Recent studies have suggested that under high or near-maximal mitochondrial respiratory activity, ion-translocating proteins within the inner mitochondrial membrane may generate transient nonequilibrium temperature fluctuations in the adjacent mitochondrial matrix and intermembrane space. Such nonequilibrium temperature fluctuations may, in principle, influence mitochondrial mechanics and morphology. Building on elastocapillary models of mitochondrial dynamics, we investigate whether these nonequilibrium temperature fluctuations can modulate the stability of mitochondrial tubules through temperature-dependent changes in effective membrane tension and elasticity. Our numerical analysis predicts that this effect is strongly threshold-dependent: in deeply unstable states, thermal modulation remains insufficient to restore stability, whereas closer to the threshold, temperature-dependent reduction of effective membrane tension can overcome temperature-dependent elastic softening, thereby increasing the elastocapillary number, suppressing unstable modes, and shifting mitochondria toward mechanically more stable tubular states. In other words, when mitochondria begin shifting toward fission-promoting states, elevated respiratory activity, which increases the magnitude and cumulative temporal occupancy of transient thermal perturbations, tends to shift the system back toward mechanical stability. However, when mitochondria are already far within the mechanically unstable regime, transient thermal activity is no longer sufficient to restore stability. This stabilizing regime is qualitatively consistent with experimental observations linking elevated oxidative phosphorylation to mitochondrial elongation, fusion, or hyperfusion rather than fragmentation.
The retina is both metabolically active and exposed to light, resulting in persistent heat generation. However, the quantitative contributions of metabolic and irradiative heat sources, and the mechanisms responsible for dissipating this thermal load, remain incompletely characterized. Here we develop a steady-state quantitative thermal balance model of the retina that integrates heat production from metabolic activity and light absorption with multiple heat dissipation pathways into a unified energy balance framework. Metabolic heat was estimated from reported ATP consumption rates under dark-and light-adapted conditions, while irradiative heat input was quantified based on environmental luminance, pupil size, and ocular optics. These inputs were incorporated into a lumped energy balance in the form of, allowing calculation of the retinal temperature elevation relative to blood,. Heat dissipation pathways, including conduction to surrounding tissue, choroidal blood perfusion, convection, and radiation, were expressed as thermal conductances,and analyzed within the same framework,. Under sunlight, total retinal heat input reached approximately 10 mW, approximately threefold higher than at night. Despite this, the predicted steady-state retinal temperature elevation remained very small (∼10-3K), suggesting that heat is dissipated highly efficiently. The analysis shows that conduction and choroidal blood perfusion dominate retinal heat removal under physiological conditions, whereas radiation and convection contribute negligibly. The results suggest that retinal heat redistribution via conduction plays a major role in retinal thermal homeostasis, whereas choroidal blood perfusion primarily contributes to subsequent systemic heat removal rather than acting as a dominant limiting mechanism itself. This analysis provides quantitative support for the long-standing hypothesis that the choroid functions as a heat sink for the retina.
Co-transcriptional splicing and protein self-assembly are governed by coupled kinetic and thermodynamic constraints, such that modest changes in exon processing can propagate into substantial shifts in isoform-dependent mesoscale behavior. Here, we develop a cross-scale physical framework to examine whether transcription-associated kinetic pressure could differentially bias CPEB4 microexon selection and thereby reshape downstream isoform behavior. Using a simplified transcriptional kinetic model, we define an acetylation-associated high-throughput regime as a coarse-grained proxy for reduced time available for co-transcriptional exon recognition. Comparative sequence and structural analyses identify microexon 4 (me4) as less robust than microexon 3 (me3), with weaker cis-regulatory support and lower thermodynamic stability, consistent with greater susceptibility to omission under kinetically constrained conditions. A reduced probabilistic splicing framework accordingly predicts a directional bias against me4, superimposed on a basal transcript landscape in which the full-length isoform remains present. As a complementary downstream analysis, scaled-particle-theory calculations indicate that representative Δ4-enriched scenarios thermodynamically favor homotypic self-association under macromolecular crowding, suggesting a plausible physical amplification route for modest splicing bias. Orthogonal measurements in a yeast perturbation system identify oxidative and spectroscopic signatures compatible with strong butyrate-associated physicochemical stress, but these are interpreted as perturbation readouts rather than direct measurements of neuronal histone acetylation or splicing. Together, these results define a testable cross-scale framework linking transcription-associated kinetic constraints, directional microexon susceptibility, and crowding-dependent remodeling of the CPEB4 isoform assembly landscape.
