Journal of Biological Physics最新文献

We develop a refined quantum framework for the induced-fit model of allosteric enzymes incorporating vibrational exciton (Davydov’s soliton) dynamics and open-system perturbation theory. Using realistic biochemical parameters, we numerically evaluate the excitation conditions and find that under normal assumptions the quantum excitation energy remains orders of magnitude below the threshold needed to drive a stable soliton. This implies that classical Davydov conditions alone are insufficient for enzyme catalysis on sub-nanosecond timescales. To address this, we identify additional factors – multi-state energy accumulation and strong quantum-coherent processes – that could plausibly enhance the effect. We discuss model limitations (e.g., idealized 1D protein chain, neglect of dissipation) and the validity of our assumptions. By modeling allosteric enzymes as quantum multi-particle systems, we represent substrate-induced structural changes as Hamiltonian deformations and calculate transition probabilities and interaction energies that correlate with enzymatic accuracy or error. While Davydov’s soliton offers an appealing formalism, our calculations indicate they are insufficient under naïve parameter choices. Under standard parameters, this mechanism alone is not sufficient and requires auxiliary mechanisms. We present conditions (e.g., multi-state accumulation, enhanced coupling) under which solitonic behaviour might emerge, and propose experiments/simulations to validate these scenarios. This work bridges biophysical mechanisms with quantum mechanics, offering a novel perspective on enzymatic function at the quantum level. Finally, we situate our model within the broader context of macro-quantum effects (quantum coherence, tunneling, superradiance) known in biology, arguing that while Davydov’s soliton remains speculative, related quantum phenomena (e.g., proton tunneling) are well-supported in enzymatic systems.

Estimation of cell stiffness has assumed significance as their difference in cancer and normal cells is being exploited for diagnostic and therapeutic purposes. However, the cell stiffness values reported (in terms of elastic modulus (E)) are largely varied even within the same cell line, hindering their exploitation. The current study aims to dissect and understand the various parameters resulting in these differences. Based on the analysis of E values reported from 1992 to 2025, it is found that the stiffness of breast cancer cell lines decreases in the order MCF-10A > MCF-7 > MDA-MB-231. The effect of anti-cancer strategies on cell stiffness reveals the important role played by the remodeling of the cytoskeleton. The dual role played by the cytoskeleton in maintaining the required stiffness and deformability in cancer cells based on biological conditions is consistently observed through the analyzed studies. The forest plots synthesized propose that although increasing and decreasing the stiffness of cancer cells is a treatment strategy, more studies focus on decreasing the stiffness of cancer cells to limit metastasis. The trend of variation in cell stiffness urges us to hypothesize the existence of a threshold range for mean stiffness values in cancer cells. The insights obtained can serve as a framework for studies related to the stiffness of breast cancer cells and aid researchers in making meaningful assessments of the obtained E values—a criterion emerging as a major player in cancer diagnosis.

It has been known for a long time that osmosis transfers water from low to high salt concentrations, but few simulation studies have been conducted in confined spaces. This study evaluated the osmotic axial pressure at charged disk surfaces within charged cylindrical nanopores, how osmosis influences other axial pressures, and what controls it. The screening charges on the disk surfaces determine the osmotic axial pressures and critically influence the other axial pressures. External factors (electric field and ion concentrations) and internal (disk and pore wall charges) control the screening charges. The external electric field influenced the Coulomb and dielectric axial pressures more by altering the disk screening charges than by acting directly on the fixed charges. In contrast, the pore wall fixed charges influenced the Coulomb and dielectric axial pressures exclusively by altering the disk screening charges, and their influence extended into the pore center (i.e., well beyond the length of the pore wall diffuse double layer). Both pressures increased near the pore center, especially for larger disks, indicating greater screening charge accumulation around larger disks. The axial osmotic pressure was constant along the disk surface, almost to the tip, and was disk size-independent, owing to the radially constant and disk size-independent screening charges at the disk surfaces. Finally, the axial osmotic pressure at the disk surfaces (with dielectric and fluidic pressures) typically counter-balanced the Coulomb pressure that drives the disk translocation through the nanopore, reducing its speed. The disk translocation direction may reverse at low external ion concentrations.

Biofilms are widely present in any environment with water and a substrate, posing microbial contamination risks to flow pipelines. This study established a bacterial biofilm flow growth model based on the experimental phenomena of Bacillus subtilis biofilm in microfluidic channels, combining the principles of cellular automata with the finite element method. In the model, the hydrodynamic model was developed using the COMSOL platform to analyze the flow field distribution characteristics induced by micropost. A cellular automata model was developed in MATLAB, innovatively incorporating a flow direction weight algorithm and a filamentous growth mode. The study focused on the attachment behavior of biofilms in microfluidic channels, and simulations of biofilm growth in microfluidic channels with different micropost structures were conducted. The model successfully reproduced key experimental phenomena, such as the attachment and growth of filamentous structures and the aggregation of streamer-like biofilms. By combining real-time flow field analysis with the model, the attachment and growth mechanism of biofilm in the micropillar-flow system was revealed. The spatial arrangement of microposts affects the flow paths of free bacteria by altering streamline distribution. The secondary flow induced by the micropillars promotes bacterial attachment, and its spatial distribution characteristics determine the initial attachment sites of bacteria. This study provides a reference for preventing biofilm formation in flow pipelines and reducing the risk of microbial contamination in similar devices.

