CyberSec Research

Biofilms in porous media critically influence hydraulic properties in environmental and engineered systems. However, a mechanistic understanding of how microbial life controls permeability remains elusive. By combining microfluidics, controlled pressure gradient and time-lapse microscopy, we quantify how motile and non-motile bacteria colonize a porous landscape and alter its resistance to flow. We find that while both strains achieve nearly identical total biomass, they cause drastically different permeability reductions - 78% for motile cells versus 94% for non-motile cells. This divergence stems from motility, which limits biomass spatial accumulation, whereas non-motile cells clog the entire system. We develop a mechanistic model that accurately predicts permeability dynamics from the pore-scale biomass distribution. We conclude that the spatial organization of biomass, not its total amount, is the primary factor controlling permeability.

The remarkable progress of artificial intelligence (AI) has revealed the enormous energy demands of modern digital architectures, raising deep concerns about sustainability. In stark contrast, the human brain operates efficiently on only ~20 watts, and individual cells process gigabit-scale genetic information using energy on the order of trillionths of a watt. Under the same energy budget, a general-purpose digital processor can perform only a few simple operations per second. This striking disparity suggests that biological systems follow algorithms fundamentally distinct from conventional computation. The framework of information thermodynamics-especially Maxwell's demon and the Szilard engine-offers a theoretical clue, setting the lower bound of energy required for information processing. However, digital processors exceed this limit by about six orders of magnitude. Recent single-molecule studies have revealed that biological molecular motors convert Brownian motion into mechanical work, realizing a "demon-like" operational principle. These findings suggest that living systems have already implemented an ultra-efficient information-energy conversion mechanism that transcends digital computation. Here, we experimentally establish a quantitative correspondence between positional information (bits) and mechanical work, demonstrating that molecular machines selectively exploit rare but functional fluctuations arising from Brownian motion to achieve ATP-level energy efficiency. This integration of information, energy, and timescale indicates that life realizes a Maxwell's demon-like mechanism for energy-efficient information processing.

Many protein-protein interaction (PPI) networks take place in the fluid yet structured plasma membrane. Lipid domains, sometimes termed rafts, have been implicated in the functioning of various membrane-bound signaling processes. Here, we present a model and a Monte Carlo simulation framework to investigate how changes in the domain size that arise from perturbations to membrane criticality can lead to changes in the rate of interactions among components, leading to altered outcomes. For simple PPI networks, we show that the activity can be highly sensitive to thermodynamic parameters near the critical point of the membrane phase transition. When protein-protein interactions change the partitioning of some components, our system sometimes forms out of equilibrium domains we term pockets, driven by a mixture of thermodynamic interactions and kinetic sorting. More generally, we predict that near the critical point many different PPI networks will have their outcomes depend sensitively on perturbations that influence critical behavior.

Radiation chemistry of model systems irradiated with ultra-high dose-rates (UHDR) is key to obtain a mechanistic understanding of the sparing of healthy tissue, which is called the FLASH effect. It is envisioned to be used for efficient treatment of cancer by FLASH radiotherapy. However, it seems that even the most simple model systems, water irradiated with varying dose-rates (DR), pose a challenge. This became evident, as differences within measured and predicted hydrogen peroxide (H2O2) yields (g-values) for exposure of liquid samples to conventional DR and UHDR were reported. Many of the recently reported values contradict older experiments and current Monte-Carlo simulations(MCS). In the present work, we aim to identify possible reasons of these discrepancies and propose ways to overcome this issue. Hereby a short review of recent and classical literature concerning experimental and simulational studies is performed. The studies cover different radiation sources, from gamma rays, high-energy electrons, heavy particles (protons and ions) with low and high linear energy transfer (LET), and samples of hypoxic & oxygenated water, with cosolutes such as bovine-serum albumine (BSA). Results are for additional experimental parameters, such as solvent, sample container and analysis methods used to determine the respective g-values of H2O2. Similarly the parameter of the MCS by the step-by-step approach, or the independent-reaction time (IRT) method are discussed. Here, UHDR induced modification of the radical-radical interaction and dynamics, not governed by diffusion processes, may cause problems. Approaches to test these different models are highlighted to allow progress: by making the step from a purely descriptive discourse of the effects observed, towards testable models, which should clarify the reasons of how and why such a disagreement came to light in the first place.

