Frontiers in Physics

This work focuses on the global and partial identification problem for fractional differential equations. We provide a general numerical procedure based on global and local optimization algorithms with two refinements for biological systems that ensure solution positivity and homogeneous parameter units. The method is applied to a new fractional model of Dengue outbreak called the Fractional Homogeneous Nishiura (FHN) model, calibrated using data of newly infected people in Cape Verde. We show that our identification method yields a better fit between data and model solutions than previous approaches and that our FHN model captures the dynamics of Dengue more closely than existing systems.

Cardiovascular diseases, such as stroke and heart attacks, are the leading cause of death worldwide. Computational models like cardiovascular digital twins (CVDTs) offer a promising path for research and intervention but are challenged by the complexity of simulating the full human vasculature. This study evaluates the feasibility of simulating blood flow through a vascular network containing 34 billion vessels (the estimated number in the human body) using first-principles physics and simplified geometry which is a first step towards CVDT. We synthesized 3D vasculature using a fractal model and computed blood flow rates via Poiseuille equation and steady-state fluid dynamics, implemented with high-performance computing. Simulations were conducted for networks ranging from 6 vessels to 34 billion vessels. The results demonstrated high accuracy (within 1% of bench-marks), reproducibility across platforms, and strong scalability. Simulating the full vasculature required 156 node-hours on the second-fastest supercomputer in the world, using 29 TB of memory and 84 TFLOPS. Maximum speedup factor was 80, with parallel efficiency no lower than 0.48. These findings show it is computationally feasible to simulate blood flow through a full-body vascular network at scale. The approach is well suited to parallel computing, suggesting that with continued development, CVDTs could enable whole-organism modeling for applications such as stroke, trauma, radiation injury, and cancer metastasis.

Mechanical properties of epithelial tissues play essential roles in morphogenesis and physiological function. In this study, we analytically derived the in-plane bulk modulus, shear modulus, and Poissons ratio of a three-dimensional cell vertex model of epithelial monolayers. We showed that the model can robustly reproduce a near-zero in-plane Poissons ratio, a mechanical feature reported in cultured epithelial tissues. Numerical simulations further confirmed that the theoretically predicted Poissons ratio accurately describes the response of the model under finite, biologically relevant strains. In addition, the model exhibits not only morphological bistability between squamous-like and columnar-like states, but also mechanical bistability characterized by distinct elastic responses. Together, these results provide a minimal three-dimensional framework that links cell-scale mechanical interactions and epithelial morphology to tissue-scale elastic properties.

Biomolecular condensates that form via liquid-liquid phase separation (LLPS) of, most prominently, intrinsically disordered proteins (IDPs) are ubiquitous in eukaryotic cells and responsible for regulating a plethora of biological functions. Amongst these, they contribute to regulating cell motility, either individually within an extracellular matrix or collectively within confluent epithelial tissue. In this computational study we focus on the latter with the aim of investigating whether the mutual exertion of mechanical forces during collective migration in an epithelium can principally trigger cytoplasmatic LLPS. Since present models for confluent epithelial motility have so far only considered cells that are devoid of phase separating (protein) solutes, we extend a common multiphase approach for 2D cell motility with a mixing contribution including any number of protein solutes. Our model considers the phase behavior in both intracellular and extracellular regions and determines to what extend the membrane is permeated by the solutes under the influence of mechanical and osmotic forces. Our initial calculations unlock a very rich behavior involving formation and dissolution of condensates during migration, as well as an impact of LLPS on the very nature of the motility itself, through feedback mechanisms which may bear biological relevance.

The flying fox bats roost in large colonies, suspended upside-down with minimal grip efforts from tree branches that are exposed to environmental disturbances. In this study, we investigate the oscillation dynamics of bats hanging from tree branches under natural conditions with wind. Bats modulate their grips to control the oscillation during wind disturbances and actively transform their postures. Using field observations, we analyze the angular deformation, speed, and phase of individual and collective bats swaying motions in response to environmental perturbations. We observed the mechanical coupling-based synchronization of collective bat oscillations on a tree branch. To rationalize this new phenomenon of bats synchronization behavior, we perform a table-top experiment of a physical model using active oscillators and passive systems. This work could inform the design of bio-inspired suspension systems and contribute to our understanding of animal balance and collective behavior in unsteady and complex environments.

