Journal of Biological Physics最新文献

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. 

Electroporation, a widely used physical method for transiently increasing cell permeability, facilitates molecular delivery for therapeutic and research applications. While electroporation proves to be a useful process, the mechanisms of pore formation and molecular transport remain incompletely understood. This study investigates the dynamics of electropore formation in lipid bilayers using molecular dynamics (MD) simulations and subsequent molecular transport by quantitative diffusion modeling. MD simulations reveal different stages of pore formation under applied electric fields, focusing on the lipid headgroup realignment and the hydration process of the pores. An FDM (Finite Difference Method)-based transport model quantifies the transport of molecules, such as glucose, calcein and bleomycin, using pore dimensions obtained from MD simulations. The results demonstrate a size-dependent transport efficiency, with smaller molecules diffusing more rapidly than larger ones. This work underscores the synergy between atomistic simulations and macroscopic transport modeling. Also, the findings offer valuable insights for optimizing electroporation protocols and developing targeted delivery systems for drugs and genetic material.

Ebola virus disease (EVD) is an acute life-threatening disease caused by highly pathogenic Ebolavirus (EBOV), with reported case fatality rates reaching 90%. There have been numerous EBOV outbreaks and epidemics since the first outbreak was reported in Africa in 1976. Despite the approval of three vaccines and two monoclonal antibody therapies by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of EVD the urgent need for alternative therapeutic strategies persists. In the present study, we screened a library of 235 phytocompounds derived from Azadirachta indica against the key EBOV viral protein 24 (VP24), VP30, VP35 and VP40 through a random forest-based machine learning model with an accuracy of 84.5%. Initially, 48 compounds were identified as active, and subsequent toxicity assessment refined the selection to a promising candidate, daucosterol. Molecular docking studies indicated that daucosterol exhibited significant binding affinity to all four viral proteins. Subsequent validation through molecular dynamics simulations confirmed the stability of daucosterol protein complexes. These results imply that daucosterol acts as a potential multitarget inhibitor against EBOV proteins and could serve as a promising lead compound for future therapeutic development against EVD.

Since Waddington proposed the concept of the “epigenetic landscape” in 1957, researchers have developed various methodologies to represent it in diverse processes. Studying the epigenetic landscape provides valuable qualitative information regarding cell development and the stability of phenotypic and morphogenetic patterns. Although Waddington’s original idea was a visual metaphor, a contemporary perspective relates it to the landscape formed by the basins of attraction of a dynamical system describing the temporal evolution of protein concentrations driven by a gene regulatory network. Transitions among these attractors can be driven by stochastic perturbations, with the cell state more likely to transition to the nearest attractor or to the one that presents the path of least resistance. In this study, we define the epigenetic landscape using the free energy potential obtained from the solution of the Fokker-Planck equation on the regulatory network. Specifically, we obtained a numerical approximate solution of the Fokker-Planck equation describing the Arabidopsis thaliana flower morphogenesis process. We observed good agreement between the coexpression matrix obtained from the Fokker-Planck equation and the experimental coexpression matrix. This paper proposes a method for obtaining this landscape by solving the Fokker-Planck equation (FPE) associated with a dynamical system describing the temporal evolution of protein concentrations involved in the process of interest. As these systems are high-dimensional and analytical solutions are often unfeasible, we propose a gamma mixture model to solve the FPE, transforming this problem into an optimization problem. This methodology can enhance the analysis of gene regulatory networks by directly relating theoretical mathematical models with experimental observations of coexpression matrices, thus providing a discriminating technique for competing models.

Eyring-Powell nanofluids have great potential for use in biomedical engineering to create more effective medical procedures and treatments due to their special properties of fluidity, efficient heat transfer, and interaction with biological systems. This study investigates bioconvection flow and its heat transfer characteristics of the magnetohydrodynamic ternary hybrid nanofluid containing silver, copper, and aluminum nanoparticles with human blood. The forced convection in a porous media, radiation, and viscous dissipation have been considered. The governing equations are reduced to dimensionless partial differential equations and further simplified using the local non-similarity method to obtain ordinary differential equations, which were solved numerically using the BVP4C algorithm. The results indicate that the concentration profiles reduce with inertia coefficients and Schmidt numbers, while radiation parameters increase the surface temperature. A higher Lewis number accelerates thermal diffusion, in contrast to mass diffusion. Fast dissipation of temperature prevents microbial growth and is useful in applications dealing with medicine administration and wound healing. These results support existing research and provide recommendations for further improvement of industrial and biological processes. As the (M) rises from (0.1) to (1,) the Nusselt number declines as follows: for (Ag) by around (4.48%-2.82%), for (Ag+Cu) by (13.57%, -7.58%) and for (Ag+Cu+Al) by (17.21%-12.53%). The Nusselt number increases around (1.01%-1.64%) as (lambda) rises from (0.1) to (0.3) for (Ag), for (Ag+Cu) by the Nusselt number increases by (6.77%-13.80%) and for (Ag+Cu+Al) by (13.84%-15.10%). The article proposes non-similar transformations for solving complex problems on the movement of ternary nanofluids. This provides insight into medical applications such as drug delivery and diagnostic tools and advances nanofluidic dynamics in healthcare.

Cytomegalovirus (CMV) is a significant clinical pathogen posing risks, especially for immunocompromised individuals. This study investigates the transport dynamics of CMV within viscoelastic saliva, focusing on factors influencing viral mobility. We employed mathematical models, including the Basset-Boussinesq-Oseen (BBO) equation, to analyze how viral density, particle diameter, saliva viscosity, and external electric and magnetic fields affect CMV movement in the oesophagus. Novel insights include the discovery that smaller CMV particles move significantly faster compared to larger ones, highlighting a critical aspect of viral infectivity. Additionally, we found that increased peristaltic wave amplitudes in the oesophagus greatly enhance viral velocity. More notably, our investigation reveals that the application of external magnetic fields can manipulate CMV transport by exerting forces that reduce viral mobility, thus potentially lowering infection rates through electromagnetic interactions. These findings underscore the complex interplay between fluid rheology, particle shape, and external fields in viral dynamics, suggesting novel therapeutic interventions aimed at controlling CMV spread based on saliva viscosity and electromagnetic manipulation. Our research paves the way for innovative strategies in viral infection management and therapeutic development.