Journal of Biological Physics最新文献

Abstract

Changes in the osmolality of the extracellular medium (ECM) affect cell volume and cellular processes such as cell migration and proliferation. Not only may high concentrations of zinc (Zn) lead to cell death by apoptosis, but Zn is also a physiological suppressor of apoptosis. The aim of our study was to examine whether Zn and regulation of extracellular osmolality had an effect on the lung cancer cell line (A549) and how to be changed in ECM according to elements and osmolality depending on incubation time and Zn application. Our study consisted of four groups: cell-free medium, ECM of cancer cell after 24 h incubation (24hECM), ECM of cancer cell after 48 h incubation (48hECM), and ECM of cancer cell after 48 h incubation with ZnCl₂ (48hECM + Zn). ECM osmolality was measured by using osmometer, and the levels of chromium (Cr), iron (Fe), and magnesium (Mg) elements were analyzed using ICP-OES device for all groups. According to the result of the analysis, a statistically significant difference was found when osmolality and element values of ECM of 24hECM and 48hECM groups were compared with the values of the 48hECM + Zn group. It was observed that there was a decrease in the levels of Cr, Fe, and Mg with Zn application and incubation period in ECM. The regulation of ECM osmolality is a promising method due to biophysical effects on cancer cells. In our study, we speculated that the understanding of the effects of Zn and osmolality with the relationship between ECM and cancer cell might lead to the discovery of biophysical approaches as a novel therapeutic strategy.

Context-dependent computation is a relevant characteristic of neural systems, endowing them with the capacity of adaptively modifying behavioral responses and flexibly discriminating between relevant and irrelevant information in a stimulus. This ability is particularly highlighted in solving conflicting tasks. A long-standing problem in computational neuroscience, flexible routing of information, is also closely linked with the ability to perform context-dependent associations. Here we present an extension of a context-dependent associative memory model to achieve context-dependent decision-making in the presence of conflicting and noisy multi-attribute stimuli. In these models, the input vectors are multiplied by context vectors via the Kronecker tensor product. To outfit the model with a noisy dynamic, we embedded the context-dependent associative memory in a leaky competing accumulator model, and, finally, we proved the power of the model in the reproduction of a behavioral experiment with monkeys in a context-dependent conflicting decision-making task. At the end, we discuss the neural feasibility of the tensor product and made the suggestive observation that the capacities of tensor context models are surprisingly in alignment with the more recent experimental findings about functional flexibility at different levels of brain organization.

The quality and strength of drug and albumin interaction affecting the drug-free concentration and physiological activity are important issues in pharmacokinetic research. In the present study, not only did we evaluate the binding strength of ceftriaxone and ceftizoxime to bovine serum albumin (BSA), but we also investigated the kinetic and thermodynamic parameters including KD, KA, ΔS, and ΔH. We applied in vitro optical fluorescence spectroscopy and surface plasmon resonance (SPR) sensing approaches as well as molecular docking analyses. The kinetic and thermodynamic investigations were done using different concentrations of drugs at three temperatures. Thermodynamic parameters visibly demonstrated that the binding was an exothermic and spontaneous process. The obtained negative values of both enthalpy change (ΔH) and entropy change (ΔS) in fluorescence and SPR and also molecular docking investigations showed that the major binding force involved in the complexation of drugs to BSA was hydrogen bonding. Static quenching was the foremost fluorescence quenching mechanism between them. Furthermore, the results of ΔG and K_D values proved that the interaction of ceftriaxone-BSA was stronger than ceftizoxime-BSA. Finally, molecular docking confirmed that the preferable binding sites of ceftizoxime and ceftriaxone were site IIA and site IB of albumin, respectively.

Graphic abstract

The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.

The nerve cells are responsible for transmitting messages through the action potential, which generates electrical stimulation. One of the methods and tools of electrical stimulation is infrared neural stimulation (INS). Since the mechanism of INS is based on electromagnetic radiation, it explains how a neuron is stimulated by the heat distribution which is generated by the laser. The present study is focused on modeling and simulating the conditions in which deformed temperature related to the Hodgkin and Huxley model can be effectively and safely used to activate the neurons, the fires of which depend on temperature. The results explain ionic channels in the single and network neurons, which behave differently when thermal stimulation is applied to the cell. It causes the variation of the pattern of the action potential in the Hodgkin-Huxley (HH) model. The stability of the phase-plane at high temperatures has lower fluctuations than at low temperatures, so the channel gates open and close faster. The behavior of these channels under various membrane temperatures shows that the firing rate increases with temperature. Also, the domain of the spikes reduces and the spikes occur faster with increasing temperature.

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein–ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

Proteins are involved in numerous cellular activities such as transport and catalysis. Misfolding during biosynthesis and malfunctioning as a molecular machine may lead to physiological disorders and metabolic problems. Protein folding and mechanical work may be viewed as thermodynamic energetically favorable processes in which stochastic nonequilibrium intermediate states may be present with conditions such as thermal fluctuations. In my opinion, measuring those thermal fluctuations may be a way to access the energy exchange between the protein and the physiological environment and to better understand how those nonequilibrium states may influence the misfolding/folding process and the efficiency of the molecular engine cycle. Here, I discuss luminescence thermometry as a possible way to measure those temperature fluctuations from a single-molecule experimental perspective with its current technical limitations and challenges.

Identifying gene regulatory networks (GRN) from observation data is significant to understand biological systems. Conventional studies focus on improving the performance of identification algorithms. However, besides algorithm performance, the GRN identification is strongly depended on the observation data. In this work, for three GRN S-system models, three observation data collection schemes are used to perform the identifiability test procedure. A modified genetic algorithm-particle swarm optimization algorithm is proposed to implement this task, including the multi-level mutation operation and velocity limitation strategy. The results show that, in scheme 1 (starting from a special initial condition), the GRN systems are of identifiability using the sufficient transient observation data. In scheme 2, the observation data are short of sufficient system dynamic. The GRN systems are not of identifiability even though the state trajectories can be reproduced. As a special case of scheme 2, i.e., the steady-state observation data, the equilibrium point analysis is given to explain why it is infeasible for GRN identification. In schemes 1 and 2, the observation data are obtained from zero-input GRN systems, which will evolve to the steady state at last. The sufficient transient observation data in scheme 1 can be obtained by changing the experimental conditions. Additionally, the valid observation data can be also obtained by means of adding impulse excitation signal into GRN systems (scheme 3). Consequently, the GRN systems are identifiable using scheme 3. Owing to its universality and simplicity, these results provide a guide for biologists to collect valid observation data for identifying GRNs and to further understand GRN dynamics.