Spiral geometries commonly occur in natural and engineered systems and are fundamentally described by curvature and torsion. In deformation-dominated systems, these variables evolve dynamically, requiring a continuum mechanical framework to link geometry and deformation. This study focused on refractile bodies (R-bodies), protein supramolecular assemblies that undergo reversible roll-spiral transformations in response to stimuli such as pH changes. Although multiple R-body types with distinct morphologies and unrolling behaviours were experimentally identified, their deformation mechanisms lack quantitative theoretical descriptions. We proposed a deformation-gradient-tensor-based continuum model incorporating geometrical mapping from the rolled to spiral state within a unified framework. The model successfully reconstructed macroscopic deformation behaviours of types 51, 7, and Pa R-bodies by capturing differences in unrolling behaviours, tapered geometry, and spatio-temporal evolution. The analysis revealed that deformation proceeds through a coupled process in which the curvature decreases via straightening, while the torsion increases by twisting. Importantly, the framework connected the macroscopic morphology with microscopic lattice deformation, enabling quantitative inference of lattice intervals and angles. The proposed comprehensive geometric model of the R-body roll-spiral transformation offers a general mathematical foundation for understanding deformation-driven spiral transformations in soft matter systems.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

A major challenge in neuroscience is to predict how currents in nanodomains affect voltage and ionic concentrations. Cable and Rall theory provide analytic current-voltage relations by neglecting concentration gradients, and the impact of concentration gradients is usually studied numerically with the Poisson-Nernst-Planck (PNP) model. A precise quantitative understanding of the combined dynamics remains limited because analytic current-voltage-concentration relations are missing. In this work we derive such relations using a novel approach based on cross-diffusion equations. For narrow cylindrical domains, we derive time-dependent and steady-state expressions that explicitly show how currents affect voltage and ionic concentrations. We find that the influx of only one ion can significantly change the concentrations of all the other ions even if no channels for these ions are present. After a current injection we compute a biphasic voltage transient where the small-time asymptotic corresponds to the steady-state solution of the cable equation. We show that the accuracy of cable theory prediction for the voltage depends on how the current is distributed among the various ions. Finally, we develop an iterative method to accurately compute steady-state profiles for voltage and concentrations using first-order results by subdividing a cylinder into small segments.

We find that multi-site temporal control of optogenetic photostimulation in peripheral nerves can enhance firing rates by overcoming the intrinsic limitation of opsin photophysics. The benefits of multi-site optogenetic stimulation were demonstrated with three approaches: (1) in silico modeling, (2) ex vivo in the sciatic nerve, and (3) in vivo in the vagus nerve. An in silico model of multi-site optogenetic stimulation was developed in two Hodgkin and Huxley type neuron models, that supported our hypothesis. The ex vivo sciatic nerve showed an increase in firing frequency that is physiologically relevant for functional control. The technique was then applied in vivo for optogenetic vagus nerve stimulation resulting in significant changes in heart rate compared with standard methods of single-site stimulation. Improving the control of optogenetically induced neural firing will have broad impacts for future developments in optical nerve interfaces and brain-machine interfaces.

Bridging molecular and emergent properties is essential for designing soft matter. Synthetic DNA materials are attractive in this context because their sequence design space supports a wide range of material properties. Targeted design of DNA materials is hindered by scale differences and manual exploration of vast design spaces. We address this challenge with a computational workflow that links sequence-level design to rheological material properties. Concretely, we use machine learning to parametrise scalable, DNA-sequence-aware simulations, which we then evaluate using graph-based rheology. In our example, we study materials composed of self-interacting, multivalent DNA nanostars assembled from single strands. Structure and flexibility of nanostars are quantified with nucleotide-level oxDNA simulations, enabling Bayesian optimisation of a more coarse-grained bead-spring model. The bead-spring model allows efficient simulation of network formation between nanostars, governed by hybridisation free energies, which are computed with oxDNA and NUPACK. Nanostar valency and network connectivity are translated into rheological material properties with a graph-based method that we extend to include hydrodynamic interactions, yielding good agreement with experimental reference data. We generalise our findings by analysing theoretical graph representations of DNA materials and show how machine learning can optimise sequence affinities to produce desired rheological responses. Our work illustrates how machine learning can bridge scales and automate coarse-graining to facilitate targeted design of DNA materials through sequence-property relationships. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/728076v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@20b8c6org.highwire.dtl.DTLVardef@42f843org.highwire.dtl.DTLVardef@b90119org.highwire.dtl.DTLVardef@1f72d66_HPS_FORMAT_FIGEXP M_FIG C_FIG

