Physical biology最新文献

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.

This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using standardized preprocessing techniques, and fractal measures were computed to capture complexity and connectivity properties within breast tissue structures. We introduced the succolarity reservoir as a novel parameter accounting for tissues' latent connectivity. Fractal dimension, multifractality strength, and succolarity reservoir were found to effectively characterize specific features of mammographic texture in contrast to lacunarity and Rényi dimensions; however, their incorporation into machine learning models yielded moderate discriminatory performance between categories. In addition, while succolarity reservoir exhibits conceptual potential for differentiating Luminal B from other molecular subtypes, its overall discriminative power remains limited. This proof-of-concept study underscores the exploratory potential of fractal-based texture analysis as a non-invasive biomarker in breast carcinoma diagnosis.

The ryanodine receptor isoform-1 (RyR1) is a large intracellular calcium release channel essential for skeletal muscle contraction. While cryo-electron microscopy has revealed structural snapshots of RyR1 in closed and open states, the dynamic features associated with calcium-dependent gating remain incompletely understood. In this study, we integrated all-atom molecular dynamics (MD) simulations with domain-level bioinformatics analyses to characterize and compare the structural dynamics of RyR1 in its closed and open conformations. Our simulations revealed distinct structural differences, including domain flexibility patterns, solvent accessibility, and hydrogen bonding networks, between the closed and open states. The closed state exhibited more extensive inter-subunit contacts and stable hydrogen-bonding networks, supporting a compact architecture characterized by inter-subunit domain engagement and intra-subunit domain loosening. In contrast, the open state showed increased solvent exposure and reduced inter-subunit interactions, reflecting inter-subunit domain loosening coupled with intra-subunit domain engagement, particularly in regions connecting the cytoplasmic and pore-forming domains. The comparative approach provides structural perspectives on how calcium binding may contribute to RyR1's conformational organization relevant to gating function. Our findings highlight the utility of integrating MD simulations with domain-scale analyses to investigate large protein complexes and generate hypotheses for future experimental validation.

The engagement of protein and ribonucleic acid (RNA)/deoxyribonucleic acid (DNA) is examined in three varied conformations of protein molecules and two different configurations of RNA/DNA, namely finite and cyclic. This analysis emphasizes density of states (DOS) and band structures by making use of a tight-binding Hamiltonian in combination with Green's function techniques. At a steady temperature and a defined quantity of building blocks in the RNA and DNA strands, the spectral diagrams show flat energy curves for both RNA and DNA molecules, showcasing characteristics akin to those found in semiconductors. The key distinctions between the cyclic configuration and the finite case lie in the peak height and the arrangement of the peaks in the DOS, as well as the shifts in band positions. The coupling of protein molecules with the RNA and DNA models yields a reduction of the energy gap in the protein-RNA system and a progression from semiconductor properties to metallic ones in the protein-DNA structure. Furthermore, the role of temperature in determining the DOS leads to changes in the peak levels and their respective positions. It is expected that the coupling of protein and RNA/DNA will directly exert a straightforward influence on the electronic attributes of RNA/DNA, which differ among diverse protein structures, thus creating opportunities for newly conducted research with significant biological implications.

Metastatic glioblastoma multiforme (GBM) is known for its dismal prognosis due to the dissemination of single cells throughout the brain parenchyma and along white matter tracts, resulting in heightened resistance to therapies. Understanding the intricate relationship between cell migration, physical confinement, and chemotherapeutic resistance in GBM is imperative for advancing treatment strategies. In this study, we employed G55, a representative migratory GBM cell line, to investigate this phenomenon. We generated three distinct cell populations: (1) cells migrating without confinement, assessed via the Scratch assay; (2) cells migrating a short distance (10μm) under confinement, examined through the Transwell assay; and (3) cells migrating long distances (>100μm) under confinement, studied using the Microchannel assay. Comparative analyses of protein expression profiles and chemotherapy sensitivity among these groups revealed that migration combined with physical confinement plays a pivotal role in augmenting chemotherapeutic resistance in interstitial invasive cancer cells. Moreover, we demonstrate the utility of the microchannel device, which facilitates controlled cell migration under physical confinement, as an effectivein vitrotool for investigating metastatic cancer and associated treatment resistance. This study sheds light on the mechanisms underlying GBM progression and highlights potential avenues for therapeutic intervention.

Gene regulatory networks with negative feedback play a crucial role in conferring robustness and evolutionary resilience to biological systems. However, the discrete nature of molecular components and probabilistic interactions in these networks are inherently subject to fluctuations, which pose challenges for stability analysis. Traditional analysis methods for stochastic systems, like the Langevin equation and the Fokker-Planck equation, are widely used. However, these methods primarily provide approximations of system behavior and may not be suitable for systems that exhibit non-mass-action kinetics, such as those described by Hill functions. In this study, we employed a second-moment approach to analyze the stability of a gene regulatory network with negative feedback under intrinsic fluctuations. By transforming the stochastic system into a set of ordinary differential equations for the mean concentration and second central moment, we performed a stability analysis similar to that used in deterministic models, where there are no fluctuations. Our results show that the incorporation of the second central moment introduces two additional negative eigenvalues, indicating that the system remains stable under intrinsic fluctuations. Furthermore, the stability of the second central moment suggests that the fluctuations do not induce instability in the system. The stationary values of the mean concentrations were found to be the same as those in the deterministic case, indicating that fluctuations did not influence stationary mean concentrations. This framework provides a practical and insightful method for analyzing the stability of stochastic systems and can be extended to other biochemical networks with regulatory feedback and intrinsic fluctuations through a framework of ordinary differential equations.