CyberSec Research

We propose a new theory for aging based on dynamical systems and provide a data-driven computational method to quantify the changes at the cellular level. We use ergodic theory to decompose the dynamics of changes during aging and show that aging is fundamentally a dissipative process within biological systems, akin to dynamical systems where dissipation occurs due to non-conservative forces. To quantify the dissipation dynamics, we employ a transformer-based machine learning algorithm to analyze gene expression data, incorporating age as a token to assess how age-related dissipation is reflected in the embedding space. By evaluating the dynamics of gene and age embeddings, we provide a cellular aging map (CAM) and identify patterns indicative of divergence in gene embedding space, nonlinear transitions, and entropy variations during aging for various tissues and cell types. Our results provide a novel perspective on aging as a dissipative process and introduce a computational framework that enables measuring age-related changes with molecular resolution.

A physics-informed neural network (PINN) models the dynamics of a system by integrating the governing physical laws into the architecture of a neural network. By enforcing physical laws as constraints, PINN overcomes challenges with data scarsity and potentially high dimensionality. Existing PINN frameworks rely on fully observed time-course data, the acquisition of which could be prohibitive for many systems. In this study, we developed a new PINN learning paradigm, namely Constrained Learning, that enables the approximation of first-order derivatives or motions using non-time course or partially observed data. Computational principles and a general mathematical formulation of Constrained Learning were developed. We further introduced MPOCtrL (Message Passing Optimization-based Constrained Learning) an optimization approach tailored for the Constrained Learning framework that strives to balance the fitting of physical models and observed data. Its code is available at github link: https://github.com/ptdang1001/MPOCtrL Experiments on synthetic and real-world data demonstrated that MPOCtrL can effectively detect the nonlinear dependency between observed data and the underlying physical properties of the system. In particular, on the task of metabolic flux analysis, MPOCtrL outperforms all existing data-driven flux estimators.

This work investigates how biodiversity is affected in a cyclic spatial May-Leonard model with hierarchical and non-hierarchical rules. Here we propose a generalization of the traditional rock-paper-scissors model by considering highly reactive species, i. e., species that react in a stronger manner compared to the others in respect to either competition or reproduction. These two classes of models, called here Highly Competitive and Highly Reproductive models, may lead to hierarchical and non-hierarchical dynamics, depending on the number of highly reactive species. The fundamental feature of these models is the fact that hierarchical models may as well support biodiversity, however, with a higher probability of extinction than the non-hierarchical ones, which are in fact more robust. This analysis is done by evaluating the probability of extinction as a function of mobility. In particular, we have analyzed how the dominance scheme changes depending on the highly reactive species for non-hierarchical models, where the findings lead to the conclusion that highly reactive species are usually at a disadvantage compared to the others. Moreover, we have investigated the power spectrum and the characteristic length of each species, including more information on the behavior of the several systems considered in the present work.

Suspensions of motile microorganisms can spontaneously give rise to large scale fluid motion, known as bioconvection, which is characterized by dense, cell-rich downwelling plumes interspersed with broad upwelling regions. In this study, we investigate bioconvection in shallow suspensions of Chlamydomonas reinhardtii cells confined within spiral-shaped boundaries, combining detailed experimental observations with 3D simulations. Under open liquid-air interface conditions, cells accumulate near the surface due to negative gravitaxis, forming spiral shaped density patterns that subsequently fragment into lattice-like structures and give rise to downwelling plumes. Space-time analyses reveal coherent rotational dynamics, with inward-moving patterns near the spiral core and bidirectional motion farther from the center. Introducing confinement by sealing the top boundary with an air-impermeable transparent wall triggers striking transitions in the bioconvection patterns, driven by oxygen depletion: initially stable structures reorganize into new patterns with reduced characteristic wavelengths. Complementary 3D simulations, based on the incompressible Navier-Stokes equations and incorporating negative buoyancy and active stress from swimming cells, capture the initial pattern formation and its subsequent instability, reproducing the fragmentation of spiral-shaped accumulations into downwelling plumes and the emergence of strong vortical flows, nearly an order of magnitude faster than individual cell swimming speeds. However, these models do not capture the oxygen-driven pattern transitions observed experimentally. Our findings reveal that confinement geometry, oxygen dynamics, and metabolic transitions critically govern bioconvection pattern evolution, offering new strategies to control microbial self-organization and flow through environmental and geometric design.