Arterial thrombosis is initiated when mechanical forces in flowing blood exceed the activation thresholds of platelets and von Willebrand factor (vWF). Despite extensive experimental characterization of shear-induced platelet aggregation, a unified theoretical framework that maps hemodynamic forcing onto clot nucleation is lacking. Here we present Force-Gated Thrombosis (FGT), a non-equilibrium mechanical theory that treats thrombus formation as a continuous phase transition driven by an effective mechanical forcing {Sigma} ={sigma} + |{nabla}{sigma}| + {beta}{varepsilon}, which combines local wall shear stress{sigma} , shear gradient |{nabla}{sigma}|, and extensional strain rate{varepsilon} . We introduce a dimensionless Thrombosis Number {Theta} = ({Sigma}/{Sigma}c)(P/P0)m(C/C0)n, which incorporates platelet concentration P and coagulation factor concentration C, and governs the transition between stable flow ({Theta} < 1) and active clot growth ({Theta} > 1). The thrombus density is represented by a scalar order parameter{varphi} whose dynamics follow a Ginzburg- Landau free energy functional. For a simplified stenosed artery we derive an analytic closed-form thrombosis onset criterion and a critical flow rate [Formula], where{delta} is stenosis severity. Linear stability analysis shows that perturbations grow at rate{omega} (k) = {Lambda}({Theta}) - D{varphi}k2, becoming unstable when {Theta} > 1. Near threshold the clot volume fraction scales as{varphi} [~] ({Theta} - 1)1/2, a mean-field critical exponent consistent with Ginzburg- Landau theory. Systematic comparison with fifteen published experimental and computational datasets spanning shear rates from 100 to 15,000 s-1 confirms that FGT correctly predicts the existence, location, and approximate severity of pathological thrombus formation across diverse vascular geometries. The theory provides a quantitative bridge between single-molecule mechanobiology and macroscale clinical thrombosis, and yields experimentally testable predictions distinguishing FGT from purely biochemical models.

The aorta shows significant regional variation in geometry and composition. This complexity makes numerical modeling challenging, as it requires identifying material parameters. Typically, the Holzapfel-Gasser-Ogden model is used. However, it suffers from nonuniqueness and sensitivity to outliers, which can obscure biological variation. In addition, standard compressible formulations with a volumetric-isochoric split fail to couple volumetric and anisotropic responses. To address these issues, a regularized dual-estimation framework was introduced. This framework combines a global baseline estimator with local refinement while maintaining structural material continuity. Furthermore, it uses a Modified Anisotropic model to improve the representation of compressibility physics. For validation, the approach included uniaxial extension and protein quantification from Wistar rats. The results show that the proximal ascending/aortic-arch segment is most compliant at low stretch, whereas the abdominal aorta stiffens earlier and becomes fiber-dominated at lower stretch levels. Notably, these trends align directionally with regional composition. However, the fitted stress components are model-based descriptors rather than direct measurements of individual constituents.

Flow and extensional deformation of mucin networks are fundamental in mucus biophysics, governing how mucus functions as a protective and lubricating, and transport-facilitating layer. While the shear and oscillatory rheology of mucin solutions have been characterized in considerable detail, their behavior under extensional deformation remains comparatively understudied. Here, we report a concentration-dependent transition in extensional flow response of mucin solutions using a bespoke dripping-onto-substrate extensional rheometer. We show that mucin solutions at the lower concentrations undergo linear filament thinning, whereas semidilute mucin solutions form highly extensible filaments, with radius decaying exponentially in time, consistent with the elastocapillary thinning observed in solutions of high molecular weight synthetic polymers. Remarkably, at higher mucin concentrations inter-chain mucin associations produce a sudden reduction in the apparent elastocapillary relaxation time. We demonstrate how increasing macromolecular concentration redistributes the balance between viscous and elastic stresses during capillary thinning in a biopolymer network and reveal a concentration-driven reduction in mucin filament extensibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/725541v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1f593acorg.highwire.dtl.DTLVardef@1b23686org.highwire.dtl.DTLVardef@119add3org.highwire.dtl.DTLVardef@e31908_HPS_FORMAT_FIGEXP M_FIG C_FIG

