Journal of Biological Physics最新文献

Friction is ubiquitous but an essential force for insects during locomotion. Insects use dedicated bio-mechanical systems such as adhesive pads to modulate the intensity of friction, providing a stable grip with touching substrates for locomotion. However, how to uncover behavioral adaptation and regulatory neural circuits of friction modification is still largely understood. In this study, we devised a novel behavior paradigm to investigate adaptive behavioral alternation of Drosophila larvae under low-friction surfaces. We found a tail looseness phenotype similar to slipping behavior in humans, as a primary indicator to assess the degree of slipping. We found a gradual reduction on slipping level in wild-type larvae after successive larval crawling, coupled with incremental tail contraction, displacement, and speed acceleration. Meanwhile, we also found a strong correlation between tail looseness index and length of contraction, suggesting that lengthening tail contraction may contribute to enlarging the contact area with the tube. Moreover, we found a delayed adaptation in rut mutant larvae, inferring that neural plasticity may participate in slipping adaptation. In conclusion, our paradigm can be easily and reliably replicated, providing a feasible pathway to uncover the behavioral principle and neural mechanism of acclimation of Drosophila larvae to low-friction conditions.

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body’s immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system’s dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system’s bistable region, which can affect the system’s normal functioning.

Myricetin (MYR) is a bioactive secondary metabolite found in plants that is recognized for its nutraceutical value and is an essential constituent of various foods and beverages. It is reported to exhibit a plethora of activities, including antioxidant, antimicrobial, antidiabetic, anticancer, and anti-inflammatory. Alpha-2-macroglobulin (α2M) is a major plasma anti-proteinase that can inhibit proteinases of both human and non-human origin, regardless of their specificity and catalytic mechanism. Here, we explored the interaction of MYR-α2M using various biochemical and biophysical techniques. It was found that the interaction of MYR brings subtle change in its anti-proteolytic potential and thereby alters its structure and function, as can be seen from absorbance and fluorescence spectroscopy. UV spectroscopy of α2M in presence of MYR indicated the occurrence of hyperchromism, suggesting complex formation. Fluorescence spectroscopy reveals that MYR reduces the fluorescence intensity of native α2M with a shift in the wavelength maxima. At 318.15 K, MYR binds to α2M with a binding constant of 2.4 × 10³ M⁻¹, which indicates significant binding. The ΔG value was found to be − 7.56 kcal mol⁻¹ at 298.15 K, suggesting the interaction to be spontaneous and thermodynamically favorable. The secondary structure of α2M does not involve any major change as was confirmed by CD analysis. The molecular docking indicates that Asp-146, Ser-172, Glu-174, and Tyr-180 were the key residues involved in α2M-MYR complex formation. This study contributes to our understanding of the function and mechanism of protein and flavonoid binding by providing a molecular basis of the interaction between MYR and α2M.

Kinesins constitute a superfamily of microtubule (MT)-based motor proteins, which can perform diverse biological functions in cells such as transporting vesicle, regulating MT dynamics, and segregating chromosome. Some motors such as kinesin-1, kinesin-2, and kinesin-3 do the activity mainly on the MT lattice, while others such as kinesin-7 and kinesin-8 do the activity mainly at the MT plus end. To perform the different functions, it is required that the former motors can reside on the MT lattice for longer times than at the end, while the latter motors can reside at the MT plus end for long times. Here, a simple but general theory of the MT-end residence time of the kinesin motor is presented, with which the factors dictating the residence time are determined. The theory is further used to study specifically the MT-end residence times of Drosophila kinesin-1, kinesin-2/KIF3AB, kinesin-3/Unc104, kinesin-5/Eg5, kinesin-7/CENP-E, and kinesin-8/Kip3 motors, with the theoretical results being in agreement with the available experimental data.

From a physical viewpoint, any external stimuli including noise disturbance can inject energy into the media, and the electric response is regulated by the equivalent electric stimulus. For example, mode transition in electric activities in neurons occurs and kinds of spatial patterns are formed during the wave propagation. In this paper, a feasible criterion is suggested to explain and control the growth of electric synapse and memristive synapse between Hindmarsh-Rose neurons in the presence of noise. It is claimed that synaptic coupling can be enhanced adaptively due to energy diversity, and the coupling intensity is increased to a saturation value until two neurons reach certain energy balance. Two identical neurons can reach perfect synchronization when electric synapse coupling is further increased. This scheme is also considered in a chain neural network and uniform noise is applied on all neurons. However, reaching synchronization becomes difficult for neurons in presenting spiking, bursting, and chaotic and periodic patterns, even when the local energy balance is corrupted to continue further growth of the coupling intensity. In the presence of noise, energy diversity becomes uncertain because of spatial diversity in excitability, and development of regular patterns is blocked. The similar scheme is used to control the growth of memristive synapse for neurons, and the synchronization stability and pattern formation are controlled by the energy diversity among neurons effectively. These results provide possible guidance for knowing the biophysical mechanism for synapse growth and energy flow can be applied to control the synchronous patterns between neurons.

