Biological Physics

arXiv:physics.bio-ph

Molecular and cellular biophysics, biomechanics, biophysical chemistry.

Trending in Biological Physics

Computational Modeling of Exciton-bath Hamiltonians for LH2 and LH3 Complexes of Purple Photosynthetic Bacteria at Room Temperature

Light harvesting 2 (LH2) complex is a primary component of the photosynthetic unit of purple bacteria that is responsible for harvesting and relaying excitons. The electronic absorption line shape of LH2 contains two major bands at 800 nm and 850 nm wavelength regions. Under low light condition, some species of purple bacteria replace LH2 with LH3, a variant form with almost the same structure as the former but with distinctively different spectral features. The major difference between the absorption line shapes of LH2 and LH3 is the shift of the 850 nm band of the former to a new 820 nm region. The microscopic origin of this difference has been subject to some theoretical/computational investigations. However, the genuine molecular level source of such difference is not clearly understood yet. This work reports a comprehensive computational study of LH2 and LH3 complexes so as to clarify different molecular level features of LH2 and LH3 complexes and to construct simple exciton-bath models with a common form. All-atomistic molecular dynamics (MD) simulations of both LH2 and LH3 complexes provide detailed molecular level structural differences of BChls in the two complexes, in particular, in their patterns of hydrogen bonding (HB) and torsional angles of the acetyl group. Time-dependent density functional theory calculation of the excitation energies of BChls for structures sampled from the MD simulations, suggests that the observed differences in HB and torsional angles cannot fully account for the experimentally observed spectral shift of LH3. Potential sources that can explain the actual spectral shift of LH3 are discussed, and their magnitudes are assessed through fitting of experimental line shapes.

2601.0057624

Jan 2026Biological Physics

Exchange controls coarsening of surface condensates

Biological membranes often exhibit heterogeneous protein patterns, which cells control. Strong patterns, like the polarity spot in budding yeast, can be described as surface condensates, formed by physical interactions between constituents. However, it is unclear how these interactions affect the material exchange with the bulk. To study this, we analyze a thermodynamically consistent model, which reveals that passive exchange generally accelerates the coarsening of surface condensates. Active exchange can further accelerate coarsening, although it can also fully arrest it and induce complex patterns involving various length scales. We reveal how these behaviors are related to non-local transport via diffusion through the bulk, rationalizing the various scaling laws we observe and allowing us to interpret biologically relevant scenarios.

2511.036191

Nov 2025Biological Physics

Marr's three levels for embryonic development: information, dynamical systems, gene networks

Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through cellular interactions. We propose that both extremes -- as well as the continuum of intermediate cases -- can be conceptualized as information processing systems, whose operation can be described using ``Marr's three levels of analysis'': the computational problem being solved, the algorithms employed, and their molecular implementation. At the first level, we argue that normative theories, such as information-theoretic optimization principles, provide a formalization of the computational problem. At the second level, we show how simplified information processing architectures provide a framework for developmental algorithms, which are formalized mathematically using dynamical systems theory. At the third level, the implementation of developmental algorithms is described by mechanistic biophysical and gene regulatory network models.

2510.245361

Oct 2025Biological Physics

Dynamic principles of concentration buffering through liquid-liquid phase separation

Living systems must maintain robust biochemical function despite fluctuations that span a wide range of timescales. Biomolecular condensates formed by liquid-liquid phase separation (LLPS) have been shown to buffer concentration fluctuations, but the principles governing their dynamic regulation remain unclear. We address this by probing the response of LLPS to oscillatory perturbations that mimic fluctuations across different timescales, establishing the first systematic frequency-domain analysis of concentration buffering by condensates. We find that condensates act as frequency-selective filters: the perturbed dilute phase behaves as a high-pass filter, while the dense phase attenuates both low- and high-frequency perturbations. We establish quantitative links between LLPS parameters including interaction strength, droplet size, and molecular diffusivity, and the timescale range over which condensates effectively buffer concentration fluctuations. These findings establish the fundamental dynamical limits of concentration buffering by LLPS, with implications for how cells may use LLPS to adapt to fluctuating environments and for the design of synthetic condensates with programmable control properties.

2510.205532

Oct 2025Biological Physics

Inheritance entropy quantifies epigenetic regulation of cell-cycle exit in human bone marrow stromal cells

Human bone marrow stromal cells (BMSC) include skeletal stem cells with ground-breaking therapeutic potential. However, BMSC colonies have very heterogeneous in vivo behaviour, due to their different potency; this unpredictability is the greatest hurdle to the development of skeletal regeneration therapies. Colony-level heterogeneity urges a fundamental question: how is it possible that one colony as a collective unit behaves differently from another one? If cell-to-cell variability were just an uncorrelated random process, a million cells in a transplant-bound colony would be enough to yield statistical homogeneity, hence washing out any colony-level traits. A possible answer is that the differences between two originating cells are transmitted to their progenies and collectively persist through an hereditary mechanism. But non-genetic inheritance remains an elusive notion, both at the experimental and at the theoretical level. Here, we prove that heterogeneity in the lineage topology of BMSC clonal colonies is determined by heritable traits that regulate cell-cycle exit. The cornerstone of this result is the definition of a novel entropy of the colony, which measures the hereditary ramifications in the distribution of inactive cells across different branches of the proliferation tree. We measure the entropy in 32 clonal colonies, obtained from single-cell lineage tracing experiments, and show that in the greatest majority of clones this entropy is decisively smaller than that of the corresponding non-hereditary lineage. This result indicates that hereditary epigenetic factors play a major role in determining cycle exit of bone marrow stromal cells.

