Physical biology最新文献

Allosteric communication in proteins relies on network connectivity patterns that channel conformational signals between distant sites. We introduce a unified mathematical framework based on three complementary measures of network organization derived from a single quantity. The first, the dynamic distanceRij, quantifies the mean-squared relative fluctuation between residue pairs. From this foundation, we derive two further metrics: the edge centrality, which identifies contacts critical for global connectivity by measuring their recurrence across all possible communication pathways, and the entropy sensitivity, which quantifies how perturbations to specific interactions alter system-wide flexibility. The mathematical structure shows that both topological centrality and thermodynamic sensitivity are linear functions of the dynamic distance. This derived unification demonstrates that residue pairs with high dynamic dissimilarity simultaneously function as flexible bottlenecks essential for allosteric communication. Applied to the oncoprotein KRAS, all three measures converge to identify the same residue pairs, corresponding to experimentally known allosteric sites. This convergence provides a unified graph-theoretical explanation for their functional importance. Analysis of the G12D and Q61H mutations and adagrasib binding shows how local perturbations rewire global communication pathways, highlighting specific residue pairs that gain or lose importance as network bottlenecks.

SLC6A16 (NTT5) is a poorly understood member of the solute carrier 6 (SLC6) family, a group of sodium-dependent transporters that shuttle amino acids and monoamines across cell membrane. While many SLC6 transporters have been well characterized, the substrate selectivity, and thereby the function of SLC6A16 remains unknown. Therefore, we employed computational modeling to predict the structures of human, bovine, and mouse variants of SLC6A16, which will guide future experimental studies on substrate selectivity. By comparing key features involved in transport and substrate recognition, we identified notable differences between SLC6A16 and other SLC6 family members, which typically share conserved elements. Moreover, our analyses suggest that human and bovine SLC6A16 might transport negatively charged amino acids such as glutamate and aspartate. Ultimately, our findings provide the first structural insights into SLC6A16 and offer testable hypotheses about its potential physiological role.

Biological systems in general operates out of equilibrium which demands the requirement for constant supply of energy due to non-equilibrium entropy production. The thermodynamic uncertainty relation (TUR) essentially imposes a bound on minimum current fluctuation the system can have given a entropy production rate. The fluctuation eventually impacts the signal to noise ratio imposing an upper bound on information transmission accuracy. In this study, we explored the role of TUR on the information transmission capacity of a set of cellular signaling systems using a coupled mathematical and machine learning approaches to experimental data in yeast under several stress conditions. Cell signaling systems are involved in sensing the changes in environment by activating a set of transcription factors (TF) which typically diffuse inside the nucleus to trigger transcription of the required genes. However, the inherent stochasticity of the biochemical pathways associated with signaling processes severely limits the accuracy of estimating the environmental input by the transcription factors. Application of TUR reveals a general picture about the working principle of the transcription factors. We found that the the activation followed by biased diffusion of transcription factors (TF) towards the nucleus triggers entropy production which amplifies magnitude of the overall TF currents towards the nucleus as well as reduces the fluctuations. These outcomes significantly improve the accuracy of information transmission carried out by the transcription factors following the bound imposed by TUR. Thus, experimental observations coupled with TUR based theoretical models demonstrate the role of thermodynamic fluctuation and entropy production on cellular information processing.

The propagation of noise through parallel pathways is a characteristic feature of feed-forward loops (FFLs) in genetic networks. Although the contributions of the direct and indirect pathways to output variability have been well characterized, the impact of their joint action arising from their shared input and output remains poorly understood. Here, we identify a cross-interaction noise emerging specifically from this pathway convergence. Using inter-gene correlations, we reveal the regulatory basis of the cross-interaction noise and interpret it as synergy or redundancy in noise propagation. Positive values of cross-interaction noise reflect synergy (noise amplification), while negative values reflect redundancy (noise suppression); a zero value indicates that the parallel pathways act independently. Synergy typically arises in coherent FFLs, whereas redundancy is common in incoherent ones. To quantify this effect, we introduce relative synergy noise, a dimensionless quantity, which captures the magnitude and sign of synergy and redundant noise relative to other noise sources. Further, by systematically tuning intrinsic noise strengths through effective gene expression burst, we find that when the intermediate node exhibits the highest intrinsic noise, it results in a relative synergy noise value approaching zero, indicating pathway independence. In contrast, when intrinsic noises follow a hierarchy in which the input is the most noisy, the intermediate is the least noisy, and the output is in between them, FFLs exhibit the strongest synergy in coherent motifs and the strongest redundancy in incoherent motifs. Furthermore, by relating these synergies and redundancies to dynamical properties such as sign-sensitive delay or response acceleration, the framework offers a statistical lens to interpret the functional roles in cellular decision-making. Our framework, thus, advances the mechanistic understanding of noise propagation in FFLs by quantifying pathway coupling as a measurable and biologically interpretable quantity.

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 characteristicsin 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.