We investigate how an active bath of enzymes influences the liquid-liquid phase separation (LLPS) of a non-interacting condensing protein. The enzyme we choose to use as the active driver is urease, an enzyme that has been shown by several groups to exhibit enhanced diffusion in the presence of its substrate. The non-interacting LLPS protein is ubiquilin-2, a protein that condenses with increasing temperature and salt. Using a microfluidic device with semipermeable membranes, we create a chemostatic environment to maintain the substrate content to feed the enzymatic bath and remove the products of the chemical reaction. Thus, we isolate the physical enhanced fluctuations from the chemical changes of the enzyme activity. We also compare the results to controls without activity or in the presence of the products of the reaction. We find that the active bath is able to enhance droplet size, density, and concentration, implying that more ubiquilin-2 is in condensed form. This result is consistent with an interpretation that the active bath acts as an effective temperature. Simulations provide an underlying interpretation for our experimental results. Together, these findings provide the first demonstration that physical enzymatic activity can act as an effective temperature to modify LLPS behavior, with implications for intracellular organization in the enzymatically active cellular environment.

Fluorescence-based single-molecule localization, transport, and sensing require spatial confinement to extend the molecule's residence time during imaging, sufficient temporal resolution to capture fast dynamics, and efficient fluorescence background suppression. Two-dimensional (2D) materials offer large-area, atomically flat surfaces suitable for massively parallel in-plane biomolecule imaging, yet achieving guided motion in one-dimensional confinements using top-down nanofabrication remains challenging. Here, we demonstrate that thermally induced wrinkles in exfoliated hexagonal boron nitride (hBN) act as self-assembled nanochannels that enable biomolecule confinement and imaging under wide-field fluorescence microscopy. By controlling annealing parameters and substrate properties, we obtain scalable and reproducible wrinkle networks whose densities and morphologies can be tuned. Structural characterization using atomic force and scanning electron microscopy is complemented by fluorescence imaging and Kelvin probe force microscopy, confirming that aqueous solutions fill and remain stably retained within the nanochannels for periods exceeding 10 hours. We further achieve selective ATTO647N-DNA localization and imaging in the one-dimensional channels through the formation of a graphene/hBN vertical heterostructure. The graphene overlayer serves as a quenching mask that suppresses background fluorescence both from high-strain hBN regions and from DNA adsorbed on top of the 2D layer. Overall, these results provide a scalable, lithography-free route for creating planar nanofluidic confinements fully compatible with single-molecule imaging. This platform enables fundamental nanobiology studies as well as on-chip biomolecule transport and sensing applications.

The protective capsid encasing the genetic material of Human Immunodeficiency Virus (HIV) has been shown to traverse the nuclear pore complex (NPC) intact, despite exceeding the passive diffusion threshold by over three orders of magnitude. This remarkable feat is attributed to the properties of the capsid surface, which confer solubility within the NPC's phase-separated, condensate-like barrier. In this context, we apply the classical framework of wetting and capillarity -- integrating analytical methods with sharp- and diffuse-interface numerical simulations -- to elucidate the physical underpinnings of HIV nuclear entry. Our analysis captures several key phenomena: the reorientation of incoming capsids due to torques arising from asymmetric capillary forces; the role of confinement in limiting capsid penetration depths; the classification of translocation mechanics according to changes in topology and interfacial area; and the influence of (spontaneous) rotational symmetry-breaking on energetics. These effects are all shown to depend critically on capsid geometry, arguing for a physical basis for HIV's characteristic capsid shape.

Molecular dynamics simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm-1 which compares with 20 cm-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both, the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments.

Slicks are thin viscous films that can be found at the air--water interface of water bodies such as lakes, rivers and oceans. These micro-layers are enriched in surfactants, organic matter, and microorganisms, and exhibit steep physical and chemical gradients across only tens to hundreds of micrometers. In such geometrically confined environments, the hydrodynamics and transport of nutrients, pollutants, and microorganisms are constrained, yet they collectively sustain key biogenic processes. It remains however largely unexplored how the hydrodynamic flows and transport are affected by the vertical extent of slicks relative to the size of microbial colonies. Here, we study this question by combining analytical and numerical approaches to model a microbial colony as an active carpet: a two-dimensional distribution of micro-swimmers exerting dipolar forces. We show that there exists a ratio between the carpet size and the confinement height that is optimal for the enhancement of particle transport toward the colony edges through advective flows that recirculate in 3D vortex-ring-like patterns with a characteristic length comparable to the confinement height. Our results demonstrate that finite, coherent vortex-ring-like structures can arise solely from the geometrical confinement ratio of slick thickness to microbial colony size. These findings shed light on the interplay between collective activity and out-of-equilibrium transport, and on how microbial communities form, spread, and persist in geometrically constrained environments such as surface slicks.