2510.185891

Oct 2025Biological Physics

Quantifying the compressibility of the human brain

In the human brain, the allowed patterns of activity are constrained by the correlations between brain regions. Yet it remains unclear which correlations -- and how many -- are needed to predict large-scale neural activity. Here, we present an information-theoretic framework to identify the most important correlations, which provide the most accurate predictions of neural states. Applying our framework to cortical activity in humans, we discover that the vast majority of variance in activity is explained by a small number of correlations. This means that the brain is highly compressible: only a sparse network of correlations is needed to predict large-scale activity. We find that this compressibility is strikingly consistent across different individuals and cognitive tasks, and that, counterintuitively, the most important correlations are not necessarily the strongest. Together, these results suggest that nearly all correlations are not needed to predict neural activity, and we provide the tools to uncover the key correlations that are.

2510.163271

Oct 2025Biological Physics

Sparing of DNA irradiated with Ultra-High Dose-Rates under Physiological Oxygen and Salt conditions

Cancer treatment with radiotherapy aims to kill tumor cells and spare healthy tissue.Thus,the experimentally observed sparing of healthy tissue by the FLASH effect during irradiations with ultra-high dose rates (UHDR) enables clinicians to extend the therapeutic window.However, the underlying radiobiological and chemical mechanisms are far from being understood.DNA is one of the main molecular targets for radiotherapy.Ionizing radiation damage to DNA in water depends strongly on salt,pH,buffer and oxygen content of the solvent.Here we present a study of plasmid DNA pUC19,irradiated with 18MeV electrons at low dose rates (LDR) and UHDR under tightly controlled ambient and physiological oxygen conditions in PBS at pH 7.4.For the first time a sparing effect of DNA strand-break induction between UHDR(>10MGy/s) and LDR(<0.1Gy/s) irradiated plasmid DNA under physiological oxygen, salt and pH is observed for total doses above 10Gy.Under physiological oxygen (physoxia,5%O2,40mmHg),more single (SSB) and double strand-breaks (DSB) are observed when exposed to LDR, than to UHDR.This behaviour is absent for ambient oxygen (normoxia,21%O2,150-160mmHg).The experiments are accompanied by TOPAS-nBio based particle-scattering and chemical MCS to obtain information about the yields of reactive oxygen species (ROS).Hereby,an extended set of chemical reactions was considered, which improved upon the discrepancy between experiment and simulations of previous works, and allowed to predict DR dependent g-values of hydrogen peroxide (H2O2).To explain the observed DNA sparing effect under FLASH conditions at physoxia,the following model was proposed:The interplay of O2 with OH induced H-abstraction at the phosphate backbone,and the conversion of DNA base-damage to SSB,under consideration of the dose-rate dependent H3O+ yield via beta elimination processes is accounted for, to explain the observed behavior.

2510.154781

Oct 2025Biological Physics

Confinement reduces surface accumulation of swimming bacteria

Many swimming bacteria naturally inhabit confined environments, yet how confinement influences their swimming behaviors remains unclear. Here, we combine experiments, continuum modeling and particle-based simulations to investigate near-surface bacterial swimming in dilute suspensions under varying confinement. Confinement reduces near-surface accumulation and facilitates bacterial escape. These effects are quantitatively captured by models incorporating the force quadrupole, a higher-order hydrodynamic singularity, that generates a rotational flow reorienting bacteria away from surfaces. Under strong confinement, bacterial trajectories straighten due to the balancing torques exerted by opposing surfaces. These findings highlight the role of hydrodynamic quadrupole interactions in near-surface bacterial motility, with implications for microbial ecology, infection control, and industrial applications.

2510.080921

Oct 2025Biological Physics

TopoSPAM: Topology grounded Simulation Platform for morphogenesis and biological Active Matter

We present a topology grounded, multiscale simulation platform for morphogenesis and biological active matter. Morphogenesis and biological active matter represent keystone problems in biology with additional, far-reaching implications across the biomedical sciences. Addressing these problems will require flexible, cross-scale models of tissue shape, development, and dysfunction that can be tuned to understand, model, and predict relevant individual cases. Current approaches to simulating anatomical or cellular subsystems tend to rely on static, assumed shapes. Meanwhile, the potential for topology to provide natural dimensionality reduction and organization of shape and dynamical outcomes is not fully exploited. TopoSPAM combines ease of use with powerful simulation algorithms and methodological advances, including active nematic gels, topological-defect-driven shape dynamics, and an active 3D vertex model of tissues. It is capable of determining emergent flows and shapes across scales.

