Journal of Biological Physics最新文献

The transportation of the cargoes in biological cells is primarily driven by the motor proteins on filamentous protein tracks. The stochastic nature of the motion of motor protein often leads to its spontaneous detachment from the track. We formulate a mathematical model to study the effect of the detachment of motor protein on its track bound transport. We calculate two quantities: the distance traveled by the motor protein in given time, and the average time taken by a single motor protein to reach a target distance. Expectedly, both of these quantities decrease with the increasing detachment rate if the motor velocity is kept fixed. However, the existing experimental data suggest that a change in the detachment rate also affects the velocity of the motor protein. This relation between motor protein speed and its detachment rate results in a non-monotonic dependence on the distance traveled in fixed time and transport rate to a fixed distance. Therefore, we demonstrate that optimal motor speeds can be identified for the time and distance controlled conditions.

There is an increasing interest in the biological and therapeutic effects of fisetin, a natural phenolic compound. Fisetin has affinity on some neuronal targets and may have the potential to modulate neuronal activity. In this study the effects of acute application of fisetin on synchronized events were evaluated electro-physiologically. Besides, interaction of fisetin with closely related channels were investigated in silico. Acute horizontal hippocampal slices were obtained from 32- to 36-day-old C57BL/6 mice. Extracellular field potentials were recorded from CA3 region of the hippocampus. Bath application of 4 aminopyridine (4AP, 100 µM) initiated ictal- and interictal-like synchronized epileptiform discharges in the brain slices. Fifty micromolar fisetin was applied to the recording chamber during the epileptiform activity. The duration and frequencies of both ictal-like and interictal-like activities were calculated from the electrophysiological records. Molecular docking was performed to reveal interaction of fisetin on GABA-A, NMDA, AMPA receptors, and HCN2 channel, which are neuronal structures directly involved in recorded activity. Although fisetin does not affect basal neuronal activity in brain slice, it reduced the duration of ictal-like discharges significantly. Molecular docking results indicated that fisetin has no effect on GABA-A, NMDA, and AMPA receptors. However, fisetin binds to the (5JON) HCN2 channel strongly with the binding energy of −7.66 kcal/mol. Reduction on the duration of 4AP-induced ictal-like discharges can be explained as HCN channels can cause an inhibitory effect via enhancing M-type K + channels which increase K outward currents.

Biological neurons keep sensitive to external stimuli and appropriate firing modes can be triggered to give effective response to external chemical and physical signals. A piezoelectric neural circuit can perceive external voice and nonlinear vibration by generating equivalent piezoelectric voltage, which can generate an equivalent trans-membrane current for inducing a variety of firing modes in the neural activities. Biological neurons can receive external stimuli from more ion channels and synapse synchronously, but the further encoding and priority in mode selection are competitive. In particular, noisy disturbance and electromagnetic radiation make it more difficult in signals identification and mode selection in the firing patterns of neurons driven by multi-channel signals. In this paper, two different periodic signals accompanied by noise are used to excite the piezoelectric neural circuit, and the signal processing in the piezoelectric neuron driven by acoustic waves under noise is reproduced and explained. The physical energy of the piezoelectric neural circuit and Hamilton energy in the neuron driven by mixed signals are calculated to explain the biophysical mechanism of auditory neuron when external stimuli are applied. It is found that the neuron prefers to respond to the external stimulus with higher physical energy and the signal which can increase the Hamilton energy of the neuron. For example, stronger inputs used to inject higher energy and it is detected and responded more sensitively. The involvement of noise is helpful to detect the external signal under stochastic resonance, and the additive noise changes the excitability of neuron as the external stimulus. The results indicate that energy controls the firing patterns and mode selection in neurons, and it provides clues to control the neural activities by injecting appropriate energy into the neurons and network.