Exosomes released by epithelial keratinocytes and dermal fibroblasts significantly accelerate wound healing. Moreover, endogenous electric fields (EFs) were demonstrated to promote wound healing by directing the migration of epidermal cells toward the wound center, it is currently unclear whether EFs may facilitate wound healing by regulating the secretion of exosomes in these cells. In this study, we demonstrated that physiological-intensity EFs significantly enhanced exosome secretion from HaCaT cells, with the total protein content of the exosomes increased by approximately 1.5 times higher than that of the control group. Additionally, the exosomes derived from EF-stimulated HaCaT cells accelerated the wound healing rate of HaCaT and HSF cells, and the wound closure rate increased by approximately 20%. Mechanistically, we identified that EFs regulated exosome secretion by influencing the expression of exosome-related proteins—including ALIX and TSG101. Overall, our research results indicate that the electric field is an effective regulatory factor for enhancing exosome secretion and establish a novel high-exosome-producing strategy based on bioelectrics. This may lay the foundation for the translational application of exosomes in wound healing and other fields.

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Interest in studying the interaction of small molecules with DNA is caused by the need to develop new, highly effective, and low-toxic drugs for cancer treatment. The strong and highly specific binding of thionine with DNA makes it a promising candidate for use in medicine and pharmacology. In this study, DNA-thionine complexes in aqueous solutions were investigated using UV–Vis absorption spectroscopy. The thermal stability of native DNA was studied in a broad range of thionine concentrations. The mechanisms of thionine binding to DNA, depending on the concentration of thionine, have been established. At low thionine concentrations ([c_th] ≤ 1.5 mg/L), thionine molecules intercalate between the base pairs of the DNA double helix. At a thionine concentration of 1.5 – 10 mg/L, the groove binding and external electrostatic interaction of positively charged thionine with negatively charged biopolymer phosphate groups of the DNA backbones is preferable. In all cases, the interaction of thionine with DNA leads to an increase in the thermal stability of the polynucleotide. These findings provide valuable insight into the concentration-dependent molecular mechanisms of DNA-small molecule interactions, supporting the rational design of anticancer and antimicrobial agents, as well as exploiting molecular probes for nucleic acid detection, imaging, and other biomedical applications.

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Turing patterns emerging from the vegetation-water model exhibit complex spatial and networked structures, while parameter identification of these patterns has become a challenging inverse problem. This paper aims to present two types of methods for parameter identification, based on a vegetation-water model coupled with climate data on precipitation, temperature, and carbon dioxide concentration in Zhangye. The statistical approach identifies parameters through handcrafted image feature matching using the distance metric. In addition, the deep learning method is employed for parameter identification, one is the modified ResNet50 with a regression head and integrated regularization to enhance generalization; the other is the improved VGG19 that adopts the Gaussian Error Linear Unit (GELU) function and mixed-precision training for greater efficiency. The identification results show that the deep learning methods achieve superior accuracy and robustness compared to the statistical approach, and ResNet50 achieves the best overall performance. Normalized difference vegetation index (NDVI) data further validate the numerical simulation results. Results from parameter identification on patterns enhance the parameterization and predictive capacity of vegetation-water models under climate change.

Regulatory T cells (Treg) and T helper 17 cells (Th17), both derived from naïve T cells, play pivotal roles in modulating immune responses, and their dynamic balance is critical for maintaining immune homeostasis. Existing studies predominantly focus on the regulatory mechanisms of individual cell types and lack a systematic analysis of how multiparametric interactions and stochastic perturbations jointly influence cell-fate equilibrium. In this study, we investigate the gene regulatory network of Treg and Th17 cells in two major aspects: (i) elucidating the dynamical features of the network and (ii) examining the regulatory effects of Gaussian white noise on the balance between the two lineages. By integrating systems dynamics, non-equilibrium mechanics, and stochastic process theory, we propose a unified modeling framework that incorporates Gaussian white noise to simulate stochastic perturbations in gene expression, thereby establishing a mapping between parameter sets and cellular phenotypes and quantifying the regulatory weights of key factors. Our results demonstrate that parameters such as extracellular TGF-β input, foxp3 mRNA synthesis rate, and Stat3 protein degradation rate significantly modulate the differentiation balance between Treg and Th17 cells. Furthermore, within a certain range, stronger Gaussian white noise promotes the differentiation of naïve T cells toward the Th17 lineage, thereby enhancing immune responsiveness. This finding aligns with prior experimental evidence demonstrating that stochastic noise can amplify immune response efficacy. This framework uniquely couples static and dynamic perturbations, revealing stochasticity’s role in cell-fate decisions and offering both a quantitative tool for studying Th17–Treg balance and a generalizable approach for other differentiation systems.

Quantum sensors have emerged as a promising tool in the field of fundamental biophotonics and advanced material science applications. The goal of understanding brain functions at the level of the individual neurons is a major concern of neuroscience. Nitrogen-vacancy (NV) centers in diamond offer a potential quantum sensing approach for recording very weak magnetic fields generated by neurons with remarkable spatial and temporal resolution. The review represents new developments in the use of NV-diamond magnetometry to brain mapping and bioimaging. The current significant advances include diamond nanopillar arrays, wide-field imaging, and NV-based detection of single-neuron signals. The NV magnetometry technique is superior in its ability to combine the spatial resolution of nanoscale with microseconds time resolution, which is better than conventional methods, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG). In the present article, we have discussed some of the issues, such as enhancing biocompatibility, minimizing noise, and combining NV sensors with other neurotechnology. This review provides a summary of the principles, current developments, and outlooks of NV-diamond magnetometry, which can greatly revolutionize the bioimaging and mapping of brain activity.