Understanding the mechanical behavior of brain tissue is crucial for advancing both fundamental neuroscience and clinical applications. Yet, accurately measuring these properties remains challenging due to the brain unique mechanical attributes and complex anatomical structures. This review provides a comprehensive overview of commonly used techniques for characterizing brain tissue mechanical properties, covering both invasive methods such as atomic force microscopy, indentation, axial mechanical testing, and oscillatory shear testing and noninvasive approaches like magnetic resonance elastography and ultrasound elastography. Each technique is evaluated in terms of working principles, applicability, representative studies, and experimental limitations. We further summarize existing publications that have used these techniques to measure human brain tissue mechanical properties. With a primary focus on invasive studies, we systematically compare their sample preparation, testing conditions, reported mechanical parameters, and modeling strategies. Key sensitivity factors influencing testing outcomes (e.g., sample size, anatomical location, strain rate, temperature, conditioning, and post-mortem interval) are also discussed. Additionally, selected noninvasive studies are reviewed to assess their potential for in vivo characterization. A comparative discussion between invasive and noninvasive methods, as well as in vivo versus ex vivo testing, is included. This review aims to offer practical guidance for researchers and clinicians in selecting appropriate mechanical testing approaches and contributes a curated dataset to support constitutive modeling of human brain tissue.

Capturing the physical organisation and dynamics of genomic regions is one of the major open challenges in biology. The kinetoplast DNA (kDNA) is a topologically complex genome, made by thousands of DNA (mini and maxi) circles interlinked into a two-dimensional Olympic network. The organisation and dynamics of these DNA circles are poorly understood. In this paper, we show that dCas9 linked to Quantum Dots can efficiently label different classes of DNA minicircles in kDNA. We use this method to study the distribution and dynamics of different classes of DNA minicircles within the network. We discover that maxicircles display a preference to localise at the periphery of the network and that they undergo subdiffusive dynamics. From the latter, we can also quantify the effective network stiffness, confirming previous indirect estimations via AFM. Our method could be used more generally, to quantify the location, dynamics and material properties of genomic regions in other complex genomes, such as that of bacteria, and to study their behaviour in the presence of DNA-binding proteins.

The apparent paradox of Maxwell's demon motivated the development of information thermodynamics and, more recently, engineering advances enabling the creation of nanoscale information engines. From these advances, it is now understood that nanoscale machines like the molecular motors within cells can in principle operate as Maxwell demons. This motivates the question: does information help power molecular motors? Answering this would seemingly require simultaneous measurement of all system degrees of freedom, which is generally intractable in single-molecule experiments. To overcome this limitation, we derive a statistical estimator to infer both the direction and magnitude of subsystem heat flows, and thus to determine whether -- and how strongly -- a motor operates as a Maxwell demon. The estimator uses only trajectory measurements for a single degree of freedom. We demonstrate the estimator by applying it to simulations of an experimental realization of an information engine and a kinesin molecular motor. Our results show that kinesin transitions to a Maxwell-demon mechanism in the presence of nonequilibrium noise, with a corresponding increase in velocity consistent with experiments. These findings suggest that molecular motors may have evolved to leverage active fluctuations within cells.

British biophysics has a rich tradition of scientific invention and innovation, on several occasions resulting in new technologies which have transformed biological insight, such as rapid DNA sequencing, high-precision super-resolution and label-free microscopy hardware, new approaches for high-throughput and single-molecule bio-sensing, and the development of a range of de novo bio-inspired synthetic materials. Some of these advances have been established through democratised, open-source platforms and many have biomedical success, a key example involving the SARS-CoV-2 spike protein during the COVID-19 pandemic. Here, three UK labs made crucial contributions in revealing how the spike protein targets human cells, and how therapies such as vaccines and neutralizing nanobodies likely work, enabled in large part through the biophysical technological innovations of cryo-electron microscopy. In this review, we discuss leading-edge technological and methodological innovations which resulted from initial outcomes of discovery-led 'Physics of Life' (PoL) research (capturing biophysics, biological physics and multiple blends of physical-life sciences interdisciplinary research in the UK) and which have matured into wider-reaching sustainable commercial ventures enabling significant translational impact. We describe the fundamental biophysical science which led to a diverse range of academic spinouts, presenting the scientific questions that were first asked and addressed through innovating new techniques and approaches, and highlighting the key publications which ultimately led to commercialisation. We consider these example companies through the lens of opportunities and challenges for academic biophysics research in partnership with British industry. Finally, we propose recommendations concerning future resourcing and structuring of UK biophysics research and the training and support of...