2509.249051

Sep 2025Biological Physics

Modal analysis and optimization of swimming active filaments

Active flexible filaments form the classical continuum framework for modelling the locomotion of spermatozoa and algae driven by the periodic oscillation of flagella. This framework also applies to the locomotion of various artificial swimmers. Classical studies have quantified the relationship between internal forcing (localised or distributed internal moments or forces) and external output (filament shape and swimming speed). In this paper, we pose locomotion as a mathematical optimisation problem and demonstrate that the swimming of an isolated active filament can be accurately described and optimised using a small number of eigenmodes, significantly reducing computational complexity. In particular, we reveal that the motion of a filament with monophasic forcing, relevant to recently proposed artificial swimmers, is governed by exactly four forcing eigenmodes, only two of which are independent. We further present optimisations of such swimmers under various constraints.

2510.096271

Sep 2025Biological Physics

Joint Probability Distribution of mRNA and Protein Molecules in a Stochastic Gene Expression Model

Stochastic modeling of gene expression is a classic problem in theoretical biophysics. However, models formulated via chemical master equation have long been considered analytically intractable unless burst approximation is applied. This article shows that general stochastic gene expression models with an arbitrary number of gene states admit direct analysis. Based on chemical master equation and high-dimensional binomial moment method, we derive recurrence relations for binomial moments in steady state, yielding analytical expressions to arbitrary order in a hierarchical manner. Subsequently, the joint probability mass function of mRNA and protein copy number can be reconstructed. An algorithm is developed for numerical computation. Particularly, explicit expressions for low-order cumulants are presented. Compared with models under burst approximation, the mean remains exact, whereas the variance typically differs. We estimate the difference between two second-order binomial moments using functional analysis, therefore evaluating the validity of burst approximation.

2509.091221

Sep 2025Biological Physics

Multiscale Growth Kinetics of Model Biomolecular Condensates Under Passive and Active Conditions

Living cells exhibit a complex organization comprising numerous compartments, among which are RNA- and protein-rich membraneless, liquid-like organelles known as biomolecular condensates. Energy-consuming processes regulate their formation and dissolution, with (de-)phosphorylation by specific enzymes being among the most commonly involved reactions. By employing a model system consisting of a phosphorylatable peptide and homopolymeric RNA, we elucidate how enzymatic activity modulates the growth kinetics and alters the local structure of biomolecular condensates. Under passive condition, time-resolved ultra-small-angle X-ray scattering with synchrotron source reveals a nucleation-driven coalescence mechanism maintained over four decades in time, similar to the coarsening of simple binary fluid mixtures. Coarse-grained molecular dynamics simulations show that peptide-decorated RNA chains assembled shortly after mixing constitute the relevant subunits. In contrast, actively-formed condensates initially display a local mass fractal structure, which gradually matures upon enzymatic activity before condensates undergo coalescence. Both types of condensate eventually reach a steady state but fluorescence recovery after photobleaching indicates a peptide diffusivity twice higher in actively-formed condensates consistent with their loosely-packed local structure. We expect multiscale, integrative approaches implemented with model systems to link effectively the functional properties of membraneless organelles to their formation and dissolution kinetics as regulated by cellular active processes.

2508.163981

Aug 2025Biological Physics

Engineering Supercomputing Platforms for Biomolecular Applications

A range of computational biology software (GROMACS, AMBER, NAMD, LAMMPS, OpenMM, Psi4 and RELION) was benchmarked on a representative selection of HPC hardware, including AMD EPYC 7742 CPU nodes, NVIDIA V100 and AMD MI250X GPU nodes, and an NVIDIA GH200 testbed. The raw performance, power efficiency and data storage requirements of the software was evaluated for each HPC facility, along with qualitative factors such as the user experience and software environment. It was found that the diversity of methods used within computational biology means that there is no single HPC hardware that can optimally run every type of HPC job, and that diverse hardware is the only way to properly support all methods. New hardware, such as AMD GPUs and Nvidia AI chips, are mostly compatible with existing methods, but are also more labour-intensive to support. GPUs offer the most efficient way to run most computational biology tasks, though some tasks still require CPUs. A fast HPC node running molecular dynamics can produce around 10GB of data per day, however, most facilities and research institutions lack short-term and long-term means to store this data. Finally, as the HPC landscape has become more complex, deploying software and keeping HPC systems online has become more difficult. This situation could be improved through hiring/training in DevOps practices, expanding the consortium model to provide greater support to HPC system administrators, and implementing build frameworks/containerisation/virtualisation tools to allow users to configure their own software environment, rather than relying on centralised software installations.

