Physical biology最新文献

Wall shear stress (WSS) is a critical factor in vascular biology, and both high and low WSS are implicated in atherosclerosis. Fibronectin (FN) is a key extracellular matrix protein that plays an important role in cell activities. Under high shear stress, plasma FN undergoes fibrillogenesis; however, its behavior under low shear stress remains unclear. This study aimed to investigate the formation ofin vitrocell-free fibrillar FN (FFN) under low shear rate conditions and its effect on bovine aortic endothelial cell behavior. FN (500µg ml^-1) was perfused through slide chambers at three flow rates (0.16 ml h^-1, 0.25 ml h^-1, and 0.48 ml h^-1), corresponding to low shear rates of 0.35 s^-1, 0.55 s^-1, and 1.05 s^-1, respectively, for 4 h at room temperature. The formed FN matrices were observed using fluorescence microscopy and scanning electron microscopy. Under low shear rates, distinct FN matrix structures were observed. FFN0.48 formed immense fibrils with smooth surfaces, FFN0.25 formed a matrix with a rough surface, and FFN16 exhibited nodular structures. FFN0.25 supported cell activities to a greater extent than native FN and other FFN surfaces. Our study suggests that abnormally low shear conditions impact FN structure and function and enhance the understanding of FN fibrillogenesis in vascular biology, particularly in atherosclerosis.

Rheotaxis is a fundamental mechanism of sperm cells that guides them in navigating towards the oocyte. The present study investigates the phenomenon of sperm rheotaxis in Newtonian and non-Newtonian fluid media, which for the first time explores a viscosity range equivalent to that of the oviductal fluid of the female reproductive tract in rectilinear microfluidic channels. Three parameters, the progressive velocity while performing rheotaxis, the radius of rotation during rheotaxis, and the percentage of rheotactic sperm cells in the bulk and near-wall regions of the microfluidic channel were measured. Numerical simulations of the flow were conducted to estimate the shear rate, flow velocity, and the drag force acting on the sperm head at specific locations where the sperms undergo rheotaxis. Increasing the flow velocity resulted in a change in the position of rheotactic sperm from the bulk center to the near wall region, an increase and subsequent decrease in the sperm's upstream progressive velocity, and a decrease in the radius of rotation. We observed that with an increase in viscosity, rheotactic sperms migrate to the near wall regions at lower flow rates, the upstream progressive velocity of the sperm decreases for Newtonian and increases for non-Newtonian media, and the radius of rotation increases for Newtonian and decreases for non-Newtonian media. These results quantify the effects of fluid properties such as viscosity and flow rate on sperm rheotaxis and navigation, thereby paving the way for manipulating sperm behavior in microfluidic devices, potentially leading to advancements in assisted reproduction techniques.

Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented-with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeonHalobacterium salinarumand the bacteriumEscherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

Complexity in biology is often described using a multi-map hierarchical architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes driving evolution. Furthermore, natural selection, along with other neutral forces, can, in turn, modify these maps. At each level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and how they ultimately contribute to evolutionary dynamics.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

The synthesis of RNA thermometers is aimed at achieving temperature responses with desired thresholds and sensitivities. Although previous works have generated thermometers with a variety of thresholds and sensitivities as well as guidelines for design, possible constraints in the achievable thresholds and sensitivities remain unclear. We addressed this issue using a two-state model and its variants, as well as melt profiles generated from thermodynamic computations. In the two-state model, we found that the threshold was inversely proportional to the sensitivity, in the case of a fixed energy difference between the two states. Notably, this constraint could persist in variations of the two-state model with sequentially unfolding states and branched parallel pathways. Furthermore, the melt profiles generated from a library of thermometers exhibited a similar constraint. These results should inform the design of RNA thermometers as well as other responses that are mediated in a similar fashion.

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.

Understanding the structural and functional development of human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs) is essential to engineering cardiac tissue that enables pharmaceutical testing, modeling diseases, and designing therapies. Here we use a method not commonly applied to biological materials, small angle x-ray scattering, to characterize the structural development of hiPSC-CMs within three-dimensional engineered tissues during their preliminary stages of maturation. An x-ray scattering experimental method enables the reliable characterization of the cardiomyocyte myofilament spacing with maturation time. The myofilament lattice spacing monotonically decreases as the tissue matures from its initial post-seeding state over the span of 10 days. Visualization of the spacing at a grid of positions in the tissue provides an approach to characterizing the maturation and organization of cardiomyocyte myofilaments and has the potential to help elucidate mechanisms of pathophysiology, and disease progression, thereby stimulating new biological hypotheses in stem cell engineering.

Dopaminergic neurons are specialized cells in the substantia nigra, tasked with dopamine secretion. This secretion relies on intracellular calcium signaling coupled to neuronal electrical activity. These neurons are known to display spontaneous calcium oscillationsin-vitroandin-vivo, even in synaptic isolation, controlling the basal dopamine levels. Here we outline a kinetic model for the ion exchange across the neuronal plasma membrane. Crucially, we relax the assumption of constant, cytoplasmic sodium and potassium concentration. We show that sodium-potassium dynamics are strongly coupled to calcium dynamics and are essential for the robustness of spontaneous firing frequency. The model predicts several regimes of electrical activity, including tonic and 'burst' oscillations, and predicts the switch between those in response to perturbations. 'Bursting' correlates with increased calcium amplitudes, while maintaining constant average, allowing for a vast change in the calcium signal responsible for dopamine secretion. All the above traits provide the flexibility to create rich action potential dynamics that are crucial for cellular function.