This review explores the innovative design to achieve advanced optical functions in natural materials and intricate optical systems inspired by the unique refractive index profiles found in nature. By understanding the physical principles behind biological structures, we can develop materials with tailored optical properties that mimic these natural systems. One key area discussed is biomimetic materials design, where biological systems such as apple skin and the vision system inspire new materials. Another focus is on intricate optical systems based on refractive index contrast. These principles can be extended to design devices like waveguides, photonic crystals, and metamaterials, which manipulate light in novel ways. Additionally, the review covers optical scattering engineering, which is crucial for biomedical imaging. By adjusting the real and imaginary parts of the refractive index, we can control how much light is scattered and absorbed by tissues. This is particularly important for techniques like optical coherence tomography and multiphoton microscopy, where tailored scattering properties can improve imaging depth and resolution. The review also discusses various techniques for measuring the refractive index of biological tissues which provide comprehensive insights into the optical properties of biological materials, facilitating the development of advanced biomimetic designs. In conclusion, the manipulation of refractive index profiles in biological systems offers exciting opportunities for technological advancements. By drawing inspiration from nature and understanding the underlying physical principles, we can create materials and devices with enhanced performance and new functionalities. Future research should focus on further exploring these principles and translating them into practical applications to address real-world challenges.

This study investigates the radial densification of spruce wood using explicit Finite Element Method simulations, focusing on the effects of various densification protocols. These protocols include quasi-static compression, oscillatory excitation, and self-densification through shrinking hydrogel fillings and their impact on the morphogenesis of folding patterns across different tissue types. The simulations incorporate the an isotropic mechanical behavior of wood tracheid walls and account for moisture and delignification effects using a hierarchical approach. Our results reveal the technological potential of targeted densification in creating tailored density profiles that enhance stiffness and strength. These insights offer valuable guidance for optimizing densification processes in practical applications.

Stick insect stepping patterns have been studied for insights about locomotor rhythm generation and control, because the underlying neural system is relatively accessible experimentally and produces a variety of rhythmic outputs. Harnessing the experimental identification of effective interactions among neuronal units involved in stick insect stepping pattern generation, previous studies proposed computational models simulating aspects of stick insect locomotor activity. While these models generate diverse stepping patterns and transitions between them, there has not been an in-depth analysis of the mechanisms underlying their dynamics. In this study, we focus on modeling rhythm generation by the neurons associated with the protraction-retraction, levitation-depression, and extension-flexion antagonistic muscle pairs of the mesothoracic (middle) leg of stick insects. Our model features a reduced central pattern generator (CPG) circuit for each joint and includes synaptic interactions among the CPGs; we also consider extensions such as the inclusion of motoneuron pools controlled by the CPG components. The resulting network is described by an 18-dimensional system of ordinary differential equations. We use fast-slow decomposition, projection into interacting phase planes, and a heavy reliance on input-dependent nullclines to analyze this model. Specifically, we identify and elucidate dynamic mechanisms capable of generating a stepping rhythm, with a sequence of biologically constrained phase relationships, in a three-joint stick insect limb model. Furthermore, we explain the robustness to parameter changes and tunability of these patterns. In particular, the model allows us to identify possible mechanisms by which neuromodulatory and top-down effects could tune stepping pattern output frequency.

Achieving high-resolution optical imaging deep within heterogeneous and scattering media remains a fundamental challenge in biological microscopy, where conventional techniques are hindered by multiple light scattering and absorption. Here, we present a non-invasive imaging approach that harnesses the nonlinear response of luminescent labels in conjunction with the statistical and spatial properties of speckle patterns - an effect of random light interference. Using avalanching nanoparticles (ANPs) with strong photoluminescence nonlinearity, we demonstrate that random speckle illumination can be converted into a single, localized, sub-diffraction excitation spot. This spot can be scanned across the sample using the angular memory effect, enabling high-resolution imaging through a scattering layer. Our method is general, fast, and cost-effective. It requires no wavefront shaping, no feedback, and no reconstruction algorithm, offering a powerful new route to deep, high-resolution imaging through complex media.

