Physical biology最新文献

Rust fungi cause significant economic and biodiversity losses worldwide, yet effective control strategies for them remain limited. A major challenge in identifying control targets is the inability to culture them through the different stages of their life cycle in the laboratory, thereby restricting their study. Current research suggests that a complex interplay of physical and chemical plant properties influences rust fungal infection, and successful culture protocols likely need to incorporate multiple aspects of the plant host environment into an artificial system. These include plant surface moisture, charge, hardness, hydrophobicity, topography, texture and chemical make-up. This review outlines key plant characteristics that influence infection by rust fungi, examines attempts to replicate these characteristics in vitro, and assesses the level of success. We conclude by proposing a potential culture approach that integrates inoculation methods, media composition, physical properties of media, chemical additives, and environmental conditions.

Biological membranes define cellular and organelle boundaries, and perform vital functions, providing transport, recognition, signaling, and interaction with other cells. These membranes are majorly composed of lipid bilayers and membrane proteins. Membrane proteins perform most membrane functions. Based on their localization, they are classified as integral and peripheral proteins. In this overview, we provide basic information about membrane proteins structure, conformational dynamics, and functions, and outline the methodologies used to produce highly-pure functional membrane proteins forin vitrobiophysical characterizations based on selected examples. To this end, expression of membrane proteins in a host, their extraction, purification and reconstitution in model lipid bilayers are described. Further, biophysical approaches play key role in elucidation of the structure and function of membrane proteins. Our focus here is on the technique of continuous wave electron paramagnetic/spin resonance (CW ESR) spectroscopy applied to spin-labeled membrane proteins. We describe the basic principles of membrane proteins labeling with nitroxide spin labels (paramagnetic tags) and how the CW ESR can be successfully used in elucidating the conformational dynamics of such proteins. We describe the basic principles of the CW ESR technique. The capability of this technique to characterize physiologically relevant conformational dynamics of proteins is demonstrated using two examples of CW ESR studies on spin-labeled human Tau and influenza A M2 proteins. The method is highly suitable to study physiological structure-function relationships of a broad range of proteins, and to explain the malfunctional states of proteins linked to diseases. This review is directed to the broader biophysical community with interest in molecular biophysics of biological membranes.

Metastatic melanoma presents a formidable challenge in oncology due to its high invasiveness and resistance to current treatments. Central to its ability to metastasize is the Notch signaling pathway, which, when activated through direct cell-cell interactions, propels cells into a metastatic state through mechanisms akin to the epithelial-mesenchymal transition (EMT). While the upregulation of miR-222 has been identified as a critical step in this metastatic progression, the mechanism through which this upregulation persists in the absence of active Notch signaling remains unclear. Here we introduce a dynamical system model that integrates miR-222 gene regulation with histone feedback mechanisms. Through computational analysis spanning both sustained and pulsatile ligand inputs, we delineate the non-linear decision boundaries that govern melanoma cell fate transitions, taking into account the dynamics of Notch signaling and the role of epigenetic modifications. Dimensional analysis reduces the 11-parameter system to three critical control groups governing chromatin modification rates and feedback strengths, providing a theoretical framework for parameter selection in the absence of complete kinetic measurements. Global sensitivity analysis identifies PRC2-mediated methylation and KDM5A-mediated demethylation as the dominant control parameters, while stochastic simulations show population heterogeneity consistent with the variable EMT responses observed in cancer cell populations. Our analysis examines the interplay between Notch signaling pathways and epigenetic regulation in dictating melanoma cell fate.

