosis; and Golgi-related genes reflected altered vesicular trafficking and glycosylation. By 7 d, mitochondrial alterations subsided, whereas ER and Golgi pathways displayed downregulation of metabolic and neuronal signaling processes, indicating persistent dysfunction and incomplete microvascular recovery. Phase-specific drug–gene interaction analysis will be useful to understand temporal organelle-associated transcriptional organization and to guide future investigations of neurovascular remodeling after ischemic stroke. Full article
(This article belongs to the Special Issue Advances in Computational Biophysics)
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15 pages, 2252 KB  
Article
Evaluating the Effectiveness of Machine Learning for Alzheimer’s Disease Prediction Using Applied Explainability
by Chih-Hao Huang, Feras A. Batarseh and Aman Ullah
Biophysica 2025, 5(4), 54; https://doi.org/10.3390/biophysica5040054 - 12 Nov 2025
Viewed by 1387
Abstract
Early and accurate diagnosis of Alzheimer’s disease (AD) is critical for patient outcomes yet presents a significant clinical challenge. This study evaluates the effectiveness of four machine learning models—Logistic Regression, Random Forest, Support Vector Machine, and a Feed-Forward Neural Network—for the five-class classification [...] Read more.
Early and accurate diagnosis of Alzheimer’s disease (AD) is critical for patient outcomes yet presents a significant clinical challenge. This study evaluates the effectiveness of four machine learning models—Logistic Regression, Random Forest, Support Vector Machine, and a Feed-Forward Neural Network—for the five-class classification of AD stages. We systematically compare model performance under two conditions, one including cognitive assessment data and one without, to quantify the diagnostic value of these functional tests. To ensure transparency, we use SHapley Additive exPlanations (SHAPs) to interpret the model predictions. Results show that the inclusion of cognitive data is paramount for accuracy. The RF model performed best, achieving an accuracy of 84.4% with cognitive data included. Without this, performance for all models dropped significantly. SHAP analysis revealed that in the presence of cognitive data, models primarily rely on functional scores like the Clinical Dementia Rating—Sum of Boxes. In their absence, models correctly identify key biological markers, including PET (positron emission tomography) imaging of amyloid burden (FBB, AV45) and hippocampal atrophy, as the next-best predictors. This work underscores the indispensable role of cognitive assessments in AD classification and demonstrates that explainable AI can validate model behavior against clinical knowledge, fostering trust in computational diagnostic tools. Full article
(This article belongs to the Special Issue Advances in Computational Biophysics)
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15 pages, 2431 KB  
Article
Dynamic Features Control the Stabilization of the Green and Red Forms of the Chromophore in AzamiGreen Fluorescent Protein Variants
by Vladimir B. Krapivin, Roman A. Stepanyuk and Maria G. Khrenova
Biophysica 2025, 5(4), 53; https://doi.org/10.3390/biophysica5040053 - 10 Nov 2025
Viewed by 1367
Abstract
Fluorescent proteins find application as biocompatible, genetically encoded labels for visualization of living organisms tissues. Green fluorescent proteins (GFPs) are the most diverse, but proteins with red fluorescence have advantages, such as lower phototoxicity and better penetration into biological tissues. A promising approach [...] Read more.
Fluorescent proteins find application as biocompatible, genetically encoded labels for visualization of living organisms tissues. Green fluorescent proteins (GFPs) are the most diverse, but proteins with red fluorescence have advantages, such as lower phototoxicity and better penetration into biological tissues. A promising approach is to obtain red fluorescent proteins (RFPs) from GFPs by introducing mutations that stabilize the oxidized chromophore state with an extended conjugated π-system. However, to date this remains a non-trivial task and experimental developments are carried out mainly by random mutagenesis. Development of descriptors obtained in molecular modeling can rationalize this field. Herein, we rely on experimental data on the AzamiGreen fluorescent protein and its variants that are oxidized to the red form. We perform classical molecular dynamics (MD) and combined quantum mechanics/molecular mechanics (QM/MM) simulations to determine structural and dynamic features that govern oxidation. We demonstrate that the red state is predominantly stabilized by interactions of polar lysine residues with chromophore oxygen atoms. Dynamic network analysis demonstrates that in red fluorescent proteins the chromophore motions are correlated with the movement of surrounding protein side chains to a higher extent than in green variants. The presence of different resonance forms of the chromophore determines the fluorescence band maximum value: a decrease in the phenolate form population leads to the red shift. Full article
(This article belongs to the Special Issue Advances in Computational Biophysics)
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Review

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25 pages, 1176 KB  
Review
Integrating AI with Cellular and Mechanobiology: Trends and Perspectives
by Sakib Mohammad, Md Sakhawat Hossain and Sydney L. Sarver
Biophysica 2025, 5(4), 62; https://doi.org/10.3390/biophysica5040062 - 14 Dec 2025
Viewed by 1677
Abstract
Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data [...] Read more.
Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data have made traditional analysis methods difficult to scale. Artificial intelligence (AI) has emerged as a practical tool to address these challenges by providing new methods for interpreting and predicting biological behavior. Recent studies have demonstrated potential in several areas, including image-based analysis of cell and nuclear morphology, traction force microscopy (TFM), cell segmentation, motility analysis, and the detection of cancer biomarkers. Within this context, we review AI applications that either incorporate mechanical inputs/outputs directly or infer mechanobiologically relevant information from cellular and nuclear structure. This study summarizes progress in four key domains: AI/ML-based cell morphology studies, cancer biomarker identification, cell segmentation, and prediction of traction forces and motility. We also discuss the advantages and limitations of integrating AI/ML into mechanobiological research. Finally, we highlight future directions, including physics-informed and hybrid AI approaches, multimodal data integration, generative strategies, and opportunities for computational biophysics-aligned applications. Full article
(This article belongs to the Special Issue Advances in Computational Biophysics)
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38 pages, 1234 KB  
Review
AI-Enhanced Morphological Phenotyping in Humanized Mouse Models: A Transformative Approach to Infectious Disease Research
by Asim Muhammad, Xin-Yu Zheng, Hui-Lin Gan, Yu-Xin Guo, Jia-Hong Xie, Yan-Jun Chen and Jin-Jun Chen
Biophysica 2025, 5(4), 43; https://doi.org/10.3390/biophysica5040043 - 24 Sep 2025
Cited by 2 | Viewed by 2389
Abstract
Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive [...] Read more.
Humanized mouse models offer human-specific platforms for investigating complex host–pathogen interactions, addressing shortcomings of conventional preclinical models that often fail to replicate human immune responses accurately. This integrative review examines the intersection of advanced morphological phenotyping and artificial intelligence (AI) to enhance predictive capacity and translational relevance in infectious disease research. A structured literature search was conducted across PubMed, Scopus, and Web of Science (2010–2025), applying defined inclusion and exclusion criteria. Evidence synthesis highlights imaging modalities, AI-driven phenotyping, and standardization strategies, supported by comparative analyses and quality considerations. Persistent challenges include variability in engraftment, lack of harmonized scoring systems, and ethical governance. We propose recommendations for standardized protocols, risk-of-bias mitigation, and collaborative training frameworks to accelerate adoption of these technologies in translational medicine. Full article
(This article belongs to the Special Issue Advances in Computational Biophysics)
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