CyberSec Research

The macroscopic (population-level) dynamics of chemotactic cell movement -- arising from underlying microscopic (individual-based) models -- are often described by parabolic partial differential equations (PDEs) governing the spatio-temporal evolution of cell concentrations. In certain cases, these macroscopic PDEs can be analytically derived from microscopic models, thereby elucidating the dependence of PDE coefficients on the parameters of the underlying individual-based dynamics. However, such analytical derivations are not always feasible, particularly for more complex or nonlinear microscopic models. In these instances, neural networks offer a promising alternative for estimating the coefficients of macroscopic PDEs directly from data generated by microscopic simulations. In this work, three microscopic models of chemotaxis are investigated. The macroscopic chemotaxis sensitivity is estimated using neural networks, thereby bridging the gap between individual-level behaviours and population-level descriptions. The results are compared with macroscopic PDEs, which can be derived for each model in certain parameter regimes.

Adhesion-independent migration is a prominent mode of cell motility in confined environments, yet the physical principles that guide such movement remain incompletely understood. We present a phase-field model for simulating the motility of deformable, non-adherent cells driven by contractile surface instabilities of the cell cortex. This model couples surface and bulk hydrodynamics, accommodates large shape deformations and incorporates a diffusible contraction-generating molecule (myosin) that drives cortical flows. These capabilities enable a systematic exploration of how mechanical cues direct cell polarization and migration. We first demonstrate that spontaneous symmetry breaking of cortical activity can lead to persistent and directed movement in channels. We then investigate how various physical cues - including gradients in friction, viscosity, and channel width as well as external flows and hydrodynamic interactions between cells - steer migration. Our results show that active surface dynamics can generate stimulus-specific cell behaviors, such as migration up friction gradients or escape from narrow regions. Beyond cell migration, the model offers a versatile platform for exploring the mechanics of active surfaces in biological systems.

Accurate visualization of interventional devices, such as medical needles, is essential for the safe and effective guidance of minimally invasive procedures. Ultrasound (US) imaging is widely used for needle guidance, but the two-dimensional nature of most clinical probes limits accurate three-dimensional (3D) localization, particularly of the needle tip. We present a novel system that integrates volumetric US imaging with 3D needle tracking by combining a fiber-optic hydrophone embedded in the needle and a sparse spiral US array. Real-time volumetric imaging was achieved using plane-wave techniques, while precise needle tip tracking was enabled through communication between the probe and hydrophone. The feasibility of the approach was demonstrated using a nerve block training phantom. This proof-of-concept system enables simultaneous volumetric anatomical imaging and 3D needle tip tracking, with strong potential to enhance the efficacy and safety of image-guided interventional procedures.

We study the transport of active Brownian particles (ABPs) in three-dimensional (3D) oscillatory geometries, which are spatially periodic. We establish a generalized Fick-Jacobs approach, which reduces a 3D system to an effective 1D system based on the assumption that a fast equilibration of particles along the transversal directions of the geometry. The transport characteristics of ABPs are computed semi-analytically and corroborated by numerical simulations. At the optimal frequency of the geometry oscillation, particles exhibit higher average velocity $\langle v \rangle$ and effective diffusion coefficient $D_{\text{eff}}$, resembling the phenomena of stochastic resonance. This effect is further enhanced by the self-propelled velocity of ABPs and the amplitude of geometry oscillations. These findings have significant implications for the development of micro- and nanofluidic devices with enhanced control over particle transport and precise manipulation of small-scale biomedical devices.

Evolution has shaped animal bodies, yet to what extent biomechanical devices provide constraints and opportunities across different behaviors remains unclear. In birds, quiet breathing operates at a resonance of the respiratory biomechanics, but song, a behavior thought to be shaped by strong sexual selection, requires much higher breathing rates. Combining physiological recordings with a nonlinear biomechanical model, we show in canaries (Serinus canaria) that song production drives the system into a nonlinear regime that broadens the frequency range of amplified responses. This enhancement encompasses all syllabic rates, with an average magnification of ~94% of the theoretical maximum. Our results show that birds breathe and sing at a resonance, revealing that optimization strategies can be shared across behavioral states and extend to sexually selected displays.

Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling knowledge from a small number of high-fidelity quantum calculations into increasingly coarse-grained, machine-learned quantum Hamiltonians. By retaining explicit electronic degrees of freedom, our approach further enables a faithful embedding of quantum and classical degrees of freedom that captures long-range electrostatics and the quantum response to a classical environment to infinite order. As validation, we compute the proton dissociation constants of weak acids and the kinetic rate of an enzymatic reaction entirely from first principles, reproducing experimental measurements within chemical accuracy or their uncertainties. Our work demonstrates a path to condensed phase simulations of reaction free energies at the highest levels of accuracy with converged statistics.

