Journal of Biological Physics最新文献

I present three proofs that idealized neurocomputational architectures capable of navigation guided by allocentric maps with landmark structure can be computationally universal. Navigation may occur online, in the environment, or offline, in the animal’s head. The first proof encodes the counters of a two-counter machine as positions of movable markers on orthogonal axes. The second directly simulates a one-tape Turing machine using a writable tape-path embedded in the map. The third realizes the tape-path’s predecessor/successor relation through local cues at a two-row landmark track rather than through a globally indexed path primitive. I establish universality in two senses: each class of navigation models is Turing-complete, and each contains a single machine that simulates every machine in its class. These constructions are mathematically close to classical graph-based models, including Kolmogorov–Uspensky machines, storage-modification machines, and graph Turing machines, so the bare computability results are unsurprising. My contribution is to reconstruct them explicitly within an allocentric cognitive-map architecture whose representational and computational primitives are drawn from empirical and theoretical work on spatial navigation and cognitive control. This shows that an allocentric navigation-based architecture can be computationally universal and opens the possibility of reconstructing aspects of biological cognition, including symbolic processing, in navigation-based terms.

Obesity, hyperglycemia, and aging are major risk factors for dementia-related disorders, particularly Alzheimer’s disease. We develop a time-fractional reaction–diffusion mathematical framework integrating nine interacting biological compartments governed by Michaelis–Menten kinetics and Hill functions to characterize the nonlinear interplay between metabolic dysfunction and neurodegeneration. Rigorous analysis establishes boundedness, stability, and the existence of a global attractor describing long-term system behavior. To enhance biological realism and predictive capability, we implement fractional physics-informed neural networks that embed fractional partial differential equations within deep learning architectures. This approach captures memory-dependent and spatiotemporal diffusion dynamics characteristic of neurological disorders while enabling estimation of time-varying parameters, fractional orders, and latent states. Global sensitivity analysis using partial-rank correlation coefficients identifies key drivers of tau pathology, insulin dysregulation, and dementia progression. Simulations reveal heterogeneous diffusion patterns linking obesity to accelerated neurodegenerative processes. An optimal control framework incorporating behavioral interventions and personalized anti-amyloid therapies is proposed to delay obesity-induced cognitive decline. The model highlights the critical roles of neuroinflammation and blood–brain barrier permeability in disease acceleration. The proposed FPI-NN framework achieves high predictive accuracy, with (mathcal {R}^{2}) values of 0.95, 0.93, 0.92, and 0.92 for (textrm{Y}_{1}), (textrm{W}_{beta }), (textrm{W}_{tau }), and (textrm{V}_{textrm{d}}), respectively. The corresponding RMSE values are 0.078, 0.094, 0.098, and 0.099, while the MAE values are 0.065, 0.079, 0.082, and 0.084, respectively. The MAPE values are (6.50%), (7.90%), (8.20%), and (8.40%), respectively. Cross-validation further confirms the stability and robustness of the proposed framework. This work provides a reproducible and interpretable computational platform for modeling fractional-order neurodegenerative dynamics and optimizing therapeutic strategies in obesity-associated dementia.

Hepatitis C virus (HCV) remains a major global health concern, causing chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC), with an estimated 185 million people affected worldwide. In the absence of an FDA-approved vaccine, the development of effective antiviral strategies remains essential. The HCV NS3-4A protease, which mediates cleavage of the viral polyprotein at specific downstream sites of NS3 and is essential for replication-complex formation, represents a key therapeutic target. In this study, approximately 1,480 phytocompounds were compiled from the IMPPAT database and previously reported in vitro studies and subjected to drug-likeness, toxicity, and biological activity screening using pkCSM, SwissADME, and PASS. Twenty compounds were subsequently prioritized and docked against WT, R155K, and D168Y NS3/4A protease structures. Among these, Ladanein (LAD) and Diosmetin (DMT) exhibited favourable binding across all protein variants. MD simulations indicated stable protein–ligand interactions, while MM/GBSA calculations revealed more favourable binding free energies for LAD than DMT across the WT and mutant complexes. Residue-wise energy decomposition identified more energetically favourable hotspot residues in LAD-bound complexes, whereas DMT exhibited higher overall hydrogen-bond occupancy. PCA and free-energy landscape analyses further demonstrated ligand-dependent alterations in collective conformational states induced by the R155K and D168Y substitutions. Overall, these findings computationally prioritize LAD as a promising candidate against WT and mutant HCV NS3/4A protease, warranting further in vitro and biochemical validation.

Graphical abstract

Dynamic light scattering was used to analyze nanoscale and mesoscopic protein-associated objects formed in lysozyme–NaCl solutions under crystallization conditions. The study focuses on the sequence of particle-size populations detected after salt addition and on their relation to the initial state of the protein solution. NaCl reproducibly induced a small population with hydrodynamic diameters of 5.6–6.5 nm, which is consistent with nanoscale oligomeric lysozyme assemblies and with previous SAXS/SANS evidence for dimers and octamers in crystallization solutions. Mesoscopic particles in the tens-to-hundreds of nanometers range and large submicron-to-micron aggregates appeared at selected stages of the experiments. The samples that produced crystals showed different mesoscopic-particle dynamics, whereas a sample with similar early DLS behavior did not yield crystals. These observations suggest that, in this limited series, the evolution of protein-associated nano- and mesoscopic objects was related to the initial particle-size distribution and the full temporal trajectory rather than to any single DLS peak. The DLS data are interpreted qualitatively, since intensity-weighted distributions do not provide direct number or mass fractions of the detected populations. The results characterize a multiscale and nonstationary self-organization process in a concentrated protein solution, involving a small nanoscale population consistent with oligomeric precursor clusters, mesoscopic aggregated states, and large sedimenting objects. This work may be useful for understanding soft biological nanoobjects and mesoscopic protein assemblies formed during salt-induced association and crystallization-related restructuring of concentrated protein solutions.