Targeted drug delivery offers a promising approach for personalized medicine in treating vascular stenosis. However, biomechanical constraints, such as drug washout by high-velocity central blood flow and unintended absorption by healthy vascular walls, complicate the determination of optimal dosing locations. Conventional three-dimensional computational fluid dynamics (CFD) provides precise flow analysis but incurs prohibitive computational costs, making long-term tracking of plaque growth and reverse-engineering of optimal delivery highly inefficient. In this study, we propose a pseudo-3D stochastic growth model that dramatically reduces computational load while capturing the essential dynamics of plaque progression and regression. By modeling the advection-diffusion of lipid and drug particles as a discrete Markov process within a Stokes flow field, we simulate the morphological evolution of plaques under continuous and interrupted targeted therapies. Furthermore, by formulating the drug transport process as an absorbing Markov chain with boundaries at the healthy walls and vessel outlet, we calculate the exact reaching probability and mean first passage time (MFPT) to the plaque. Based on these probability distributions, we discover continuous "Optimal Dosing Curves", which indicate the most effective spatial coordinates for catheter-based drug release to maximize therapeutic efficacy. This mathematical framework not only elucidates the stochastic nature of vascular plaque dynamics but also provides a scalable, computationally efficient foundation for optimizing targeted drug delivery in personalized medicine.

An ongoing outbreak of Bundibugyo virus disease (BVD) in the Democratic Republic of the Congo was deemed a public health emergency of international concern in May 2026. To prevent cross-border importation, many countries, including the United States, Canada, India, Thailand, and Kenya have already proposed containment strategies, and others are likely to follow suit. How well (or poorly) are screening and quarantine containment measures are likely to work? We leverage established epidemiological theory and develop a mathematical model of traveler screening and post-arrival quarantine for BVD to answer this question. We find that traveler screening via symptom screening or molecular testing will miss the majority of infected travelers, and should be complemented by post-arrival quarantine and monitoring of sufficient duration to detect those with long incubation periods. Our findings underscore the limitations of border screening and the importance of complementary measures like post-arrival quarantine to prevent local importation of BVD.

Linear state-space models have been shown to effectively reproduce large-scale brain dynamics. We applied this approach to resting-state fMRI data acquired from 20 mice, focusing on the systems Jacobian matrix, i.e. the effective connectivity, and specifically on its component encoding nonzero-lag interactions: the differential covariance matrix. Within this matrix, we concentrated on the off-diagonal component (dC-Cov), which reflect endogenous time-lagged correlations. Our aim was to identify a decomposition of the Jacobian matrix that facilitates its interpretation from a mechanistic perspective. Since the dC-Cov captures the rotational component of signal trajectories, we employed Schur decomposition to extract 2D rotational modes, each characterized by a pair of orthogonal vectors, and an associated angular frequency. This provides a more generative formulation of the modeling framework, thereby reducing the interpretability gap between this approach and connectome-based network models of coupled neural masses. Within this framework, the precision matrix governs the coupling between different Schur modes, while we hypothesize that the dC-Cov reflects spatial constraints imposed by inter-regional distances. By examining the relationship between dC-Cov and structural constraints imposed by the spatial placement of brain areas, we found a consistent alignment between the faster Schur modes across mice and the leading eigenvectors of the structural distance matrix.