Despite decades of research, cancer remains one of the biggest health challenges. Due to the intricate interplay between multiple factors and different cancer types, it is still impossible to pinpoint a common cause for all forms of cancer. Computational modeling can be helpful in integrating scattered information to derive comprehensive information about malignancy. We describe a discrete dynamic network model of a mitogen-activated protein kinase pathway consisting of 66 nodes and 95 edges. The network consists of five input signals (Fas ligand, DNA damage, insulin, tumor necrosis factor alpha and transforming growth factor beta) and three output nodes (proliferation, apoptosis and growth arrest). Using a random asynchronous update method andin siliconode perturbations, the accuracy of the model is ensured. The results of simulations and perturbations were in agreement with the gene knockout and constitutive expression studies reported in the literature, underscoring the high precision of the deduced comprehensive network. The fidelity of our model makes it useful to understand the etiology of malignancy. Both anti-cancer and pro-cancer roles have been attributed to DUSP1 in different forms of cancers and, in our model, DUSP1 knockout under insulin and DNA damage signaling was found to universally enhance the proportion of cells undergoing apoptosis (i.e. a pro-cancerous role), thus highlighting its potential in designing novel therapeutic interventions. Moreover, although MYC is a well-known oncogene, we found that MYC's overexpression can activate p53, a prominent anti-growth agent, through the p14 and MDM2 pathways.Implications:Our findings suggest a novel role of the DUSP1 and MYC genes in regulating cell proliferation.

Weakly electric fish sense their environment in the dark using a self-generated electric field. Perturbations in the field caused by different objects are encoded by an array of sensors on their skin. The information content in these perturbations is not entirely clear. Previous work has focused on the so-called electric image (or field perturbation), which is the difference in the field at the skin surface, with and without the object present. Various features of the electric image have been shown to provide information about an object, including location. However, electric image based algorithms require information about the electric field under two qualitatively distinct conditions, and in many situations, prior information about the unperturbed field is not available. Here, we consider the more general problem of object localization with electric sensing when only instantaneous measures of the electric field are available. We show that this problem is solvable when field measurements for two slightly different object locations are considered (such as those occurring during relative motion). In doing so, we provide a direct link between sensory flow (i.e. the moment-to-moment fluctuations in raw sensory input) and electrosensory-based object localization.

The Gaussian network model (GNM) has been successful in explaining protein dynamics by modeling proteins as elastic networks of alpha carbons connected by harmonic springs. However, its uniform interaction assumption and neglect of higher-order correlations limit its accuracy in predicting experimental B-factors and residue cross-correlations critical for understanding allostery and information transfer. This study introduces an information-theoretic enhancement to the GNM, incorporating mutual information-based corrections to the Kirchhoff matrix to account for multi-body interactions and contextual residue dynamics. By iteratively optimizing B-factor predictions and applying a Monte Carlo-driven maximum entropy approach to refine covariances, our method achieves significant improvements, reducing RMSDs between predicted and experimental B-factors by 26%-46% across nine representative proteins. The model contextualizes residue assignments based on local density, solvent exposure, and allosteric roles, showing complex dynamic patterns beyond simple neighbor counts. Enhanced predictions of mutual information and entropy perturbations in proteins like KRAS improve the identification of spanning trees containing key residues, which may correspond to allosteric communication pathways. This evolvable framework, capable of incorporating additional effects and utilizing contextual residue assignments, enables precise studies of mutation effects on protein dynamics, with improved cross-correlation predictions potentially increasing accuracy in drug design and function prediction.

Bacteria utilize cell-cell signaling to coordinate gene expression in populations of cells. Bacterial signal exchange was originally interpreted as a mechanism bacteria use to regulate gene expression in response to changes in cell density, denoted as quorum sensing. Bacterial communication is now known to encompass the exchange of multiple chemical signals between different species of bacteria. Such signal crosstalk within communities of bacteria can have unexpected consequences. Some bacterial species even utilize more than one orthogonal signaling molecule, enabling such species to simultaneously communicate within distinct subsets of species. Such cells utilizing two sets of signals act as a bridge to link gene expression states within the community. Here, a mathematical model was implemented to investigate the consequences of multi-signal communication within heterogeneous bacterial communities. The model was inspired by simple neural networks, with nodes representing bacterial species and directed weights between nodes accounting for the impacts of inter-species signal exchange on gene expression. The activity state of such a network is defined as the gene expression state of each species within the community. Using the model, the stability of the activity states of such networks to changes in signal concentration and population size were quantified. Networks exchanging one set of signals were compared to network exchanging two orthogonal sets of signals. A multilayer neural network model was developed to analyze such networks exchanging orthogonal sets of signals. The model reveals that signal crosstalk increased the activity of the network. These networks were largely resilient to perturbation, however networks were more sensitive to perturbations of the largest population size. Bacterial species utilizing two orthogonal signals, within multilayer networks, had the potential to couple activity states of species that cannot directly communicate. These results give insight into strategies for manipulating signal exchange to predict and control gene expression within bacterial communities.