Cell fate decision processes are regulated by networks which contain different molecules and interactions. Different network topologies may exhibit synergistic or antagonistic effects on cellular functions. Here, we analyze six most common small networks with regulatory logic AND or OR, trying to clarify the relationship between network topologies and synergism (or antagonism) related to cell fate decisions. We systematically examine the contribution of both network topologies and regulatory logic to the cell fate synergism by bifurcation and combinatorial perturbation analysis. Initially, under a single set of parameters, the synergism of three types of networks with AND and OR logic is compared. Furthermore, to consider whether these results depend on the choices of parameter values, statistics on the synergism of five hundred parameter sets is performed. It is shown that the results are not sensitive to parameter variations, indicating that the synergy or antagonism mainly depends on the network topologies rather than the choices of parameter values. The results indicate that the topology with “Dual Inhibition” shows good synergism, while the topology with “Dual Promotion” or “Hybrid” shows antagonism. The results presented here may help us to design synergistic networks based on network structure and regulation combinations, which has promising implications for cell fate decisions and drug combinations.

Fractional calculus is very convenient tool in modeling of an emergent infectious disease system comprising previous disease states, memory of disease patterns, profile of genetic variation etc. Significant complex behaviors of a disease system could be calibrated in a proficient manner through fractional order derivatives making the disease system more realistic than integer order model. In this study, a fractional order differential equation model is developed in micro level to gain perceptions regarding the effects of host immunological memory in dynamics of SARS-CoV-2 infection. Additionally, the possible optimal control of the infection with the help of an antiviral drug, viz. 2-DG, has been exemplified here. The fractional order optimal control would enable to employ the proper administration of the drug minimizing its systematic cost which will assist the health policy makers in generating better therapeutic measures against SARS-CoV-2 infection. Numerical simulations have advantages to visualize the dynamical effects of the immunological memory and optimal control inputs in the epidemic system.

Superparamagnetic iron oxide nanoparticles (SPIONPs) are widely used in clinical research. The single domain nanoparticles are used in magnetic fluid hyperthermia (MFH) to treat cancer. When nanoparticles are exposed to an external magnetic field, it generates heat destroying tumour cells. SPIONPs have a large surface area, so the particles tend to aggregate, which leads to the destabilization of the colloidal system. To enhance the stability and biocompatibility of the nanomaterials, it is necessary to coat the surface with biocompatible material. Magnetite (Fe₃O₄) is a superparamagnetic nanoparticle (SPNPs) that was functionalized with oleic acid (OA) by sol–gel process using ethanol as the solvent. The oleic acid-coated magnetite (OA-Fe₃O₄) was characterized by X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), UV–Visible diffuse reflectance spectroscopy (UV-DRS) and vibrating sample magnetometer (VSM). The haemolysis test has been used to investigate the haemocompatibility properties of nanomaterials. Hyperthermia study shows a high SAR value for the concentration of 1 mg/ml at the field of 600 Oe and frequency of 316 kHz. The OA coating enhanced the haemocompatibility of synthesized magnetite nanoparticles which can be used for magnetic fluid hyperthermia applications.

The hydrophobic force is one of the most dominant factors in protein folding. A protein becomes functional only when it achieves its three-dimensional structure and stability upon folding. For a better understanding of the hydrophobic effects and their function in protein folding, quantitative measurement of the hydrophobicity of amino acid side chains is crucial. Spike protein is the primary structural protein in SARS-CoV-2 and SARS-CoV. This study explores how protein sequences in SARS-CoV-2 and SARS-CoV spike proteins encode hydrophobic interactions. Computational tools/techniques have been utilized to investigate the protein sequences of the spike proteins of SARS-CoV-2 and SARS-CoV. Investigations provided an estimate of hydrophobic distribution and its relative strength, indicating a hydrophobic pattern. Analysis of the spike protein's hydrophobic profile may help identify and treat the virus-caused disease; additionally, it can give an insight into the transmissibility and pathogenicity of the virus.

Experiments using conventional experimental approaches to capture the dynamics of ion channels are not always feasible, and even when possible and feasible, some can be time-consuming. In this work, the ionic current–time dynamics during cardiac action potentials (APs) are predicted from a single AP waveform by means of artificial neural networks (ANNs). The data collection is accomplished by the use of a single-cell model to run electrophysiological simulations in order to identify ionic currents based on fluctuations in ion channel conductance. The relevant ionic currents, as well as the corresponding cardiac AP, are then calculated and fed into the ANN algorithm, which predicts the desired currents solely based on the AP curve. The validity of the proposed methodology for the Bayesian approach is demonstrated by the R (validation) scores obtained from training data, test data, and the entire data set. The Bayesian regularization’s (BR) strength and dependability are further supported by error values and the regression presentations, all of which are positive indicators. As a result of the high convergence between the simulated currents and the currents generated by including the efficacy of a developed Bayesian solver, it is possible to generate behavior of ionic currents during time for the desired AP waveform for any electrical excitable cell.