The stereotypical reaching motion of the octopus arm has drawn growing attention for its efficient control of a highly deformable body. Previous studies suggest that its characteristic bend propagation may share underlying principles with the dynamics of a whip. This work investigates whether whip-like passive dynamics in water can reproduce the kinematic features observed in biological reaching and their similarities and differences. Platform-based whipping tests were performed in water and air while systematically varying material stiffness and driving speed. Image-based quantification revealed that the Ecoflex Gel 2 arm driven at 150 rpm (motor speed) reproduced curvature propagation similar to that observed in octopus reaching. However, its bend-point velocity decreased monotonically rather than exhibiting the biological bell-shaped profile, confirming that the octopus reaching movement is not merely a passive whipping behavior. The absence of propagation in air further highlights the critical role of the surrounding medium in forming octopus-like reaching motion. This study provides a new perspective for understand biological reaching movement, and offers a potential platform for future hydrodynamic research.

Molecular transitions -- such as protein folding, allostery, and membrane transport -- are central to biology yet remain notoriously difficult to simulate. Their intrinsic rarity pushes them beyond reach of standard molecular dynamics, while enhanced-sampling methods are costly and often depend on arbitrary variables that bias outcomes. We introduce Gen-COMPAS, a generative committor-guided path sampling framework that reconstructs transition pathways without predefined variables and at a fraction of the cost. Gen-COMPAS couples a generative diffusion model, which produces physically realistic intermediates, with committor-based filtering to pinpoint transition states. Short unbiased simulations from these intermediates rapidly yield full transition-path ensembles that converge within nanoseconds, where conventional methods require orders of magnitude more sampling. Applied to systems from a miniprotein to a ribose-binding protein to a mitochondrial carrier, Gen-COMPAS retrieves committors, transition states, and free-energy landscapes efficiently, uniting machine learning and molecular dynamics for broad mechanistic and practical insight.

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.

Hypothesis: Bacterial contamination of surfaces poses a major threat to public health. Designing effective antibacterial or self-cleaning surfaces requires understanding how bacteria-laden droplets interact with solid substrates and how readily they can be removed. We hypothesize that bacterial motility critically influences the early-stage surface interaction (i.e., surface adhesion) of bacteria-laden droplets, which cannot be captured by conventional contact angle goniometry. Experiments: Sessile droplets containing live and dead Escherichia coli (E. coli) were studied to probe their wetting and interfacial behavior. Contact angle goniometry was used to probe dynamic wetting, while a cantilever-deflection-based method was used to quantify adhesion. Internal flow dynamics were visualized using micro-particle image velocimetry (PIV) and analyzed statistically. Complementary sliding experiments on moderately wettable substrates were performed to assess contact line mobility under tilt. Findings: Despite lower surface tension, droplets containing live bacteria exhibited lower surface adhesion forces than their dead counterparts, with adhesion further decreasing at higher bacterial concentrations. Micro-PIV revealed that flagellated live E. coli actively resist evaporation-driven capillary flow via upstream migration, while at higher concentrations, collective dynamics emerge, producing spatially coherent bacterial motion despite temporal variability. These coordinated flows disrupt passive transport and promote depinning of the contact line, thereby reducing adhesion. Sliding experiments confirmed enhanced contact line mobility and frequent stick-slip motion in live droplets, even with lower receding contact angles and higher hysteresis. These findings provide mechanistic insight into droplet retention, informing the design of self-cleaning/antifouling surfaces.