2506.155851

Jun 2025Biological Physics

Active waves from non-reciprocity and cytoplasmic exchange

Pattern formation in active biological matter typically arises from the feedback between chemical concentration fields and mechanical stresses. The actomyosin cortex of cells is an archetypal example of an active thin film that displays such patterns. Here, we show how pulsatory patterns emerge in a minimal model of the actomyosin cortex with a single stress-regulating chemical species that exchanges material with the cytoplasm via a linear turnover reaction. Deriving a low-dimensional amplitude-phase model, valid for a one-dimensional periodic domain and a spherical surface, we show that nonlinear waves arise from a secondary parity-breaking bifurcation that originates from the nonreciprocal interaction between spatial modes of the concentration field. Numerical analysis confirms these analytical predictions, and also reveals analogous pulsatory patterns on impermeable domains. Our study provides a generic route to the emergence of nonreciprocity-driven pulsatory patterns that can be controlled by both the strength of activity and the turnover rate.

2505.097401

May 2025Biological Physics

Efficiently driving F$_1$ molecular motor in experiment by suppressing nonequilibrium variation

F$_1$-ATPase (F$_1$) is central to cellular energy transduction. Forcibly rotated by another motor F$_\mathrm{o}$, F$_1$ catalyzes ATP synthesis by converting mechanical work into chemical free energy stored in the molecule ATP. The details of how F$_\mathrm{o}$ drives F$_1$ are not fully understood; however, evaluating efficient ways to rotate F$_1$ could provide fruitful insights into this driving since there is a selective pressure to improve efficiency. Here, we show that rotating F$_1$ with an angle clamp is significantly more efficient than a constant torque. Our experiments, combined with theory and simulation, indicate that the angle clamp significantly suppresses the nonequilibrium variation that contributes to the futile dissipation of input work.

2505.011014

May 2025Biological Physics

The Effect of Hydration and Dynamics on the Mass Density of Single Proteins

The density of a protein molecule is a key property within a variety of experimental techniques. We present a computational method for determining protein mass density that explicitly incorporates hydration effects. Our approach uses molecular dynamics simulations to quantify the volume of solvent excluded by a protein. Applied to a dataset of 260 soluble proteins, this yields an average density of 1.296 g cm-3, notably lower than the widely cited value of 1.35 g cm-3. Contrary to previous suggestions, we find no correlation between protein density and molecular weight. We instead find correlations with residue composition, particularly with hydrophobic amino acid content. Using these correlations, we train a regressor capable of accurately predicting protein density from sequence-derived features alone. Examining the effect of incorporating water molecules on the measured density, we find that water molecules buried in internal cavities have a negligible effect, whereas those at the surface have a profound impact. Furthermore, by calculating the density of a titin domain and of the Bovine Pancreatic Trypsin over molecular dynamics trajectories, we show that individual proteins can occupy states with close but distinguishable densities. Finally, we analyse the density of water in the vicinity of proteins, showing that the first two hydration shells exhibit higher density than bulk water. When included in cumulative density calculations, these hydration layers contribute to a net increase in local solvent density. Overall, we find that proteins are less dense than previously reported, which is offset by their ability to induce a higher density of water in their vicinity.

2504.149831

Apr 2025Biological Physics

Topology of the simplest gene switch

Complex gene regulatory networks often display emergent simple behavior. Sometimes this simplicity can be traced to a nearly equivalent energy landscape, but not always. Here, we show how a topological theory for stochastic and biochemical networks can predict phase transitions between dynamical regimes, where the simplest landscape paradigm would fail. We demonstrate the utility of this topological approach for a simple gene network, revealing a new oscillatory regime in addition to previously recognized multimodal stationary phases. We show how local winding numbers predict the steady-state locations in the single-mode and bimodal phases, and a flux analysis predicts the respective strengths of the steady-state peaks.

2503.017171

Mar 2025Biological Physics

In-situ biological ozone detection by measuring electrochemical impedances of plant tissues

This work demonstrates biological detection of a low concentration of O3 by measuring electrochemical impedances of tissues in tobacco and tomato plants located indoor and outdoor. The lower range of generated ozone in the O3-air mix is about 30 ug/m3 over the atmospheric level, which allows phytosensors to be considered as biodetectors of environmental pollutants. The ozone stress affects stomatal regulation that in turn influences the hydrodynamics of fluid transport system in plants. Sensors utilize electrochemical impedance spectroscopy (EIS) to measure ionic fluid content at several positions on the plant stem and calculate a variation of fluid distribution in control and experimental cases indoors. Outdoor setup uses the same methodology and sensors but different analysis due to uncontrolled nature of ozone pollution and the overlap of various stressors. The measurement results indicate a qualitative and quantitative reaction of hydrodynamic system to changes in O3 concentration in the upper part of stem with a delay of 10-20 minutes between the onset of exposure and biological response. The probability of false-negative responses from a single plant is about 0.15 +/-0.06. Pooling data from at least three plants allows for 92% confidence in detecting excess O3. Measurements on days with low and high ozone levels of 80 ug/m3 to 130 ug/m3 result in a 2.33-fold difference in sensor values and thus demonstrate biological detection of high O3 also outdoors. Statistically significant data include 948 sensor-plant attempts during 51 days with 9 plants and about 10E7 samples collected in automated experiments. Long-term measurements have demonstrated the high reliability of electrochemical sensors, especially in harsh outdoor conditions with rain, heat and UV/IR radiations. The described approach has applications in environmental monitoring, biological pollution detection and biosensing.