In this article, we integrate the release of Wolbachia-carrying male mosquitoes with chemical insecticide spraying to formulate a coupled model that incorporates mosquito stage structure and malaria transmission dynamics. The model accounts for mating competition influenced by cytoplasmic incompatibility and employs a Holling-II type saturated release mechanism to dynamically match the release intensity of infected male mosquitoes with the density of wild mosquitoes. The study first verifies the positive invariance and boundedness of solutions of the model. It then derives the basic reproduction number, followed by the existence conditions and stability properties of equilibria. Subsequently, it reveals saddle-node bifurcation, forward bifurcation, backward bifurcation and their compound phenomena, thereby elucidating the abrupt dynamic changes near parameter thresholds of the system. Numerical simulations validate the theoretical results, quantify the regulatory effects of key parameters, and confirm that the combined strategy effectively suppresses mosquito population growth and interrupts malaria transmission through synergistic effects. The results illustrate that accurate tuning of critical parameters for the integrated control strategy and exploitation of synergism between Wolbachia and chemical insecticides can effectively modulate mosquito populations and block malaria transmission.
Amorphospaceis an abstract space of theoretically possible biological traits, shapes, or property values. It is interesting to explore which parts of a morphospace life occupies, as compared to those parts which could be occupied, but are not. Comparing random and natural non-coding (nc) RNA secondary structures is an established approach to studying morphospace occupation for RNA structures. Most earlier studies have focused on the minimum free energy structure, while relatively few have looked at the Boltzmann distribution, describing the ensemble of energetically suboptimal RNA folds. These suboptimal structures may have important roles and functions, and hence should be examined carefully. Here we compare random and natural ncRNA in terms of their Boltzmann distributions, finding that natural RNA tend to have very similar profiles to random RNA, with the main difference being that natural RNA are slightly more energetically stable, except for very short sequences (20 to 30 nucleotides) which appear to be equally or slightlylessstable. We infer that natural ncRNA and random RNA occupy similar parts of the morphospace, indicating that the biophysics of the genotype-phenotype map largely determines the ensemble properties of ncRNA.
In contrast with bird flocks, schools of fish, and migratory herds that display directed motion, mating swarms of male insects do not possess global order. Over the last decade or so, substantial progress has been made in quantitatively demonstrating the collective nature of these swarms. Their emergent collective behaviour cannot be determined by passive observations alone; instead, they must be perturbed. Here I identify a new putative emergent property of swarming by modelling the first detailed experimental studies of swarms of female insects. I show that swarms of female Anopheles gambiae mosquitoes are predicted to undergo cohesion-to-fragmentation (order-to-disorder) transitions and so are prone to disintegration in the face of environmental perturbations. This may explain why it is difficult, if not impossible, to observe pure female swarms of Anopheles mosquitoes in the wild. More generally, the new results may explain why most natural swarms of insects fit into one of two classes, namely uniformly distributed and clumped, which are the extremes of a continuum.
The outer retina exhibits several distinctive physiological features, including continuous turnover of rod photoreceptor outer segments (OS), a strong reliance on aerobic glycolysis despite oxygen availability, and an unusually high rate of choroidal blood flow. The mechanistic links between these phenomena remain incompletely understood. Here, we present a quantitative reaction-diffusion model of energy metabolism in rod photoreceptors that connects metabolic supply and demand to OS length and daily shedding. Because rod OS lack mitochondria, ATP and glycolytic intermediates must be supplied by diffusion from the inner segment, where oxidative phosphorylation and the initial steps of glycolysis occur. We model the diffusion and consumption of ATP and fructose-1,6-bisphosphate along the OS and show that diffusion-limited energy supply constrains OS length. Using literature-derived parameters, the model accurately predicts observed OS lengths in mammals (∼28µm) and amphibians (∼50µm), explains diurnal variations in OS length and tip shedding at light onset, and provides a unified explanation for the coexistence of high glycolytic flux, low oxygen extraction, and high choroidal blood flow in the outer retina. The model generates experimentally testable predictions regarding metabolite gradients along the OS and offers a general framework for understanding how metabolic constraints shape cellular morphology and tissue-level physiology.