During development, embryonic tissues experience mechanical stresses ranging from cellular to supracellular length scales. In response, cells generate active forces that drive rearrangements, allowing the tissue to relax accumulated stresses. The nature of these responses depends strongly on the magnitude and duration of the deformation, giving rise to the tissue's characteristic viscoelastic behavior. Although experiments have characterized tissue rheology in various contexts, simpler theoretical approaches that directly connect cellular activity to emergent rheological behavior are still limited. In this study, we employ a vertex-based model of epithelial tissue incorporating active force fluctuations in cell vertices to represent cell motility. We capture distinct rounding dynamics and motility-dependent timescales by benchmarking against experimental observations such as the bulging of presomitic mesoderm (PSM) explants driven by Fibroblast Growth Factor(FGF) gradients. Stress relaxation tests reveal rapid short-timescale relaxation alongside persistent long-timescale residual stresses that decrease from anterior to posterior (AP) region of the PSM. By applying oscillatory shear, we analyzed the resulting elastic and viscous responses, revealing motility dependence of storage and loss modulus. Finally, we introduce spatially patterned cues applied in a temporally pulsed manner, mimicking dynamic biochemical or mechanical signals during development. Our results show that while higher motility promotes tissue remodeling in response to these cues, this response is constrained by spatial scale; cellular-scale perturbations are relaxed irrespective of motility strength, preventing complete morphological adaptation.

It is well known that the different cell-division models, such as Timer, Sizer, and Adder, can be distinguished based on the correlations between different single-cell-level quantities such as birth-size, division-time, division-size, and division-added-size. Here, we show that other statistical properties of these quantities can also be used to distinguish between them. Additionally, the statistical relationships and different correlation patterns can also differentiate between the different types of single-cell growth, such as linear and exponential. Further, we demonstrate that various population-level distributions, such as age, size, and added-size distributions, are indistinguishable across different models of cell division despite them having different division rules and correlation patterns. Moreover, this indistinguishability is robust to stochasticity in growth rate and holds for both exponential and linear growth. Finally, we show that our theoretical predictions are corroborated by simulations and supported by existing single-cell experimental data.

Peristalsis is the driving mechanism behind a broad array of biological and engineered flows. In peristaltic pumping, a wave-like contraction of the tube wall produces local changes in volume which induce flow. Net flow arises due to geometric nonlinearities in the momentum equation, which must be properly captured to compute the flow accurately. While most previous models focus on radius-imposed peristalsis, they often neglect longitudinal length changes - a natural consequence of radial contraction in elastic materials. In this paper, to capture a more accurate picture of peristaltic pumping, we calculate the flow in an elastic vessel undergoing contractions in the transverse and longitudinal directions simultaneously, keeping the geometric nonlinearities arising in the strain. A careful analysis requires us to study our fluid using the Lagrangian coordinates of the elastic tube. We perform analytic calculations of the flow characteristics by studying the fluid inside a fixed boundary with time-dependent metric. This mathematical manipulation works even for large-amplitude contractions, as we confirm by comparing our analytical results to COMSOL simulations. We demonstrate that transverse and longitudinal contractions induce instantaneous flows at the same order in wall strain, but in opposite directions. We investigate the influence of the wall's Poisson ratio on the flow profile. Incompressible walls suppress flow by minimizing local volume changes, whereas auxetic walls enhance flow. For radius-imposed peristaltic waves, wall incompressibility reduces both reflux and particle trapping. In contrast, length-imposed waves typically generate backflow, although trapping can still occur at large amplitudes for some Poisson ratios. These results yield a more complete description of peristalsis in elastic media and offer a framework for studying contraction-induced flows more broadly.

Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine learning (ML) models mostly based on the application of large language models to sequence information have been developed to predict antibody properties. Yet there are open directions to incorporate structural information, not only to enhance prediction but also to offer insights into the underlying molecular mechanisms. This chapter provides an overview of these approaches and describes two ML frameworks that integrate structural data (via graph representations) with neural networks to predict properties of antibodies: ANTIPASTI predicts binding affinity (a global property) whereas INFUSSE predicts residue flexibility (a local property). We survey the principles underpinning these models; the ways in which they encode structural knowledge; and the strategies that can be used to extract biologically relevant statistical signals that can help discover and disentangle molecular determinants of the properties of interest.

We study the genetic interfaces between two species of an expanding colony that consists of individual microorganisms that reproduce and undergo diffusion, both at the frontier and in the interior. Within the bulk of the colony, the genetic interface is controlled in a simple way via interspecies interactions. However, at the frontier of the colony, the genetic interface width saturates at finite values for long times, both for neutral strains and interspecies interactions such as antagonism. This finite width arises from geometric effects: genetic interfaces drift toward local minima at an undulating colony frontier, where a focusing mechanism induced by curvature impedes diffusive mixing. Numerical simulations support a logarithmic dependence of the genetic interface width on the strength of the number fluctuations.