Journal of Biological Physics最新文献

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.

Electrophysiological signals in plants, which are a part of the plant electrome, are essential for mediating responses to environmental stimuli but exhibit complex, non-linear dynamics that challenge conventional analyses. Here, we introduce the time dispersion analysis of features (TDAF), a novel method that preserves temporal integrity by assessing the dispersion of signal features over time by segmenting time series and evaluating the temporal evolution of extracted features. Unlike traditional methods, such as moving averages or stationarity-based models, that summarize the signal or lose temporal information, TDAF analyzes the evolution of features over time, maintaining their dynamic structure. We applied TDAF to investigate the effects of a moderate static magnetic field (~ 0.4 mT) on the electrome of common bean plants (Phaseolus vulgaris L.). Signals from 30 plants were recorded before and during magnetic field exposure, generating time series with 225,000 points each. Features such as approximate entropy (ApEn), detrended fluctuation analysis (DFA), fast Fourier transform (FFT), power spectral density (PSD), and average band power (ABP) were analyzed. Our results suggest that magnetic field exposure tends to reduce signal amplitude but preserves the structural complexity and temporal patterns of the electrome, indicating modulation without loss of information processing capacity. TDAF proved effective for detecting subtle physiological changes and offers a valuable tool for advancing plant electrophysiology, bioelectromagnetic research, and studies involving complex and long-duration biological signals.

Collective migration is a crucial mechanism guiding cell movement in developmental processes and disease progression. Understanding the migration behavior of cell clusters is key to advancing our knowledge of morphogenesis, wound healing, and collective cancer invasion. Despite the understanding of the response of single cells to environmental physical cues, the collective behavior of cells in response to different levels of extracellular matrix stiffness is yet to be fully understood. Here, we present a quantitative investigation into how substrate stiffness and cell cluster size modulate the collective behavior and migration dynamics of NIH 3T3 fibroblasts. With the variation of PDMS and curing agent concentrations, two contrasting soft and stiff substrates with different stiffness were developed. Using a combination of atomic force microscopy (AFM) to precisely characterize substrate elastic moduli and time-lapse microscopy for tracking migration parameters, we demonstrate that substrate mechanics and cluster geometry synergistically govern collective behavior. Fibroblast migratory characteristics were greatly improved with increased stiffness and cluster size. Large clusters on stiff substrates exhibited greater circularity (~ 0.8), migration distance, displacement (135.6 µm), directionality (0.81), and velocity (24 µm/h) compared to single cells and small clusters on soft and stiff substrates. Moreover, detailed analysis of cytoskeletal reorganization via actin staining revealed the mechanotransductive pathways that convert physical cues into migratory behavior. These findings provide important insights into how substrate stiffness influences collective cell migration, offering potential applications in elucidating the mechanisms of morphogenesis and the dynamics of collective cell invasion during tumor progression.

Antibiotic resistance in Escherichia coli (E. coli) poses a major public health threat. This study introduces a fractional-order differential equation model incorporating memory effects to analyze resistance and susceptibility dynamics in E. coli populations exposed to Ertapenem, Imipenem, and Meropenem, using real-world data from 2018 to 2023 from a hospital in Northern Cyprus. The model accounts for genetic mutations, horizontal gene transfer, and the decay of resistance. Results indicate a gradual increase in resistance, with higher fractional orders slowing growth rates. Basic reproduction number analysis identifies thresholds for resistance persistence or decline, suggesting that reducing mutation rates and enhancing decay factors can control resistance. Projections forecast an 800% rise in resistance cases by 2030 compared to 2018, underscoring the need for optimized antibiotic stewardship.

Global warming and related greenhouse effects possess significant threats to environmental sustainability. This research investigates the possibility of reducing the greenhouse gas levels and associated ambient temperature by manipulating the plankton population in a given forecasting period. To achieve this goal, an optimal control strategy is developed by Pontryagin’s minimum principle, and it is applied to a recently derived nonlinear marine ecosystem model describing the variation of greenhouse gas levels, ambient temperature, and fish interactions. The main goal is to determine an external plankton generation profile that is expected to reduce the greenhouse gas levels and associated ambient temperature to the highest possible extent. The simulation results reveal that the optimal feeding strategy enables one to achieve a reduction of 54% in greenhouse gas levels and 95% in the associated ambient temperature. This research proposes a biological-based novel control approach that can serve as an alternative solution to environmental degradation.

Nerve conduction velocity studies are essential to understanding neurological disorders like ALS, Guillain-Barré syndrome, Charcot-Marie-Tooth disease, carpal tunnel syndrome, sciatic nerve disorders, and multiple sclerosis, which are marked by slowed signal conduction. Various ions in the extracellular space (ECS) and the nerve fiber regulate signal propagation, making it crucial to analyze ECS’s impact on signal transmission. This study examines how a non-homogeneous extracellular space affects nerve conduction velocity using a modified cable model that incorporates ECS parameters such as its diameter and resistance. The results suggest that a non-homogeneous extracellular space significantly impacts the conduction velocity of propagating signals, leading to variations in the conduction velocity, signal delays, phase shifts, and resonance. The model has been thoroughly examined using various combinations of electrophysiological parameters of the ECS and nerve fibers to simulate a wide range of biological conditions, and the simulated results have been consistent and align with the existing findings.