Physical biology最新文献

Recent experimental studies have shown that physical exercise has the potential to suppress tumor progression. Such suppression has been reported to be mediated by the exercise-induced activation of natural killer (NK) cells through the release of IL-6, a cytokine. Aimed at shedding light on how exercise-induced NK cell activation helps in the suppression of cancer, we developed a coarse-grained mathematical model based on a system of ordinary differential equations describing the interaction between IL-6, NK-cells, and tumor cells. The model is then used to study how exercise duration and exercise intensity affect tumor suppression. Our results show that increasing exercise intensity or increasing exercise duration leads to greater and sustained tumor suppression. Furthermore, multi-bout exercise patterns hold promise for improving cancer treatment strategies by adjusting exercise intensity and frequency. Thus, the proposed mathematical model provides insights into the role of exercise in tumor suppression and can be instrumental in guiding future experimental studies, potentially leading to more effective exercise interventions.

We consider a Markovian model for the kinetics of RNA Polymerase (RNAP) which provides a physical explanation for the phenomenon of cooperative pushing during transcription elongation observed in biochemical experiments onEscherichia coliand yeast RNAP. To study how backtracking of RNAP affects cooperative pushing we incorporate into this model backward (upstream) RNAP moves. With a rigorous mathematical treatment of the model we derive conditions on the mutual static and kinetic interactions between RNAP under which backtracking preserves cooperative pushing. This is achieved by exact computation of several key properties in the steady state of this model, including the distribution of headway between two RNAP along the DNA template and the average RNAP velocity and flux.

Finding the stiffness map of biological tissues is of great importance in evaluating their healthy or pathological conditions. However, due to the heterogeneity and anisotropy of biological fibrous tissues, this task presents challenges and significant uncertainty when characterized only by single-mode loading experiments. In this study, we propose a new theoretical framework to map the stiffness landscape of fibrous tissues, specifically focusing on brain white matter tissue. Initially, a finite element (FE) model of the fibrous tissue was subjected to six loading cases, and their corresponding stress-strain curves were characterized. By employing multiobjective optimization, the material constants of an equivalent anisotropic material model were inversely extracted to best fit all six loading modes simultaneously. Subsequently, large-scale FE simulations were conducted, incorporating various fiber volume fractions and orientations, to train a convolutional neural network capable of predicting the equivalent anisotropic material properties solely based on the fibrous architecture of any given tissue. The proposed method, leveraging brain fiber tractography, was applied to a localized volume of white matter, demonstrating its effectiveness in precisely mapping the anisotropic behavior of fibrous tissue. In the long-term, the proposed method may find applications in traumatic brain injury, brain folding studies, and neurodegenerative diseases, where accurately capturing the material behavior of the tissue is crucial for simulations and experiments.

Fibrin fibers are important structural elements in blood coagulation. They form a mesh network that acts as a scaffold and imparts mechanical strength to the clot. A review of published work measuring the mechanics of fibrin fibers reveals a range of values for fiber extensibility. This study investigates fibrinogen concentration as a variable responsible for variability in fibrin mechanics. It expands previous work to describe the modulus, strain hardening, extensibility, and the force required for fiber failure when fibers are formed with different fibrinogen concentrations using lateral force atomic force microscopy. Analysis of the mechanical properties showed fibers formed from 1 mg ml^-1and 2 mg ml^-1fibrinogen had significantly different mechanical properties. To help clarify our findings we developed two behavior profiles to describe individual fiber mechanics. The first describes a fiber with low initial modulus and high extensible, that undergoes significant strain hardening, and has moderate strength. Most fibers formed with 1 mg ml^-1fibrinogen had this behavior profile. The second profile describes a fiber with a high initial modulus, minimal strain hardening, high strength, and low extensibility. Most fibrin fibers formed with 2 mg ml^-1fibrinogen were described by this second profile. In conclusion, we see a range of behaviors from fibers formed from native fibrinogen molecules but various fibrinogen concentrations. Potential differences in fiber formation are investigated with SEM. It is likely this range of behaviors also occursin vivo. Understanding the variability in mechanical properties could contribute to a deeper understanding of pathophysiology of coagulative disorders.

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.