2411.163213

Nov 2024Biological Physics

Super-resolved anomalous diffusion: deciphering the joint distribution of anomalous exponent and diffusion coefficient

The molecular motion in heterogeneous media displays anomalous diffusion by the mean-squared displacement $\langle X^2(t) \rangle = 2 D t^α$. Motivated by experiments reporting populations of the anomalous diffusion parameters $α$ and $D$, we aim to disentangle their respective contributions to the observed variability when this last is due to a true population of these parameters and when it arises due to finite-duration recordings. We introduce estimators of the anomalous diffusion parameters on the basis of the time-averaged mean squared displacement and study their statistical properties. By using a copula approach, we derive a formula for the joint density function of their estimations conditioned on their actual values. The methodology introduced is indeed universal, it is valid for any Gaussian process and can be applied to any quadratic time-averaged statistics. We also explain the experimentally reported relation $D\propto\exp(αc_1+c_2)$ for which we provide the exact expression. We finally compare our findings to numerical simulations of the fractional Brownian motion and quantify their accuracy by using the Hellinger distance.

2410.181331

Oct 2024Biological Physics

JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles

Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside their training data. We propose JAMUN which performs MD in a smoothed, noised space of all-atom 3D conformations of molecules by utilizing the framework of walk-jump sampling. JAMUN enables ensemble generation for small peptides at rates of an order of magnitude faster than traditional molecular dynamics. The physical priors in JAMUN enables transferability to systems outside of its training data, even to peptides that are longer than those originally trained on. Our model, code and weights are available at https://github.com/prescient-design/jamun.

2410.146214

Oct 2024Biological Physics

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Accelerator Physics Atmospheric and Oceanic Physics Applied Physics Atomic and Molecular Clusters Atomic Physics

101 papers

Geometric developmental principles for the emergence of brain-like weighted and directed neuronal networks

Brain networks exhibit remarkable structural properties, including high local clustering, short path lengths, and heavy-tailed weight and degree distributions. While these features are thought to enable efficient information processing with minimal wiring costs, the fundamental principles that generate such complex network architectures across species remain unclear. Here, we analyse single-neuron resolution connectomes across five species (C. Elegans, Platynereis, Drosophila M., zebrafish and mouse) to investigate the fundamental wiring principles underlying brain network formation. We show that distance-dependent connectivity alone produces small-world networks, but fails to generate heavy-tailed distributions. By incorporating weight-preferential attachment, which arises from spatial clustering of synapses along neurites, we reproduce heavy-tailed weight distributions while maintaining small-world topology. Adding degree-preferential attachment, linked to the extent of dendritic and axonal arborization, enables the generation of heavy-tailed degree distributions. Through systematic parameter exploration, we demonstrate that the combination of distance dependence, weight-preferential attachment, and degree-preferential attachment is sufficient to reproduce all characteristic properties of empirical brain networks. Our results reveal that activity-independent geometric constraints during neural development can account for the conserved architectural principles observed across evolutionarily distant species, suggesting universal mechanisms governing neural circuit assembly.

2601.05021Jan 2026

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Roadmap for Condensates in Cell Biology

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.

2601.03677Jan 2026

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Computational Modeling of Exciton-bath Hamiltonians for LH2 and LH3 Complexes of Purple Photosynthetic Bacteria at Room Temperature

2601.0057624Jan 2026

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Quantum Dynamics of a Nanorotor Driven by a Magnetic Field

A molecular rotor mechanism is proposed to explain weak magnetic field effects in biology. Despite being nanoscale (1 nm), this rotor exhibits quantum superposition and interference. Analytical modeling shows its quantum dynamics are highly sensitive to weak, but not strong, magnetic fields. Due to its enhanced moment of inertia, the rotor maintains quantum coherence relatively long, even in a noisy cellular environment. Operating at the mesoscopic boundary between quantum and classical behavior, such a rotor embedded in cyclical biological processes could exert significant and observable biological influence.

