Journal of Biological Physics最新文献

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.

The inference of biological networks is essential for understanding the complex regulations among biomolecules. Jacobian matrices serve as an effective means for uncovering network topologies by providing linear approximations of nonlinear regulations. Reconstructing Jacobian matrices often requires integrating experimental data with mathematical modeling techniques. A significant challenge is determining the type of experimental data required and the adequate amount of data to accurately reconstruct the Jacobian matrices. In this paper, we employ multiple pre- and post-perturbation data to infer the Jacobian matrices with the help of Taylor expansions. Furthermore, we integrate the expansions with differential approximations of the partial derivative to offer supplementary information. These data enable accurate inference of not only the signs and directions of regulations but also the strength of self-feedback in both steady-state and oscillatory systems. Comparative analysis reveals that incorporating differential approximations can significantly improve the accuracy of inference.

Bone microdamage, frequently induced by high-impact activities such as military training and sports, poses significant health risks when accumulated over time. However, this microdamage often eludes detection using conventional diagnostic techniques, necessitating the development of innovative, non-destructive testing methods for early diagnosis and prevention. Here, we investigated the complete fatigue fracture process of bone using terahertz time-domain spectroscopy (THz-TDS) to evaluate the effects of varying damage levels, induced by parameters such as the number of cycles and maximum stress, on spectral coefficients. Our findings demonstrate that both the refractive index and absorption coefficient are highly sensitive to the degree of bone damage. Notably, the refractive index exhibited a trend consistent with the Young’s modulus as a function of the number of cycles. These results highlight the potential of THz-TDS as a promising tool for clinical applications, offering novel opportunities for the early detection of bone microdamage and the prevention of fractures.

DNA gyrase is a target for treating tuberculosis caused by Mycobacterium tuberculosis. Many cases of antibiotic resistance have been reported due to different point mutations in the Chain A of DNA gyrase. Based on literature information on drug-resistance related study for DNA gyrase, we generated 4 different mutant models 3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H by inserting two mutations at each position 88 and 94 in DNA gyrase chain A. Antibiotics Clinafloxacin, Gatifloxacin, Moxifloxacin, Sitafloxacin, Prulifloxacin, Besifloxacin, Delafloxacin, Ozenoxacin were docked with 3ILW_wild to understand their stability, binding affinity, and interaction pattern with the wild-type DNA gyrase (3ILW_wild). Delafloxacin has shown more stable and favorable binding interaction with the 3ILW_wild (BFE, -8.6 kcal/mol). Docking of Delafloxacin with four mutant models (3ILW_G88A, 3ILW_G88C, 3ILW_D94G, and 3ILW_D94H) was performed to understand the impact of these mutations on binding stability and interaction. A complete loss of binding interaction with Ser118 and Pro119 was observed in mutant complexes as compared to 3ILW_wild, suggesting the role of these residues in developing resistance. Molecular dynamics simulations over 100 ns were carried out for the complex of Delafloxacin with 3ILW_wild and four mutant models. Parameters like RMSD, RMSF, radius of gyration, H-bond, and solvent-accessible surface area revealed that the mutant models are more rigid and less flexible as compared to wild-type DNA gyrase, which in turn results in loss of some interactions. It is worth noting that mutation at position 94 of DNA gyrase A has a very profound effect as it shows a positive contribution towards increased resistance due to reduced binding affinity with delafloxacin. This study explains the structural changes and mechanism behind the resistance against Delafloxacin, and may also guide the structural changes required in existing Delafloxacin or other antibiotics to develop new therapeutics to overcome the issue of resistance.

Despite the prevalence of co-infection with drug-sensitive and drug-resistant Mycobacterium tuberculosis strains within a single host, the implications of such dual infections remain poorly understood. In this study, we develop a comprehensive within-host model that incorporates both bacterial strains, their mutation dynamics, and cross-reactive immune responses. We analyze the basic reproduction number (( mathcal {R}_0 )) and identify its dependence on key parameters, finding that ( mathcal {R}_0 ) is strongly influenced by the adaptive immune response rate, bacterial fitness cost, and macrophage engulfment rates. Our bifurcation analysis reveals the presence of a backward bifurcation at ( mathcal {R}_0 = 1 ), indicating complex threshold dynamics. Utilizing optimal control theory, we evaluate treatment strategies and demonstrate that a combination therapy with at least 85% efficacy against both strains can effectively control the infection. These findings deepen our understanding of host-pathogen interactions in tuberculosis and provide valuable insights for the development of more effective anti-tuberculosis therapies.

This study investigates the electromagnetohydrodynamic (EMHD) flow of fractional Maxwell fluids through a stenosed artery, accounting for body acceleration. The flow is considered highly pulsatile. The mathematical model is formulated using differential forms of the conservation of mass and momentum. The governing equations are nondimensionalized and simplified by assuming mild stenosis. Through the application of the Caputo fractional derivative, the classical problem is transformed into its fractional equivalent. Solutions are derived using Laplace and finite Hankel transformations, with the inverse Laplace transform applied afterward. The findings show that blood velocity, flow rate, and shear stress fluctuate continuously over time due to the pulsatile flow and the effects of body acceleration.