CyberSec Research

The experimental use of micropatterned quasi-1D substrates has emerged as an useful experimental tool to study the nature of cell-cell interactions and gain insight on collective behaviour of cell colonies. Inspired by these experiments, we propose an active spin model to investigate the emergent properties of the cell assemblies. The lattice gas model incorporates the interplay of self-propulsion, polarity directional switching, intra-cellular attraction, and contact Inhibition Locomotion (CIL). In the absence of vacancies, which corresponds to a confluent cell packing on the substrate, the model reduces to an equilibrium spin model which can be solved exactly. In the presence of vacancies, the clustering is controlled by a dimensionless Peclet Number, Q - the ratio of magnitude of self-propulsion rate and directional switching rate of particles. In the absence of CIL interactions, we invoke a mapping to Katz-Lebowitz-Spohn(KLS) model to determine an exact analytical form of the cluster size distribution in the limit Q << 1. In the limit of Q >> 1, the cluster size distribution exhibits an universal scaling behaviour (in an approximate sense), such that the distribution function can be expressed as a scaled function of Q, particle density and CIL interaction strength. We characterize the phase behaviour of the system in terms of contour plots of average cluster size. The average cluster size exhibit a non-monotonic dependence on CIL interaction strength, attractive interaction strength, and self-propulsion.

Explaining the emergence of self-organized biodiversity and species abundance distribution patterns remians a fundamental challenge in ecology. While classical frameworks, such as neutral theory and models based on pairwise species interactions, have provided valuable insights, they often neglect higher-order interactions (HOIs), whose role in stabilizing ecological communities is increasingly recognized. Here, we extend the Generalized Lotka-Volterra framework to incorporate HOIs and demonstrate that these interactions can enhance ecosystem stability and prevent collapse. Our model exhibits a diverse range of emergent dynamics, including self-sustained oscillations, quasi-periodic (torus) trajectories, and intermittent chaos. Remarkably, it also reproduces empirical species abundance distributions observed across diverse natural communities. These results underscore the critical role of HOIs in structuring biodiversity and offer a broadly applicable theoretical framework for capturing complexity in ecological systems

How thousands of microtubules and molecular motors self-organize into spindles remains poorly understood. By combining static, nanometer-resolution, large-scale electron tomography reconstructions and dynamic, optical-resolution, polarized light microscopy, we test an active liquid crystal continuum model of mitotic spindles in human tissue culture cells. The predictions of this coarse-grained theory quantitatively agree with the experimentally measured spindle morphology and fluctuation spectra. These findings argue that local interactions and polymerization produce collective alignment, diffusive-like motion, and polar transport which govern the behaviors of the spindle's microtubule network, and provide a means to measure the spindle's material properties. This work demonstrates that a coarse-grained theory featuring measurable, physically-interpretable parameters can quantitatively describe the mechanical behavior and self-organization of human mitotic spindles.

Counting the number of isomers of a chemical molecule is one of the formative problems of graph theory. However, recent progress has been slow, and the problem has largely been ignored in modern network science. Here we provide an introduction to the mathematics of counting network structures and then use it to derive results for two new classes of molecules. In contrast to previously studied examples, these classes take additional chemical complexity into account and thus require the use of multi-variate generating functions. The results illustrate the elegance of counting theory, highlighting it as an important tool that should receive more attention in network science.

Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40 percent of studies do not report sample thickness. Among those that did report it, the vast majority, 98 percent, used 2 to 10 micrometer samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and lung). The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 micrometers) improve discrimination, though at the cost of reduced light intensity. These findings highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.

As the study of active matter has developed into one of the most rapidly growing subfields of condensed matter physics, more and more kinds of physical systems have been included in this framework. While the word 'active' is often thought of as referring to self-propelled particles, it is also applied to a large variety of other systems such as non-polar active nematics or certain particles with non-reciprocal interactions. Developing novel forms of active matter, as attempted, e.g., in the framework of quantum active matter, requires a clear idea of what active matter is. Here, we critically discuss how the understanding of active matter has changed over time, what precisely a definition of 'active matter' can look like, and to what extent it is (still) possible to define active matter in a way that covers all systems that are commonly understood as active matter while distinguishing them from other driven systems. Moreover, we discuss the definition of an 'active field theory', where 'active' is used as an attribute of a theoretical model rather than of a physical system. We show that the usage of the term 'active' requires agreement on a coarse-grained viewpoint. We discuss the meaning of 'active' both in general terms and via the specific examples of chemically driven particles, ultrasound-driven particles, active nematics, particles with non-reciprocal interactions, intracellular phase separation, and quantum active matter.