2512.15213Dec 2025

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A Compact Incubation Platform for Long-Term Cultivation of Biological Samples for Nitrogen-Vacancy Center Widefield Microscopy

Nitrogen-vacancy (NV) centers in diamond provide a versatile quantum sensing platform for biological imaging through magnetic field detection, offering unlimited photostability and the ability to perform long-term observations without photobleaching or phototoxicity. However, conventional stage-top incubators are incompatible with the unique requirements for NV widefield magnetometry to study cellular dynamics. Here, we present a purpose-built compact incubation platform that maintains precise environmental control of temperature, CO$_2$ atmosphere, and humidity while accommodating the complex constraints of NV widefield microscopy. The system employs a 3D-printed biocompatible chamber with integrated heating elements, temperature control, and humidified gas flow to create a stable physiological environment directly on the diamond sensing surface. We demonstrate sustained viability and proliferation of HT29 colorectal cancer cells over 90 hours of continuous incubation, with successful magnetic field imaging of immunomagnetically labeled cells after extended cultivation periods. This incubation platform enables long-term cultivation and real-time monitoring of biological samples on NV widefield magnetometry platforms, opening new possibilities for studying dynamic cellular processes using quantum sensing technologies.

2512.14482Dec 2025

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Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging

Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold standard. Thus, given the absence of PINN-based approaches for T2 estimation, we propose embedding the fundamental physics of MRI, the Bloch equation, in the loss of PINN, which is solely based on target scan data and does not require a pre-defined training database. Furthermore, by deriving rigorous upper bounds for both the T2 estimation error and the generalization error of the Bloch equation solution, we establish a theoretical foundation for evaluating the PINN's quantitative accuracy. Even without access to the ground truth or a gold standard, this theory enables us to estimate the error with respect to the real quantitative parameter T2. The accuracy of T2 mapping and the validity of the theoretical analysis are demonstrated on a numerical cardiac model and a water phantom, where our method exhibits excellent quantitative precision in the myocardial T2 range. Clinical applicability is confirmed in 94 acute myocardial infarction (AMI) patients, achieving low-error quantitative T2 estimation under the theoretical error bound, highlighting the robustness and potential of PINN.

2512.14211Dec 2025

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Quantum Correlation and Synchronisation-Enhanced Energy Transfer in Driven-Dissipative Light-Harvesting Dimers

Quantum synchronisation has recently been proposed as a mechanism for electronic excitation energy transfer in light-harvesting complexes, yet its robustness in driven-dissipative settings remains under active investigation. Here, we revisit this mechanism in cryptophyte photosynthetic antennae using an exciton--vibrational dimer model. By comparing the full open quantum dynamics with semi-classical rate equations for electronic density-matrix elements and vibrational observables, we demonstrate that quantum correlations between electronic and vibrational degrees of freedom, beyond the semi-classical factorised limit, underpin the emergence of quantum synchronisation. Furthermore, we introduce an environment-assisted transfer mechanism arising as a nonlinear, non-Condon correction to the dipole--dipole interaction. This mechanism enables long-lived quantum coherence and continuous, synchronisation-enhanced energy transfer in a driven-dissipative regime, thereby suggesting new avenues for investigating photosynthetic energy-transfer dynamics.

2512.12660Dec 2025

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Data-Efficient Learning of Anomalous Diffusion with Wavelet Representations: Enabling Direct Learning from Experimental Trajectories

Machine learning (ML) has become a versatile tool for analyzing anomalous diffusion trajectories, yet most existing pipelines are trained on large collections of simulated data. In contrast, experimental trajectories, such as those from single-particle tracking (SPT), are typically scarce and may differ substantially from the idealized models used for simulation, leading to degradation or even breakdown of performance when ML methods are applied to real data. To address this mismatch, we introduce a wavelet-based representation of anomalous diffusion that enables data-efficient learning directly from experimental recordings. This representation is constructed by applying six complementary wavelet families to each trajectory and combining the resulting wavelet modulus scalograms. We first evaluate the wavelet representation on simulated trajectories from the andi-datasets benchmark, where it clearly outperforms both feature-based and trajectory-based methods with as few as 1000 training trajectories and still retains an advantage on large training sets. We then use this representation to learn directly from experimental SPT trajectories of fluorescent beads diffusing in F-actin networks, where the wavelet representation remains superior to existing alternatives for both diffusion-exponent regression and mesh-size classification. In particular, when predicting the diffusion exponents of experimental trajectories, a model trained on 1200 experimental tracks using the wavelet representation achieves significantly lower errors than state-of-the-art deep learning models trained purely on $10^6$ simulated trajectories. We associate this data efficiency with the emergence of distinct scale fingerprints disentangling underlying diffusion mechanisms in the wavelet spectra.