Organ-on-chip (OoC) systems enable the recapitulation of key structural and functional characteristics of human tissues within controlled micro-engineered environments. In mechanically active tissues such as musculoskeletal, cardiac, and vascular systems, the incorporation of dynamic physical forces is essential for replicating the biomechanical cues governing cellular morphology and functional responses in-vivo. Without such stimuli, OoC models may fail to capture physiologically relevant tissue behaviors. Porous and semi-permeable membranes are critical components of OoCs, facilitating selective transport of nutrients, gases, and signaling molecules between cellular compartments to support biologically accurate barrier replication. Hence, fabrication strategies that permit precise modulation of membrane permeability are desirable to accommodate for the varying needs in pore size and porosity across organ systems. This study presents a two-stage fabrication process for stretchable, microporous polydimethylsiloxane (PDMS) membranes using femtosecond (fs-) pulse laser drilling. The laser-ablated pores exhibit a characteristic conical morphology, with diameters tapering from the laser entry to exit point. By modulating laser power and number of pulses, 6-15 m exit-end pore diameters were achieved in 50 m thick PDMS films. The membranes demonstrated strong mechanical resilience, with a 5-12% reduction in Youngs modulus after 500 cycles of strain loading. Furthermore, membranes fabricated at lower laser powers exhibited superior retention of elasticity, highlighting the influence of processing parameters on mechanical behavior. Cytocompatibility and permeability assessments confirmed that the membranes supported sustained cell viability and proliferation over at least three days. In size-restricted membrane pore geometries, cellular migration was constrained without any inhibition of biomolecular transport. This selective permeability is critical in multilayer OoC architectures, where a balance between biomolecular diffusion and cellular compartmentalization is necessary to preserve distinct tissue interfaces and functional organization. This work presents fs-laser micro-drilling as a robust and tunable fabrication strategy for producing mechanically resilient, selectively permeable PDMS membranes for physiologically relevant OoC applications.

Biomolecular condensates are thought to play a pivotal role in cellular organization by regulating biochemical reactants in space and time. Sustained molecular fluxes across condensate boundaries, together with the participation of phase-separating molecules in active chemical reactions such as ATP hydrolysis, call for a nonequilibrium description. Here, we propose a self-consistent framework in which diffusion-drift dynamics and chemical reactions are coupled through a conditional free energy, defined as the excess contribution to the chemical potential. Self-consistency is achieved by deriving this quantity from the same free-energy functional that governs molecular interactions and phase separation. We apply the framework to a minimal client-scaffold system and investigate how active chemical processes and phase separation interact at steady state. In doing so, our approach recovers the fundamental rules previously identified for the emergence of nonequilibrium steady-state fluxes. The model shows that active reactions involving the scaffold molecules can regulate the phase behavior of the condensate. Moreover, nonequilibrium steady-state fluxes are maximal near the boundary between the phase-separated and homogeneous regimes, suggesting that condensates sustaining molecular transport may operate close to their stability threshold. In the same region, client fluxes are also enhanced, revealing an indirect coupling between scaffold activity and client transport. These results provide a baseline for developing more detailed theories of chemically active condensates.

Advances in single-cell RNA sequencing (scRNA-seq) and high-resolution imaging techniques, such as single-molecule tracking (SMT) of RNA and transcription factors, allow researchers to quantitatively explore dynamics and variation but have never been integrated into a single coherent model. In this study, we propose a kinetic model that intakes multiple data types, including steady-state and time-resolved datasets, to simulate and fit stochastic models of gene transcription to experimental data. We find that 3-state models provide an essential improvement over the widely used 2-state model for most genes and have the property of kinetic proofreading, which we argue is advantageous in the cellular context. We further identify two dimensionless quantities derived from the rate equations which are broadly conserved across genes. Finally, we extend this model to scRNA-seq datasets to infer kinetic rates under defined perturbations and reveal biochemical insight into the mechanism of action of transcription factors.

High bit-depth medical images preserve subtle intensity variations that are important for quantitative analysis and clinical interpretation, but their large dynamic range poses challenges for efficient compression. We propose a bit-plane-aware dual-stream compression framework for 16-bit medical images by separately modeling the most significant bit (MSB) and least significant bit (LSB) components. The MSB structural stream is encoded using JPEG coding with a Duplicate Segment Skipping (DSS) strategy to exploit spatial and segment-level redundancy, while the LSB detail stream is compressed using learned image compression to represent residual variations and fine-grained details. Experiments on four MRI and CT datasets show that the proposed method consistently outperforms representative traditional and learning-based codecs, achieving the lowest bit rate across all datasets. Meanwhile, it preserves high reconstruction fidelity. As a downstream application, we further demonstrate that the compressed bitstreams can be effectively integrated with DNA encoding and converted into sequences with favorable biochemical properties.