Phenotypic plasticity-the reversible switching of cell-states-is a central tenet of development, regeneration, and cancer progression. These transitions are governed by gene regulatory networks (GRNs), whose topological features strongly influence their dynamics. While toggle switches (mutually inhibitory feedback loops between two transcription factors) are a common motif observed for binary cell-fate decisions, GRNs across diverse contexts often exhibit a more general structure: two mutually inhibiting teams of nodes. Here, we investigate the teams of nodes as a potential topological design principle of GRNs. We first analyze GRNs from the Cell Collective database and introduce a metric, impurity, which quantifies the fraction of edges inconsistent with an idealized two-team architecture. Impurity correlates strongly with statistical properties of GRN phenotypic landscapes, highlighting its predictive value. To further probe this relationship, we simulate artificial two-team networks (TTNs) using both continuous (RACIPE) and discrete (Boolean) formalisms across varying impurity, density, and network size values. TTNs exhibit toggle-switch-like robustness under perturbations and enable accurate prediction of dynamical features such as inter-team correlations and steady-state entropy. Together, our findings establish the teams paradigm as a unifying principle linking GRN topology to dynamics, with broad implications for inferring coarse-grained network properties from high-throughput sequencing data.

Growth in bacterial populations generally depends on the environment (availability and quality of nutrients, presence of a toxic inhibitor, product inhibition..). Here, we build a minimal model to describe the action of a bacteriostatic antibiotic, assuming that this drug inhibits an essential autocatalytic cycle of the cell metabolism. The model recovers known growth laws, can describe various types of antibiotics and confirms the existence of two distinct regimes of growth-dependent susceptibility, previously identified only for ribosome targeting antibiotics. We introduce a proxy for cell risk, which proves useful to compare the effects of various types of antibiotics. We also develop extensions of our model to describe the effect of combining two antibiotics targeting two different autocatalytic cycles or a regime where cell growth is inhibited by a waste product.

Thevarmultigene family, comprising approximately 60 members, encodes for variants ofPlasmodium falciparumerythrocyte membrane protein or PfEMP1, a surface antigen which is crucial for parasite blood stage virulence.vargenes are expressed in a mutually exclusive fashion and to evade immune detection,P. falciparumtranscriptionally switches from one variant to another. It has been proposed that a biased hierarchical switching pattern optimizes the growth and survival ofP. falciparuminside the human host. However, the need to establish a particular hierarchy is not well explored, since the growth advantage to the parasite remains the same even if gene identities are shuffled. Our theoretical analysis based on a Markov chain model, coupled with single cell RNA-seq data analysis, RT-qPCR and RNA-seq measurements, establishes a hierarchicalvargene expression pattern underlying the biased switching pattern. Further, inclusion of host immune response in the model suggests that the observed switching hierarchy is beneficial when cells expressing different variants are cleared at variable rates by the immune response. For instance, PfEMP1 variants that are cleared more efficiently by the immune system are expressed stably and at a higher level in the population compared to variants that are cleared slowly by the immune system, with parasites quickly turning off the expression of the slowly cleared variant. Consistent with these findings, analysis of published experimental data showed that stable variants exhibit greater binding affinities to IgM. Taken together, our study provides a mechanistic basis for the hierarchical switching pattern ofP. falciparum vargenes observed during infection.