2512.08510Dec 2025

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Modular connectivity in neural networks emerges from Poisson noise-motivated regularisation, and promotes robustness and compositional generalisation

Circuits in the brain commonly exhibit modular architectures that factorise complex tasks, resulting in the ability to compositionally generalise and reduce catastrophic forgetting. In contrast, artificial neural networks (ANNs) appear to mix all processing, because modular solutions are difficult to find as they are vanishing subspaces in the space of possible solutions. Here, we draw inspiration from fault-tolerant computation and the Poisson-like firing of real neurons to show that activity-dependent neural noise, combined with nonlinear neural responses, drives the emergence of solutions that reflect an accurate understanding of modular tasks, corresponding to acquisition of a correct world model. We find that noise-driven modularisation can be recapitulated by a deterministic regulariser that multiplicatively combines weights and activations, revealing rich phenomenology not captured in linear networks or by standard regularisation methods. Though the emergence of modular structure requires sufficiently many training samples (exponential in the number of modular task dimensions), we show that pre-modularised ANNs exhibit superior noise-robustness and the ability to generalise and extrapolate well beyond training data, compared to ANNs without such inductive biases. Together, our work demonstrates a regulariser and architectures that could encourage modularity emergence to yield functional benefits.

2512.13707Dec 2025

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From Toggle to Tuning: Controlling Turing Patterns in Gene Circuits

Controlling spatial patterns in synthetic biological systems remains challenging due to poor parameter robustness and limited experimental tunability. We introduce two complementary mechanisms-the pattern switch and the pattern dial-to systematically control Turing pattern formation in gene circuits. The switch toggles pattern onset via a single parameter, while the dial enables transitions between distinct pattern types using weakly nonlinear amplitude equations. Analyzing network size reveals a key trade-off: small networks are easier to control but less robust, while larger networks gain robustness at the cost of tunability-suggesting a sweet spot for both evolvability and designability. Our results offer practical design rules for engineering programmable patterns in living systems.

2512.01652Dec 2025

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Stochastic Kinetics of Protein Molecules in a Gene Expression Model under Burst Approximation

The burst approximation is a widely-used technique to simplify stochastic gene expression models. However, both analytical results and efficient algorithms are currently unavailable for general models under the burst approximation. In this article, we systematically analyze surrogate models with multiple gene states. Analytical solution to the chemical master equation is provided, which is further exploited from two perspectives. Theoretically, several inequalities are established using functional analysis. We conclude that the steady-state distribution of protein copy number is bounded from above by a constant multiple of some negative binomial distribution if the burst size is geometrically distributed. Computationally, efficient algorithms are developed under three circumstances based on the standard binomial moment method.

2511.14913Nov 2025

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Completion of partial structures using Patterson maps with the CrysFormer machine learning model

Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not incorporate the experimental measurements directly, such as X-ray crystallographic diffraction data. To this end, we explore an approach that more tightly couples these traditional crystallographic and recent ML-based methods, by training a hybrid 3-d vision transformer and convolutional network on inputs from both domains. We make use of two distinct input constructs / Patterson maps, which are directly obtainable from crystallographic data, and ``partial structure'' template maps derived from predicted structures deposited in the AlphaFold Protein Structure Database with subsequently omitted residues. With these, we predict electron density maps that are then post-processed into atomic models through standard crystallographic refinement processes. Introducing an initial dataset of small protein fragments taken from Protein Data Bank entries and placing them in hypothetical crystal settings, we demonstrate that our method is effective at both improving the phases of the crystallographic structure factors and completing the regions missing from partial structure templates, as well as improving the agreement of the electron density maps with the ground truth atomic structures.

2511.10440Nov 2025

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Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies

Organisms adapt to volatile environments by integrating sensory information with internal memory, yet their information processing is constrained by resource limitations. Such limitations can fundamentally alter optimal estimation strategies in biological systems. For example, recent experiments suggest that organisms exhibit nonmonotonic phase transitions between memoryless and memory-based estimation strategies depending on sensory reliability. However, an analytical understanding of these resource-induced phase transitions is still missing. This Letter presents an analytical characterization of resource-induced phase transitions in optimal estimation strategies. Our result identifies the conditions under which resource limitations alter estimation strategies and analytically reveals the mechanism underlying the emergence of discontinuous, nonmonotonic, and scaling behaviors. These results provide a theoretical foundation for understanding how limited resources shape information processing in biological systems.

2511.10184Nov 2025

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Dynamics of chemo-receptor activity with time-periodic attractant field

When exposed to a time-periodic chemical signal, an \textit{E.~coli} cell responds by modulating its receptor activity in a similar time-periodic manner. However, there exists a phase lag between the applied signal and the activity response. We study the variation of the activity amplitude and phase lag as a function of the applied frequency~$ω$, using numerical simulations. The amplitude increases with~$ω$, reaches a plateau, and then decreases again for large~$ω$. The phase lag increases monotonically with~$ω$ and finally saturates to $3π/2$ when~$ω$ is large. The activity is no longer a single-valued function of the attractant signal, and plotting activity versus attractant concentration over one complete time period generates a loop. We monitor the loop area as a function of~$ω$ and find two peaks for small and large~$ω$, and a sharp minimum at intermediate~$ω$ values. We explain these results as an interplay between the time scales associated with adaptation, activity switching, and applied signal variation. In particular, for very large~$ω$, the quasi-equilibrium approximation for activity dynamics breaks down, a regime that has not been explored in earlier studies. We perform analytical calculations in this limit and find good agreement with our simulation results.