The controlled assembly of solid-state spins with nanoscale spatial precision is an outstanding challenge for quantum technology. Here, we combine DNA-based patterning with nitrogen-vacancy (NV) ensemble quantum sensors in diamond to form and sense programmable 2D arrays of spins. We use DNA origami to control the spacing of chelated Gd$^{3+}$ spins, as verified by the observed linear relationship between proximal NVs' relaxation rate, $1/T_1$, and the engineered number of Gd$^{3+}$ spins per origami unit. We further show that DNA origami provides a robust way of functionalizing the diamond surface with spins as it preserves the charge state and spin coherence of proximal, shallow NV centers. Our work enables the formation and interrogation of ordered, strongly interacting spin networks with applications in quantum sensing and quantum simulation. We quantitatively discuss the prospects of entanglement-enhanced metrology and high-throughput proteomics.

Liquid mixtures can separate into phases with distinct composition. This phenomenon has recently come back to prominence due to its role in complex biological liquids, such as the cytoplasm, which contain thousands of components. For simple two-component mixtures phase-separated states are global free energy minima. However, local free energy minima, i.e. metastable states, are known to play a dominant role in complex systems with many components. For example, Hopfield neural networks can retrieve information from partial cues via relaxation to metastable states. Under what conditions can phase separated states be metastable, and what are the implications for information processing in multicomponent liquids? In this work we develop the general thermodynamic formalism of metastable phase separation. We then apply this formalism to an illustrative toy example inspired by recent experiments, binary mixtures with high-order interactions. Finally, as core application of the formalism, we study metastability in Hopfield liquids, a class of multicomponent mixtures capable of storing information on the composition of phases. We show that these phases can be retrieved from partial cues via metastable phase separation. Spatial simulations of liquids with a large number of components match our analytical solution. Our work suggests that complex biological mixtures can perform information retrieval through metastable phase separation.

In the 1970s, the renowned physicist Victor Weisskopf famously developed a research program to qualitatively explain properties of matter in terms of the fundamental constants of physics. But there was one type of matter prominently missing from Weisskopf's analysis: life. Here, we develop Weisskopf-style arguments demonstrating how the fundamental constants of physics can be used to understand the properties of living systems. By combining biophysical arguments and dimensional analysis, we show that vital properties of chemical self-replicators, such as growth yield, minimum doubling time, and minimum power consumption in dormancy, can be quantitatively estimated using fundamental physical constants. The calculations highlight how the laws of physics constrain chemistry-based life on Earth, and if it exists, elsewhere in our universe.

Chromosomal crossovers play a crucial role in meiotic cell division, as they ensure proper chromosome segregation and increase genetic variability. Experiments have consistently revealed two key observations across species: (i) the number of crossovers per chromosome is typically small, but at least one, and (ii) crossovers on the same chromosome are subject to interference, i.e., they are more separated than expected by chance. These observations can be explained by a recently proposed coarsening model, where the dynamics of droplets associated with chromosomes designate crossovers. We provide a comprehensive analysis of the coarsening model, which we also extend by including material exchanges between droplets, the synaptonemal complex, and the nucleoplasm. We derive scaling laws for the crossover count, which allows us to analyze data across species. Moreover, our model provides a coherent explanation of experimental data across mutants, including the wild-type and zyp1-mutant of A. thaliana. Consequently, the extended coarsening model provides a solid framework for investigating the underlying mechanisms of crossover placement.

Directing extracellular vesicles (EVs), such as exosomes and microvesicles, toward specific cells is an emerging focus in nanomedicine, owing to their natural role as carriers of proteins, RNAs, and drugs. EVs can be manipulated by external electric fields due to their intrinsic surface charge and biophysical properties. This study investigates the feasibility of using extremely low-frequency electromagnetic fields to guide EV transport. A theoretical framework based on the Fokker-Planck equation was developed and numerically solved to model vesicle trajectories under time-harmonic drift. Computational simulations were conducted to systematically assess the influence of key electric field parameters, including phase, frequency, and intensity, on vesicle displacement and trajectory. The findings demonstrate that frequencies below 5 Hz combined with field strengths of 200-2000 V/m can induce substantial directional control of EV motion. Moreover, enhanced directivity was achieved through the application of multi-component electric fields. Overall, this work establishes a theoretical foundation for the external-field-based beamforming of nanoparticles within the framework of molecular communication.