Cyanobacteria require ultra-fast metabolic switching to maintain reducing power balance during environmental fluctuations. Glucose-6-phosphate dehydrogenase (G6PDH), catalyzing the rate-limiting step of the oxidative pentose phosphate pathway (OPPP), provides essential NADPH and metabolic intermediates for biosynthetic processes and redox homeostasis. In cyanobacteria, the unique redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and resulting in precise regulation of carbon flux between anabolic and catabolic pathways. While the redox-sensitive cysteine structures of OpcA are known to regulate G6PDH, the detailed mechanisms of how redox post-translational modifications (PTMs) influence OpcA's allosteric effects on G6PDH structures and function remain elusive. To investigate this mechanism, we utilized computational modeling combined with experimental redox proteomics using Synechococcus elongatus PCC 7942 as a model system. Redox proteomics captured modified cysteine residues under light/dark or circadian shifts. Computational simulation revealed that thiol PTMs near the OpcA-G6PDH interface are crucial to allosteric regulation of regions affecting the G6PDH activity, including a potential gate region for substrate ingress and product egress, as well as critical hydrogen bond networks within the active site. These PTMs promote rapid metabolic switching by enhancing G6PDH catalytic activity when OpcA is oxidized. This study provides evidence for novel molecular mechanisms that elucidate the importance of thiol PTMs of OpcA in modulating G6PDH structure and function in an allosteric manner, demonstrating how PTM-level regulation provides a critical control mechanism that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning.

The folding and structure of biomacromolecules depend on the 3D distributions of their constituents, which ultimately controls their functionalities and interactions with other biomacromolecules. Atom probe tomography (APT) with its unparalleled compositional sensitivity at nanoscale spatial resolution, could provide complementary information to cryo-electron microscopy, yet routine APT analysis of biomacromolecules in their native state remains challenging. Here, a ferritin solution was used as a model system. Following plunge freezing in liquid nitrogen, cryogenic lift-out and cryo-APT analysis were performed. Elements from the ferritin core and shell are detected yet particles seem destroyed. We hence demonstrate the feasibility of preparing and analyzing bulk hydrated biological samples using APT, however, the cooling was too slow to vitrify the solution. This caused irrecoverable damage to the protein shell surrounding the ferritin particles due to ice crystal formation. We report on preliminary data from high-pressure frozen (HPF) deionized (DI) water, demonstrating a proof-ofprinciple experiments that intact biomacromolecules could be analyzed through a similar workflow in the future. We report on many trials (and errors) on the use of different materials for substrates and different substrate geometries, and provide a perspective on the challenges we faced to facilitate future studies across the community.

Circadian rhythms in living organisms are temporal orders emerging from biochemical circuits driven out of equilibrium. Here, we study how the rhythmicity of a biochemical clock is shaped using the KaiABC system. A phase diagram constructed as a function of KaiC and KaiA concentrations reveals a sharply bounded limit-cycle region, which naturally explains arrhythmia upon protein over-expression. Beyond the Hopf bifurcation, intrinsic noise enables regular oscillation via coherence resonance. Within the limit-cycle region, greater rhythmic precision incurs a higher energetic cost, following the thermodynamic uncertainty relation. The cost-minimizing period of the KaiABC clock ($\sim$21-hr) is close enough to entrain to 24-hr cycle of environment. Our study substantiates universal physical constraints on the robustness, precision, and efficiency of noisy biological clocks.

We introduce and study a non-equilibrium stochastic model of two fluctuating interfaces which interact through short-range attractive interactions at their points of contact. Beginning from an entangled state, the system exhibits diverse dynamics -- ranging from fast transients with small lifetimes to ultraslow evolution through quasi-stationary states -- and reaches stuck, entangled, or detached steady states. Near the stuck-detached transition, two distinct dynamical modes of evolution co-occur. When the two surfaces evolve through similar dynamics (both Edwards-Wilkinson or both Kardar-Parisi-Zhang), the invariant measure is determined and found to have an inhomogeneous product form. This exact steady state is shown to be the measure of the equilibrium Poland-Scheraga model of DNA denaturation.