Photosensitive neurons can capture and convert external optical signals, and then realize the encoding signal. It is confirmed that a variety of firing modes could be induced under optical stimuli. As a result, it is interesting to explore the mode transitions of collective dynamics in the photosensitive neuron network under external stimuli. In this work, the collective dynamics of photosensitive neurons in a small-world network with non-synaptic coupling will be discussed with spatial diversity of noise and uniform noise applied on, respectively. The results prove that a variety of different collective electrical activities could be induced under different conditions. Under spatial diversity of noise applied on, a chimera state could be observed in the evolution, and steady cluster synchronization could be detected in the end; even the nodes in each cluster depend on the degree of each node. Under uniform noise applied on, the complete synchronization window could be observed alternately in the transient process, and steady complete synchronization could be detected finally. The potential mechanism is that continuous energy is pumped in the phototubes, and energy exchange and balance between neurons to form the resonance synchronization in the network with different noise applied on. Furthermore, it is confirmed that the evolution of collective dynamical behaviors in the network depends on the external stimuli on each node. Moreover, the bifurcation analysis for the single neuron model is calculated, and the results confirm that the electrical activities of single neuron are sensitive to different kinds of noise.

Modeling and analysis of biological growth curves are an age-old study area in which much effort has been dedicated to developing new growth equations. Recent efforts focus on identifying the correct model from a large number of equations. The relative growth rate (RGR), developed by Fisher (1921), has largely been used in the statistical inference of biological growth curve models. It is convenient to express growth equations using RGR, where RGR can be expressed as functions of size or time. Even though RGR is model invariant, it has limitations when it comes to identifying actual growth patterns. By proposing interval-specific rate parameters (ISRPs), Pal et al. (2018) appeared to solve this problem. The ISRP is based on the mathematical structure of the growth equations. Therefore, it is not model invariant. The current effort is to develop a measure of growth that is model invariant like RGR and shares the advantages of ISRP. We propose a new measure of growth, which we call instantaneous maturity rate (IMR). IMR is model invariant, which allows it to distinguish growth patterns more clearly than RGR. IMR is also scale-invariant and can take several forms including increasing, decreasing, constant, sigmoidal, bell-shaped, and bathtub. A wide range of possible IMR shapes makes it possible to identify different growth curves. The estimation procedure of IMR under a stochastic setup has been developed. Statistical properties of empirical IMR estimators have also been investigated in detail. In addition to extensive simulation studies, real data sets have been analyzed to prove the utility of IMR.

In the present investigation, the effect of multi-slip condition on peristaltic flow through asymmetric channel with Joule heating effect is considered. We also considered the incompressible non-Newtonian Casson nanofluid model for blood, which is electrically conducting. Second law of thermodynamics is used to examine the entropy generation. Multi-slip condition is used at the boundary of the wall and the analysis is also restricted under the low Reynolds number and long wavelength assumption. The governing equations were transformed into a non-dimensional form by using suitable terms. The reduced non-dimensional highly nonlinear partial differential equations are solved by using the Homotopy Perturbation Sumudu transformation method (HPSTM). The influence of different physical parameters on dimensionless velocity, pressure gradient, temperature, concentration and nanoparticle is graphically presented. From the results, one can understand that the Joule heating effect controls the heat transfer in the system and as the magnetic parameter is increased, there will be decay in the velocity of fluid. The outcomes of the present investigation can be applicable in examining the chyme motion in the gastrointestinal tract and controlling the blood flow during surgery. Present study shows an excellent agreement with the previously available studies in the limiting case. 

Altered unwinding/bending fluctuations at DNA lesion sites are implicated as plausible mechanisms for damage sensing by DNA-repair proteins. These dynamics are expected to occur on similar timescales as one-dimensional (1D) diffusion of proteins on DNA if effective in stalling these proteins as they scan DNA. We examined the flexibility and dynamics of DNA oligomers containing 3 base pair (bp) mismatched sites specifically recognized in vitro by nucleotide excision repair protein Rad4 (yeast ortholog of mammalian XPC). A previous Forster resonance energy transfer (FRET) study mapped DNA conformational distributions with cytosine analog FRET pair primarily sensitive to DNA twisting/unwinding deformations (Chakraborty et al. Nucleic Acids Res. 46: 1240–1255 (2018)). These studies revealed B-DNA conformations for nonspecific (matched) constructs but significant unwinding for mismatched constructs specifically recognized by Rad4, even in the absence of Rad4. The timescales of these unwinding fluctuations, however, remained elusive. Here, we labeled DNA with Atto550/Atto647N FRET dyes suitable for fluorescence correlation spectroscopy (FCS). With these probes, we detected higher FRET in specific, mismatched DNA compared with matched DNA, reaffirming unwinding/bending deformations in mismatched DNA. FCS unveiled the dynamics of these spontaneous deformations at ~ 300 µs with no fluctuations detected for matched DNA within the ~ 600 ns–10 ms FCS time window. These studies are the first to visualize anomalous unwinding/bending fluctuations in mismatched DNA on timescales that overlap with the < 500 µs “stepping” times of repair proteins on DNA. Such “flexible hinge” dynamics at lesion sites could arrest a diffusing protein to facilitate damage interrogation and recognition.