Plants account for the vast majority of the biomass on land. They first appear in the fossil record many millions of years ago. Some researchers have proposed that the obvious success of plant-life indicates that plants are “intelligent.” Plant intelligence has been strongly challenged, however, because plants do not possess brain-like tissues similar to electrical neurons, axons, and synapses. The following paper argues that a plant leaf is more properly understood as being a kind of analog computer called a “crossbar array.” The leaf’s computational facility arises from how leaf stomata respond to environmental water vapor, light, carbon dioxide, and heat. Though counterintuitive, a leaf’s success is enhanced by the phenomenon of “stomatal oscillations.”

In many plant cells, there are two types of mitochondrial motion: directed and wiggling. While the former is mediated by F-actin and microtubules, the latter is not. The fact that mitochondria migrate via wiggling suggests the existence of other mechanisms of motion aside from those related to the cytoskeleton and protein motors. In this work, it is assumed that wiggling mitochondria are active Brownian particles, self-propelled bodies whose motion at low Reynolds number is affected by noise. The proposed mechanism of motion is microswimming, where a wiggling mitochondrion is driven by a cycle of shape changes resembling a peristaltic wave travelling along its body. The peristaltic wave is modelled on a two-sphere swimmer under the far-field approximation, yielding expressions for the kinetic and dynamic variables involved, as well as for the factors determining its interaction with chloroplasts. The calculations show that the microswimmer can reach reported speeds with small size deformations and explain the observed high percentage of wiggling mitochondria captured by chloroplasts. Using the hydrodynamic results enables the application of a theoretical probabilistic model, including active and passive noise, which fits well with experimental results on speed distribution and trajectories of wiggling mitochondria. Taken together, the results explain the main mitochondrial wiggling characteristics observed in experiments, thus suggesting the feasibility of microswimming as a mechanism for mitochondrial wiggling.

Diabetes-associated cognitive decline (DACD) represents a complex disorder arising from the intersection of metabolic dysfunction, neuroinflammation, and neurodegenerative signaling. Despite increasing clinical recognition, the molecular network governing DACD remains poorly defined, and a systems-level network pharmacology approach has not previously been applied to this condition. Curcumin–indole-3-propionic acid conjugate (CUR-IPA), developed from two bioactive neuroprotective scaffolds, offers a rational multi-target framework to modulate DACD-associated pathways. DACD-related genes were compiled and analyzed using protein–protein interaction networks, hub gene identification, and pathway enrichment analysis. Drug-likeness and ADMET properties of CUR, IPA, and CUR-IPA were predicted in silico. Molecular docking was performed against prioritized targets, followed by 200-ns molecular dynamics simulations. Protein–ligand stability and dynamics were assessed using RMSD, RMSF, DCCM, elastic network model, and Markov state model analyses. Binding energetics were quantified using MM-PBSA calculations with residue-level decomposition. Network pharmacology identified PSEN1 and TGFβ1 as central hub proteins linking amyloid regulation, neuronal maintenance, immune signaling, and metabolic pathways in DACD. CUR-IPA satisfied multiple drug-likeness filters and exhibited favorable pharmacokinetic and toxicity profiles. Docking and MD simulations demonstrated stable binding of CUR-IPA within the active sites of PSEN1 and TGFβ1. ENM revealed restricted collective motions around ligand-binding regions, while MSM analysis identified energetically favorable and kinetically stable conformational states. MM-PBSA calculations confirmed strong binding affinities driven by key catalytic residues. This study establishes the first network pharmacology framework for DACD and demonstrates that CUR-IPA acts as a stable, multi-target modulator of key neurodegenerative and metabolic pathways.

The microsporidian parasites Nosema apis and Nosema ceranae cause nosemosis in honey bees, and spore surface charge is expected to influence adhesion and colloidal stability. However, zeta-potential (ζ) data for Nosema spores are absent from the literature. Here we report the first electrophoretic light-scattering measurements of ζ for N. ceranae spores under standardized conditions (10 mM KCl, pH 7.0, 25 °C). Spores were purified by differential centrifugation and analyzed in ten technical replicates. The mean ζ was –45.2 ± 1.8 mV (coefficient of variation 4.0%). The intensity-weighted distribution was unimodal with a peak at –42.5 mV, and autocorrelation functions indicated good data quality. The pH-dependence curve yielded a point of zero charge at pH 4.8, with a steep negative shift above pH 5, implying that surface carboxyl and phosphate groups dominate the charge. At the pH of the honey-bee midgut (6.0–6.5), ζ is estimated as –20 to –30 mV, which may permit moderate aggregation and influence infection efficiency. The high repeatability (σ = ± 1.8 mV) validates electrophoretic light scattering for future screening of anti-nosemal agents.