The formation of patterns and exotic nonequilibrium steady states in active-fluid systems continues to pose challenging problems -- theoretical, numerical, and experimental -- for statistical physicists and fluid dynamicists. We combine theoretical ideas from statistical mechanics and fluid mechanics to uncover a new type of self-assembled crystal of vortex triplets in an active-spinner fluid. We begin with the two-dimensional Cahn-Hilliard-Navier-Stokes (CHNS) model for a binary-fluid system of active rotors that has two important ingredients: a scalar order parameter field phi that distinguishes regions with clockwise (CW) and counter-clockwise (CCW) spinners; and an incompressible velocity field u. In addition to the conventional CHNS coupling between phi and u, this model has a torque-induced activity term, with coefficient tau, whose consequences we explore. We demonstrate that, if we increase the activity tau, it overcomes dissipation and this system displays a hitherto unanticipated emergent triangular crystal, with spinning vortex triplets at its vertices. We show that this is a nonequilibrium counterpart of an equilibrium plastic crystal. We characterise the statistical properties of this novel crystal and suggest possible experimental realisations of this new state of active matter.

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 first-order cumulant remains exact, whereas the second-order cumulant typically differs. We estimate the difference between two second-order binomial moments using functional analysis, therefore evaluating the validity of burst approximation.

We present a COMSOL Multiphysics implementation of a continuum model for directed cell migration, a key mechanism underlying tissue self-organization and morphogenesis. The model is formulated as a partial integro-differential equation (PIDE), combining random motility with non-local, density-dependent guidance cues to capture phenomena such as cell sorting and aggregation. Our framework supports simulations in one, two, and three dimensions, with both zero-flux and periodic boundary conditions, and can be reformulated in a Lagrangian setting to efficiently handle tissue growth and domain deformation. We demonstrate that COMSOL Multiphysics enables a flexible and accessible implementation of PIDEs, providing a generalizable platform for studying collective cell behavior and pattern formation in complex biological contexts.

Gastruloids are 3D stem cell aggregate models for early embryogenesis that provide a unique platform to study how collective cell dynamics drive tissue symmetry breaking and axial elongation. Using 3D light sheet imaging, we show that a pulse of Chiron, a Wnt activator, induces coherent alignment of cell polarity during elongation. While nuclear elongation occurs with or without treatment, only Chiron-treated gastruloids exhibit quasi-long-range alignment of nuclear axes, linking cell polarity coherence to tissue-scale remodeling. A minimal physical model of polarized cells, incorporating alignment-dependent torques and polarity-mediated adhesion, reproduces symmetry breaking and elongation, demonstrating that local cell polarity alignment alone can drive tissue-scale convergence-extension flows.

Fluorescence microscopy is essential in biological and medical research, providing critical insights into cellular structures. However, limited by optical diffraction and background noise, a substantial amount of hidden information is still unexploited. To address these challenges, we introduce a novel computational method, termed Sparse Point Optimization Theory (SPOT), which accurately localizes fluorescent emitters by solving an optimization problem. Our results demonstrate that SPOT successfully resolves 30 nm fluorescent line pairs, reveals structural details beyond the diffraction limit in both Airyscan and structured illumination microscopy, and outperforms established algorithms in single-molecule localization tasks. This generic method effectively pushes the resolution limit in the presence of noise, and holds great promise for advancing fluorescence microscopy and analysis in cell biology.

Hydra, a centimeter long cylindrical-shaped freshwater organism, has emerged as an interesting model system for studying morphogenesis in animals. Recently, fluorescent imaging of cytoskeletal actin filaments on the outer surface of hydra has revealed nematic-type arrangement of actin filaments. {Several topological defects in the nematic field have also been detected. In particular, aster-like +1 defects appear at the curved head of hydra and at the tip of its tentacles, while -1/2 defects are seen at the base of the tentacles. However, functional role of these defects in tissue development is not clear. Motivated by these observations, we here model hydra's epthelial tissue as a visco-elastic membrane and the tentacles as growing membrane tubes driven by a nematic interaction among actin. We consider the epithelial layer of hydra as a fluid membrane and carry out a non-equilibrium simulation which also includes membrane growth and polymerization of actin. We show that specific kind of defect at the head does not play any positive role in emergence of the tentacles. The reorganization of actin at the base and the tip of growing tentacles are consistent with other possible defect structures at the head as well. While it is known that regions of tentacle growth are hot spots of chemical signaling, involving Wnt3/$\beta$-catenin pathway, we propose that active polymerization of actin bundles could also be an important player in the growth of tubular tentacles. In addition to polymerization, fluidity of our model membrane, capturing effective fluidity of the epithelial tissue, turns out to be essential for enabling such growth.

In biological materials, strong binding despite an applied load force is often based on clusters of dynamic bonds that share the load. Different macroscopic behaviors have been described depending on whether the load is shared locally or globally in the force-depended unbinding rate. Here we introduce and study a model in which the load is distributed over a characteristic length scale, introduced by an exponential decay. The model contains the local and global scenario as limiting cases and smoothly interpolates between them. We derive approximations in which some analytical results can be obtained. In particular, we derive rupture conditions and validate these with stochastic simulations. The model shows two main pathways for failure of the bond cluster, due to rupture of all bonds above a critical force and due to the formation of a critical crack, a large gap between closed bonds that spreads in both directions.