Pseudoknotted RNA molecules play important biological roles that depend on their folded structure. To understand the underlying principles that determine their thermodynamics and folding/unfolding mechanisms, we carried out a study on a variant of the mouse mammary tumor virus pseudoknotted RNA (VPK), a widely studied model system for RNA pseudoknots. Our method is based on a coarse-grained discrete-state model and the algorithm of PK3D (pseudoknot structure predictor in three-dimensional space), with RNA loops explicitly constructed and their conformational entropic effects incorporated. Our loop entropy calculations are validated by accurately capturing previously measured melting temperatures of RNA hairpins with varying loop lengths. For each of the hairpins that constitutes the VPK, we identified alternative conformations that are more stable than the hairpin structures at low temperatures and predicted their populations at different temperatures. Our predictions were validated by thermodynamic experiments on these hairpins. We further computed the heat capacity profiles of VPK, which are in excellent agreement with available experimental data. Notably, our model provides detailed information on the unfolding mechanisms of pseudoknotted RNA. Analysis of the distribution of base-pairing probability of VPK reveals a cooperative unfolding mechanism instead of a simple sequential unfolding of first one stem and then the other. Specifically, we find a simultaneous “loosening” of both stems as the temperature is raised, whereby both stems become partially melted and co-exist during the unfolding process.

Widespread genotyping of human populations in environmental homeostasis has created opportunities to quantify how environmental parameters affect the genomic distribution of variants in healthy populations. This represents an ongoing natural experiment upon the human species that can only be understood through developing models of adaptation. By examining the information dynamics of optimal SNP distributions within such populations, “adaptive forces” on genomic variants can be quantified through comparisons between different populations. To this end, we are performing double-blind scans of SNPs in order to ascertain any potential smooth functional relationships between the frequencies of the variants and changes in quantified environmental parameters. At present, we have sequentially examined more than twenty thousand SNPs (on chromosome 3) of nine homeostatic native populations for potential adaptive flagging of the variants as functions of 15 environmental parameters. Our first significant flag has related rs1010211 to viral pathogens in mammalian hosts. Such pathogens present a significant risk for the emergence of new infectious diseases in humans. This genomic variant is within the gene TNIK, which is a germinal center kinase (GCK). GCKs are participants in both adaptive and innate immune regulation. However, the function of TNIK is not fully understood. We quantify the adaptive force on the C allele due to the pathogens as 0.04 GEU’s/viral species.

Computational design of antimicrobial peptides (AMPs) is a promising area of research for developing novel agents against drug-resistant bacteria. AMPs are present naturally in many organisms, from bacteria to humans, a time-tested mechanism that makes them attractive as effective antibiotics. Depending on the environment, AMPs can exhibit α-helical or β-sheet conformations, a mix of both, or lack secondary structure; they can be linear or cyclic. Prediction of their structures is challenging but critical for rational design. Promising AMP leads can be developed using essentially two approaches: traditional modeling of the physicochemical mechanisms that determine peptide behavior in aqueous and membrane environments and knowledge-based, e.g., machine learning (ML) techniques, that exploit ever-growing AMP databases. Here, we explore the conformational landscapes of two recently ML-designed AMPs, characterize the dependence of these landscapes on the medium conditions, and identify features in peptide and membrane landscapes that mediate protein-membrane association. For both peptides, we observe greater conformational diversity in an aqueous solvent than in a less polar solvent, and one peptide is seen to alter its conformation more dramatically than the other upon the change of solvent. Our results support the view that structural rearrangement in response to environmental changes is central to the mechanism of membrane-structure disruption by linear peptides. We expect that the design of AMPs by ML will benefit from the incorporation of peptide conformational substates as quantified here with molecular simulations.