Bacterial chemotaxis has long been viewed as operating near the physical limits of sensing, as originally articulated by Berg and Purcell. Recent information-theoretic analyses challenge this view, suggesting that Escherichia coli uses only a small fraction of the information available in ligand arrival statistics to bias its motion. How should such low information efficiency be interpreted at the level of behavior? Here, I argue that chemotactic performance is shaped not only by information transmission and noise, but by the strategy of movement itself. Using simple scaling arguments and minimal models, I show how run-and-tumble chemotaxis can remain robust to noise through symmetry and temporal averaging, even when internal information processing is inefficient. Comparing bacterial and eukaryotic chemotaxis highlights how different sensing strategies convert physical limits into observable behavior. These considerations suggest that low information efficiency need not imply poor performance, but may instead reflect an evolved balance between robustness, simplicity, and function.
Recent investigations into exercise-induced tumor suppression suggest that higher exercise frequency enhances tumor control when the total duration of exercise within a specified time window is not constrained. An equally compelling avenue for exploration is the effect of increased exercise frequency under the condition of a fixed total exercise duration within the same time frame. Using a mathematical model of IL-6-mediated interactions between natural killer cells and tumor cells, here we explore how different combinations of exercise and rest intervals-while maintaining a constant overall exercise volume-affect tumor suppression. Our results reveal a nonmonotonic tumor response and key metrics such as the time of maximum tumor suppression and the duration of tumor suppression are found to decrease with increasing exercise frequency. Interestingly, unlike earlier study where increasing exercise frequency leads to increase in tumor suppression, here we find that under fixed exercise volume constraints, increased frequency diminishes therapeutic efficacy of exercise, suggesting exercise bouts with longer duration are more effective in suppressing tumors. These findings highlight the importance of considering total exercise volume when designing exercise-based cancer interventions.
Microtubules are dynamic biopolymers whose lengths are continuously regulated by the concerted actions of polymerization, depolymerization, and motor-protein activity. While numerous mathematical models have explored the regulation of filament length, most have been formulated in the context of growth and shrinking at a single tip of a microtubule, effectively ignoring the mechanistic description of complex phenomena such as treadmilling. Here, we develop a multiscale model for microtubule length regulation that explicitly couples the kinetics of two classes of kinesin molecular motors to filament dynamics at both microtubule tips. Motor densities along the filament are modeled using one-dimensional parabolic partial differential equations. The microtubule length evolves dynamically through a shrinkage term that depends on motor density and which closes the system. In the adiabatic regime, where motor kinetics are fast relative to length dynamics, we derive a reduced model amenable to analytic study and identify simple parameter relationships distinguishing growth, disassembly, and treadmilling behavior. Numerical simulations of the full system reveal qualitatively distinct dynamical regimes and demonstrate how bidirectional motor transport modulates filament length distributions. We parametrize our model with both in vivo and in vitro data and thus lay the foundation for developing mathematical models yielding a better understanding of cytoskeleton dynamics in living cells.
Peroxiredoxins (PRXs) are antioxidant enzymes that exhibit ∼24 h redox-state oscillations across diverse life forms. These non-transcriptional rhythms operate independently of the canonical transcription-translation feedback loops, suggesting an ancient, conserved timekeeping mechanism. However, whether this redox oscillator meets the core circadian criteria-entrainment and temperature compensation-remains a key question. To address this, we developed and calibrated a mathematical model of the mitochondrial PRX/ sulfiredoxin/thioredoxin redox cycle using physiologically meaningful parameters. The model quantitatively reproduced experimental redox oscillations inA. thaliana, D. melanogaster, andM. musculus. Simulations revealed that the PRX redox oscillator possesses both temperature-compensated periodicity and the capacity for entrainment by periodic thermal and oxidative signals, thereby fulfilling the core criteria of a circadian clock. The inverse angular speed, when integrated over the closed orbit, is largely temperature-invariant, thus providing a mathematical basis for the observed period stability. The calculated phase response curves, which show phase-dependent shifts, together with the broad Arnold tongue for 1:1 resonance, demonstrate a substantial entrainment range that enables the internal rhythm to robustly lock onto periodic environmental Zeitgebers.