The mechanical properties of arterial walls are critical for maintaining vascular function under pulsatile pressure and are closely linked to the development of cardiovascular diseases. Despite advances in imaging and elastography, comprehensive characterization of the complex mechanical behavior of arterial tissues remains challenging. Here, we present a broadband guided-wave optical coherence elastography (OCE) technique, grounded in viscoelasto-acoustic theory, for quantifying the nonlinear viscoelastic, anisotropic, and layer-specific properties of arterial walls with high spatial and temporal resolution. Our results reveal a strong stretch dependence of arterial viscoelasticity, with increasing prestress leading to a reduction in tissue viscosity. Under mechanical loading, the adventitia becomes significantly stiffer than the media, attributable to engagement of collagen fibers. Chemical degradation of collagen fibers highlighted their role in nonlinear viscoelasticity. This study demonstrates the potential of OCE as a powerful tool for detailed profiling of vascular biomechanics, with applications in basic research and future clinical diagnosis.

Molecular adhesion plays a central role in many biological systems, yet existing methods to quantify adhesive strength often struggle to bridge the gap between single-molecule resolution and biologically relevant environments. Here, we present a scalable micromagnetic bead-based adhesion assay capable of quantifying detachment forces under physiologically meaningful conditions. Designed to probe mucoadhesion in the context of mucociliary clearance, our system applies controlled magnetic forces to ligand-coated beads adhered to functionalized substrates and tracks detachment events using high-speed microscopy and calibrated z-displacement mapping. The platform combines substrate- and bead-side surface chemistry control with high-throughput imaging and in situ force calibration via Stokes drag. We demonstrate the ability to distinguish sub-nanonewton to nanonewton force regimes across a range of bead-substrate pairings, including COOH-COOH, PEG-PEG, and cell culture-derived human bronchial epithelial (HBE) mucus interactions. Surface functionalization was validated via fluorescence imaging and zeta potential measurements, while detachment forces were used to estimate binding energy and infer dissociation constants. This assay enables detailed characterization of multivalent, force-sensitive adhesive interactions and offers a powerful new approach for studying bioadhesive systems, including mucus-pathogen interactions and drug delivery materials.

The large-scale collective behavior of biological systems can be characterized by macroscopic transport, which arises from the non-equilibrium microscopic interactions among individual constituents. A prominent example is the formation of dynamic aggregates by motile eukaryotic cells or bacteria mediated by active contractile forces. In this work, we develop the two-dimensional fluctuating hydrodynamics theory based on the microscopic dynamics of a model system of aggregation by \textit{Neisseria gonorrhoeae} bacteria. The derivation of two macroscopic transport coefficients of bulk diffusivity and conductivity which determine hydrodynamic current of cells is the central result of this work. By showing how transport coefficients depend on cell density and microscopic parameters of the system we predict transport slowdown during the colony formation process. This study provides valuable analytical tools for quantifying hydrodynamic transport in experimental systems involving cellular aggregation occurring due to intermittent contractile dipole forces.

Ribosome-targeting antibiotics, such as chloramphenicol, stall elongating ribosomes during protein synthesis, disrupting mRNA translation. These antibiotic-induced pauses occur stochastically, alter collective ribosome dynamics and transiently block protein production on the affected transcript. Existing models of ribosome traffic often rely on idealized assumptions, such as infinitely long mRNAs and simplified pausing dynamics, overlooking key biological constraints. Here, we develop a Totally Asymmetric Simple Exclusion Process (TASEP) that incorporates stochastic particle pausing, using experimentally determined pausing and unpausing rates to model the effects of ribosome-targeting antibiotics. We introduce a Single-Cluster approximation, which is analytically treatable, tailored to capture the biologically relevant regime of rare and long antibiotic-induced pauses. This biologically constrained model reveals three key insights: (i) the inhibition of antibiotic-induced translation strongly depends on transcript length, with longer transcripts being disproportionately affected; (ii) reducing ribosome initiation rates significantly mitigates antibiotic vulnerability; and (iii) inhibition of translation is governed more by collective ribosome dynamics than by single-ribosome properties. Our analytical predictions match Gillespie simulations, align quantitatively with experimental observations, and yield testable hypotheses for future experiments. These findings may have broader implications for the mechanistic modeling of other biological transport processes (e.g., RNAP dynamics), and more generally for the community studying traffic models.

