Journal of Biological Physics最新文献

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.

Disinhibited brain networks exhibit various forms of spatiotemporal waves, including complex spiral waves that evolve around a fixed spatial locus. In experiments, spiral waves are observed to alter their direction of rotation over time, for instance producing a series of waves with clockwise cycles before switching to waves rotating counterclockwise, or vice-versa. To capture this effect, we developed a model based on the Complex Ginzburg–Landau equation (CGLE). By introducing a modulation in the phase gradient of the complex field that reflects global, time-dependent fluctuations in the surrounding environment, the model produced waves that alternated in their direction of rotation. The rate of alternations was directly proportional to the amplitude of phase modulation. Conditions were explored for the emergence of quasi-stationary frozen waves and noise-induced quenching of spiral waves. Overall, the modified CGLE model provides a candidate mechanism for the emergence of spiral waves where rotational directions are dynamically altered, yielding rich forms of activity that account for the spatiotemporal patterns observed in disinhibited brain circuits.

Trinitrotoluene (TNT) is widely used in military and industrial fields due to its strong explosive properties and chemical stability. However, its persistence in the environment and harmful effects on living organisms make it important to develop sensitive and selective detection methods. Previous research has identified the Escherichia coli genes yadG and aspC as promising components for TNT biosensors, based on their increased gene expression in response to TNT exposure. Although these findings are promising, it is still unclear whether the proteins produced from these genes directly interact with TNT at the molecular level. This study focuses on analyzing the binding interactions between TNT and the protein products of yadG and aspC using computational methods. Molecular docking showed that TNT binds more strongly to yadG (− 6.81 ± 0.02 kcal/mol) than to aspC (− 6.23 ± 0.00 kcal/mol). Further analysis using molecular dynamics simulations with MM-GBSA calculations confirmed that the yadG–TNT complex is more stable, with a binding free energy (ΔG) of − 23.58 kJ/mol, in line with fluorescence data that also indicated stronger binding to yadG. TNT binding to yadG involves aromatic residues (Tyr-106, His-153) and hydrophobic contacts (Ala-150), which may promote π–π stacking and suggest reduced water occupancy. These features highlight key principles for protein engineering and suggest a clear route from computational findings to biosensor development.

A quartz crystal microbalance (QCM) can be used to evaluate the physical properties of cells exposed to anticancer drugs. Mass-sensing techniques on electrodes detect subtle changes in adherent cells during drug treatment, providing insights into physiological, biochemical, and morphological events from a physical perspective. Although these methods have been established in many studies using living cells, their drug responses remain associated with cell viability. This study aimed to measure QCM response by simultaneously monitoring non-viable cell images. Treatment with mitomycin C (MMC) caused the resonant frequency to shift from a decrease to an increase, followed by a delayed rise in the proportion of non-viable cells. By contrast, treatment with 5-fluorouracil (5-FU) produced minimal frequency changes, accompanied by a shorter delay before cell death was observed. The fitting data to the model equations of the cumulative log-normal distribution curve showed a clear difference in parameter values between MMC and 5-FU, indicating distinct cell death processes. These results demonstrate that QCM-based monitoring of physical properties provides complementary information on drug responses and may serve as a useful tool in anticancer drug development.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that destroys joints, and in vitro and in vivo studies have confirmed the significant role of osteoclasts in bone degradation associated with this disease. The receptor activator of nuclear factor-kappa B ligand (RANKL) is associated with osteoclast differentiation and bone degradation in RA. The present study investigated the inhibitory effects of phytocompounds against RANKL. Virtual screening of 10,100 phytochemicals retrieved from the IMPPAT database was performed using AutoDock Vina to identify the top 10 compounds with the best binding scores. The top ten compounds were filtered using the ADME property to identify the most promising lead compound. The lead compound was furthermore analyzed using a 1-µs molecular dynamics simulation with GROMACS to understand the stability of the complex in the system. MM-PBSA was employed for binding energy calculations, and additional post-simulation analyses, including principal component analysis, free energy landscape plotting, and VMD visualization, were performed. Dihydrorobinetin was the most promising inhibitor of the RANKL protein after filtration via ADMET analysis, with a strong binding affinity of − 8.8 kcal/mol, forming four hydrogen bonds. The 1-µs simulation revealed stable binding of dihydrorobinetin with RANKL, and the binding energy calculations performed via the MM-PBSA method showed favorable binding and stability of the complex. This study provides interesting insights into the therapeutic potential of dihydrorobinetin by inducing conformational changes in RANKL to treat bone destruction in RA, laying the groundwork for further experimental validation to confirm its efficacy and clinical potential.