Synthetic cationic fluorophores are widely used as probes to measure the membrane potentials of bacterial cells, eukaryotic cells, and organelles (such as mitochondria) in electrophysiology experiments and live/dead assays. We applied an external oscillating electric field toEscherichia coliusing microelectrodes and observed that AC electro-osmosis caused fluorescence transients independent of bacterial electrophysiology, which could be mistaken for membrane depolarisation events. The fluorophores migrated within the microfluidic device in vortices, leading to concentration fluctuations manifested as dips in fluorescence. These fluorescent dips were universally present when using cationic fluorophores such as thioflavin-T, propidium iodide, Syto9, and Sytox Green, with or withoutE. colipresent, whenever AC voltages were applied. Furthermore, we also demonstrate that fluorescence dips in dense bacterial communities can arise from AC electro-osmosis rather than ion-channel activity. This cautionary tale highlights how electrical stimulation experiments in microbial communities can yield misleading results if electrokinetic effects are not accounted for. We quantified the relaxation times of fluorophores under AC electro-osmosis, which depended on the community, the cells, and the dye used: PI showed the shortest relaxation time and Syto9 the longest. Removing cells resulted in longer relaxation times, and introducing dense communities did not significantly alter the relaxation times compared with single-cell experiments. Furthermore, fluorescently labelled DNA and fluorescent colloidal beads (30-130 nm) also exhibited fluorescence dips due to AC electro-osmosis, demonstrating that charged molecules and particles readily penetrate and accumulate within these assemblies. To our knowledge, this is the first study to characterise AC electro-osmosis in dense bacterial communities, revealing the high mobility of charged molecules in such systems and suggesting possible applications for enhancing antibiotic delivery.
The G(4)C(2) hexanucleotide repeat expansion (HRE) in the c9orf72 locus is a mutation associated with amyotrophic lateral sclerosis. Recent evidence suggests a link with disrupted axonal trafficking in neurons. Here, using a neuronal-like cell line without or transfected with G(4)C(2) repeats, we characterize the motion of lysosomes inside neurites. The neurites grew either aligned to patterned lines, or oriented freely on a 2D-substrate. Implementing time-resolved (local) mean squared displacement analysis lysosome trajectories were split into sub-diffusive, diffusive, and super-diffusive parts. Our results suggest that in the presence of the G(4)C(2) repeats, lysosome trafficking is hampered, exhibiting overall decreased mean squared displacement and speed, more prominently inside aligned neurites. Moreover, a prominent effect in the super-diffusive drift velocity and diffusive motion diffusion coefficient was evident when the motion occurred inside aligned neurites. Trajectories which included super-diffusive motion, exhibited a varied ratio of anterograde/retrograde/neutral for both neurite geometries in the presence of G(4)C(2) repeats but a similar velocity decrease for both directions in each neurite geometry. Our findings support the hypothesis that impaired axonal trafficking emerges in the presence of the G(4)C(2) HRE, and demonstrate that this effect is more prominent when the neurites are aligned.