Cells are constantly exposed to diverse stimuli-chemical, mechanical, or electrical-that guide their movement. In physiological conditions, these signals often overlap, as seen during infections, where neutrophils and dendritic cells navigate through multiple chemotactic fields. How cells integrate and prioritize competing signals remains unclear. For instance, in the presence of opposing chemoattractant gradients, how do cells decide which direction to go? When should local signals dominate distant ones? A key factor in these processes is the precision with which cells sense each gradient, which depends non-monotonically on concentrations. Here, we study how gradient sensing accuracy shapes cell navigation in the presence of two distinct chemoattractant sources. We model cells as active random walkers that sense local gradients and combine these estimates to reorient their movement. Our results show that cells sensing multiple gradients can display a range of chemotactic behaviors, including anisotropic spatial patterns and varying degrees of confinement, depending on gradient shape and source location. The model also predicts cases where cells exhibit multistep navigation across sources or a hierarchical response toward one source, driven by disparities in their sensitivity to each chemoattractant. These findings highlight the role of gradient sensing in shaping spatial organization and navigation strategies in multi-field chemotaxis.

There is currently a growing interest in understanding the origins of intrinsic fluorescence as a way to design non-invasive probes for biophysical processes. In this regard, understanding how pH influences fluorescence in non-aromatic biomolecular assemblies is key to controlling their optical properties in realistic cellular conditions. Here, we combine experiments and theory to investigate the pH-dependent emission of solid-state L-Lysine (Lys). Lys aggregates prepared at different pH values using HCl and H$_2$SO$_4$ exhibit protonation- and counterion-dependent morphology and fluorescence, as shown by microscopy and steady-state measurements. We find an enhancement in the fluorescence moving from acidic to basic conditions. To uncover the molecular origin of these trends, we performed non-adiabatic molecular dynamics simulations on three Lys crystal models representing distinct protonation states. Our simulations indicate that enhanced protonation under acidic conditions facilitates non-radiative decay via proton transfer, whereas basic conditions favor radiative decay. These findings highlight pH as a key factor tuning fluorescence in Lys assemblies, offering insights for designing pH responsive optical materials based on non-aromatic amino acids.

This tutorial covers the emerging field of coarse-grained cellular growth modeling, and aims to bridge the gap between theoretical foundations and practical application. By adopting an original "cookbook" approach, it is designed to offer a hands-on guide for constructing and analyzing different key aspects of cellular growth, focusing on available results for bacteria and beyond. The tutorial is structured as a series of step-by-step "recipes", and covers essential concepts, recent literature, and key challenges. It aims to empower a broad audience, from students to seasoned researchers, to replicate, extend, and innovate in this scientific area. Specifically, each section provides detailed, bare-bone models to start working in each area, from basic steady-state growth to variable environments and focusing on different key layers relevant to biosynthesis, transcription, translation, nutrient sensing and protein degradation, links between cell cycle and growth, ending with ecological insights.

The cell body of flagellated microalgae is commonly considered to act merely as a passive load during swimming, and a larger body size would simply reduce the speed. In this work, we use numerical simulations based on a boundary element method to investigate the effect of body-flagella hydrodynamic interactions (HIs) on the swimming performance of the biflagellate, \textit{C. reinhardtii}. We find that body-flagella HIs significantly enhance the swimming speed and efficiency. As the body size increases, the competition between the enhanced HIs and the increased viscous drag leads to an optimal body size for swimming. Based on the simplified three-sphere model, we further demonstrate that the enhancement by body-flagella HIs arises from an effective nonreciprocity: the body affects the flagella more strongly during the power stroke, while the flagella affect the body more strongly during the recovery stroke. Our results have implications for both microalgal swimming and laboratory designs of biohybrid microrobots.