2511.07868Nov 2025

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Dynamic Vaccine Prioritization via Non-Markovian Final-state Optimization

Effective vaccine prioritization is critical for epidemic control, yet real outbreaks exhibit memory effects that inflate state space and make long-term prediction and optimization challenging. As a result, many strategies are tuned to short-term objectives and overlook how vaccinating certain individuals indirectly protects others. We develop a general age-stratified non-Markovian epidemic model that captures memory dynamics and accommodates diverse epidemic models within one framework via state aggregation. Building on this, we map non-Markovian final states to an equivalent Markovian representation, enabling real-time fast direct prediction of long-term epidemic outcomes under vaccination. Leveraging this mapping, we design a dynamic prioritization strategy that continually allocates doses to minimize the predicted long-term final epidemic burden, explicitly balancing indirect transmission blocking with the direct protection of important groups and outperforming static policies and those short-term heuristics that target only immediate direct effects. We further uncover the underlying mechanism that drives shifts in vaccine prioritization as the epidemic progresses and coverage accumulates, underscoring the importance of adaptive allocations. This study renders long-term prediction tractable in systems with memory and provides actionable guidance for optimal vaccine deployment.

2511.07200Nov 2025

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Theory of Semi-discontinuous DNA Replication

In biological cells, DNA replication is carried out by the replisome, a protein complex encompassing multiple DNA polymerases. DNA replication is semi-discontinuous: a DNA polymerase synthesizes one (leading) strand of the DNA continuously, and another polymerase synthesizes the other (lagging) strand discontinuously. Complex dynamics of the lagging-strand polymerase within the replisome result in the formation of short interim fragments, known as Okazaki fragments, and gaps between them. Although the semi-discontinuous replication is ubiquitous, a detailed characterization of it remains elusive. In this work, we develop a framework to investigate the semi-discontinuous replication by incorporating stochastic dynamics of the lagging-strand polymerase. Computing the size distribution of Okazaki fragments and gaps, we uncover the significance of the polymerase dissociation in shaping them. We apply the method to the previous experiment on the T4 bacteriophage replication system and identify the key parameters governing the polymerase dynamics. These results reveal that the collisions of lagging-strand polymerase with pre-synthesised Okazaki fragments primarily trigger its dissociation from the lagging strand.

2511.06904Nov 2025

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Beyond the Tip: Lattice Dynamics, Seams, and the Mechanism of Microtubule Fracture

The structural integrity of microtubules is paramount for cellular function. We present a theoretical analysis of their lattice fracture, focusing on the influence of multi-seam structures arising from monomer defects and aiming to provide a more accurate estimation of GDP lattice parameters. Our findings reveal that seams function as pre-existing pathways that accelerate damage propagation. Consequently, monomer vacancies destabilize the lattice due to the inherent structural loss of tubulin-tubulin contacts and the additive acceleration of fracture through multiple seams. Importantly, the comparison of our simulations with experiments on lattice fracture suggests that the intrinsic ratio of longitudinal to lateral binding energies is bounded at approximately 1.5, challenging previous predictions of lattice anisotropy from tip-growth models and highlighting the urgent need to incorporate into current growth models parameters obtained from lattice dynamics.

2511.06879Nov 2025

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Metabolic quantum limit and holographic bound to the information capacity of magnetoencephalography

Magnetoencephalography, the noninvasive measurement of magnetic fields produced by brain activity, utilizes quantum sensors like superconducting quantum interference devices or atomic magnetometers. Here we derive a fundamental, technology-independent bound on the information that such measurements can convey. Using the energy resolution limit of magnetic sensing together with the brain's metabolic power, we obtain a universal expression for the maximum information rate, which depends only on geometry, metabolism, and Planck's constant, and the numerical value of which is 2.6 Mbit/s. At the high bandwidth limit we arrive at a bound scaling linearly with the area of the current source boundary. We thus demonstrate a biophysical holographic bound for metabolically powered information conveyed by the magnetic field. For the geometry and metabolic power of the human brain the geometric bound is 6.6 Gbit/s.

2511.06401Nov 2025

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Exchange controls coarsening of surface condensates

2511.036191Nov 2025

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Information bounds the robustness of self-organized systems

Self-assembled systems, from synthetic nanostructures to developing organisms, are composed of fluctuating units capable of forming robust functional structures despite noise. In this Letter we ask: are there fundamental bounds on the robustness of self-organized nano-systems? By viewing self-organization as noisy encoding, we prove that the positional information capacity of short-range classical systems with discrete states obeys a bound reminiscent of area laws for quantum information. This universal bound can be saturated by fine-tuning transport coefficients. When long-range correlations are present, global constraints reduce the need for fine-tuning by providing effective integral feedback. Our work identifies bio-mimetic principles for the self-assembly of synthetic nanosystems and rationalizes, on purely information-theoretic grounds, why scale separation and hierarchical structures are common motifs in biology.

2511.01682Nov 2025

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