Rhythmic gene expression underlies core physiological processes across organisms, from circadian timekeeping to stress responses. Recent experiments suggest that the regulation of such rhythmic dynamics involves protein compartmentalization mediated by liquid-liquid phase separation (LLPS), yet the mechanisms by which LLPS feeds back onto oscillatory behaviour remain unclear. Here we develop a minimal two-phase gene-expression model in which proteins are synthesized in the dilute phase, reversibly partition into a protein-dense droplet phase, and repress their own production via condensate-mediated regulation. In the deterministic limit, LLPS does not generate limit cycles; instead, nonlinear partitioning and timescale separation between phase separation and protein turnover convert purely relaxational dynamics into damped oscillatory transients, altering the approach to equilibrium without producing sustained oscillations. In the stochastic regime, intrinsic noise interacting with this near-focus dynamics is amplified into noise-sustained, near-periodic fluctuations with a characteristic timescale, as revealed by the power spectral density and autocorrelation functions. These results show how LLPS reshapes oscillatory signatures by encoding and filtering temporal signals in phase-specific ways, providing a quantitative framework for interpreting LLPS-rhythm coupling and for engineering biomolecular systems with tunable dynamic behaviour.
Protein sequence determines structure, function, and dynamics, yet the gap between sequenced proteins and experimentally determined structures continues to widen. While machine learning approaches like AlphaFold2 have transformed structural biology, they require substantial computational resources. Coevolution-based methods such as mutual information (MI) and direct coupling analysis (DCA), such as GREMLIN, offer alternatives but depend on extensive multiple sequence alignments with thousands of homologs. Here, we present a template-based pattern-matching approach that predicts protein contact maps by identifying conserved structural motifs from homologous experimental structures. Our method encodes spatial arrangements of up to five residues within 8.0 Å distance as sequence patterns, then aligns these patterns to query sequences to predict residue-residue contacts. Critically, our approach requires only a modest number of structural templates (typically 50-500) and runs on standard hardware without graphics processing units or high-performance computing clusters, processing proteins in 12-16 min regardless of length. We validated our method on 25 well-characterized protein domains, achieving correlations of 0.735-0.942 with experimental contact maps. Comparative analysis against MI and GREMLIN demonstrated that our method achieved better contact coverage while maintaining comparable accuracy. To demonstrate broader applicability, we tested on 7599 poorly annotated sequences using high-confidence AlphaFold structures as reference, achieving meanF1-score of 0.609 ± 0.095 and accuracy of 0.954 ± 0.036. Our pattern matching approach provides a computationally efficient, interpretable alternative to both deep learning and coevolution-based methods, particularly valuable for proteins with limited sequence homologs or when rapid predictions are needed.
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 distance Rij, 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.
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.
Tumor growth occurs within a complex tumor microenvironment (TME) composed of many interacting cell types. However, the signs of tumor-immune interactions in this ecosystem are not random and possess a structure, the immune cell types in the TME tend to organize into two functional communities: a pro-tumor team and an anti-tumor team, each internally cooperative but mutually antagonistic, forming a two-team ecosystem. Quantitatively predicting the ecological outcomes of such interactions remains challenging due to cellular diversity and interaction variability, and the exact dynamical regimes accessible to such a two-team ecosystem remain unknown. Here, we model tumor-immune interactions as a structured ecosystem with two competing teams using a generalized Lotka-Volterra framework and analyze it using the cavity method. We derive phase diagrams that delineate when these two communities coexist, when one dominates, and how these outcomes depend on intra-team cooperation, cross-team inhibition, and ecological heterogeneity. Our work provides a foundation for understanding tumor-immune dynamics from a community ecology perspective.
Intracellular processes often rely on the timely encounter of mobile reaction partners, including intermittently motor-driven organelles. The underlying cytoskeletal network presents a complex landscape that both directs particle movement and introduces quenched disorder through filament organization. We investigate the mean first encounter times for pairs of intermittently processive and diffusive particles, moving in two dimensions with and without a fixed filament network. In unstructured domains, increasing particle run-length enhances exploration of the domain, but tends to slow down the encounter times compared to equivalent diffusing particles. Encounters for long-running particles occur preferentially near the periphery, contrasting with bulk encounters for the purely diffusive case. When particles are unbiased in their runs along dense filament networks, encounters are shown to be well approximated by a continuum run-and-tumble model. For biased particles, regions of convergent filament orientation serve as traps that slow the overall spatial exploration but can allow for faster encounter rates by funneling particles into regions of reduced dimensionality. These findings provide a framework for estimating intracellular encounter kinetics, highlighting the role of key physical features such as the effective diffusivity, run times, and network architecture.