Quantum computing applications in diverse domains are emerging rapidly. Given the limitations of classical computing techniques, the peculiarity of quantum circuits, which can observe quantum phenomena such as superposition, entanglement, and quantum coherence, is remarkable. This capability enables them to achieve measurement sensitivities far beyond classical limits. Research on radical pair-based magnetoreception in migratory birds has been a focus area for quite some time. A quantum mechanics-based computing approach, thus unsurprisingly, identifies a scope of application. In this study, to observe the phenomenon, electron-nucleus spin quantum circuits for different geomagnetic fluxes have been simulated and run through IBM Qiskit quantum processing units with error mitigation techniques. The results of different quantum states are consistent, suggesting singlet-triplet mechanisms that can be emulated, resembling the environment-enabling flights of migratory birds through generations of the avian species. The four-qubit model emulating electron-nucleus systems mimicking the environmental complexity outcome shows the sensitiveness to change of magnetic flux index, high probability of singlet-triplet dynamics, and upholding radical pair model states by the purity of the sub-system and full system outcome of coherence, the hallmark of singlet state dominance. The work involved performing fifty quantum circuits for different magnetic field values, each with one thousand and twenty-four shots for measurement, either in the simulator or on real quantum hardware, and for two error mitigation techniques, preceded by a noise model of a simulator run.

Undulatory slender objects have been a central theme in the hydrodynamics of swimming at low Reynolds number, where the slender body is usually assumed to be inextensible, although some microorganisms and artificial microrobots largely deform with compression and extension. Here, we theoretically study the coupling between the bending and compression/extension shape modes, using a geometrical formulation of microswimmer hydrodynamics to deal with the non-commutative effects between translation and rotation. By means of a coarse-grained minimal model and systematic perturbation expansions for small bending and compression/extension, we analytically derive the swimming velocities and report three main findings. First, we revisit the role of anisotropy in the drag ratio of the resistive force theory and generally demonstrate that no motion is possible for uniform compression with isotropic drag. We then find that the bending-compression/extension coupling generates lateral and rotational motion, which enhances the swimmer's manoeuvrability, as well as changes in progressive velocity at a higher order of expansion, while the coupling effects depend on the phase difference between the two modes. Finally, we demonstrate the importance of often-overlooked Lie bracket contributions in computing net locomotion from a deformation gait. Our study sheds light on compression as a forgotten degree of freedom in swimmer locomotion, with important implications for microswimmer hydrodynamics, including understanding of biological locomotion mechanisms and design of microrobots.

Lipid nanoparticles (LNPs) are precisely engineered drug delivery carriers commonly produced through controlled mixing processes, such as nanoprecipitation. Since their delivery efficacy greatly depends on particle size, numerous studies have proposed experimental and theoretical approaches for tuning LNP size. However, the mechanistic model for LNP fabrication has rarely been established alongside experiments, limiting a profound understanding of the kinetic processes governing LNP self-assembly. Thus, we present a population balance equation (PBE)-based model that captures the evolution of the particle size distribution (PSD) during LNP fabrication, to provide mechanistic insight into how kinetic processes control LNP size. The model showed strong agreement with experimentally observed trends in the PSD. In addition to identifying the role of each kinetic process in shaping the PSD, we analyzed the underlying mechanisms of three key operational strategies: manipulation of (1) lipid concentration, (2) flow rate ratio (FRR), and (3) mixing rate. We identified that the key to producing precisely controlled particle size lies in controlling super-saturation and lipid dilution to regulate the balance between nucleation and growth. Our findings provide mechanistic understanding that is essential in further developing strategies for tuning LNP size.

We investigate the dynamics of a pair of rigid rotating helices in a viscous fluid, as a model for bacterial flagellar bundle and a prototype of microfluidic pumps. Combining experiments with hydrodynamic modeling, we examine how spacing and phase difference between the two helices affect their torque, flow field and fluid transport capacity at low Reynolds numbers. Hydrodynamic coupling reduces the torque when the helices rotate in phase at constant angular speed, but increases the torque when they rotate out of phase. We identify a critical phase difference, at which the hydrodynamic coupling vanishes despite the close spacing between the helices. A simple model, based on the flow characteristics and positioning of a single helix, is constructed, which quantitatively predicts the torque of the helical pair in both unbounded and confined systems. Lastly, we show the influence of spacing and phase difference on the axial flux and the pump efficiency of the helices. Our findings shed light on the function of bacterial flagella and provide design principles for efficient low-Reynolds-number pumps.

Flexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. While such flexibility is influenced by various factors such as attention and context, the common neural mechanisms responsible for this modulation remain poorly understood. We developed a biologically plausible neural network model that incorporates neurons with multiple timescales and Hebbian learning rules. This model is capable of generating simple sequential patterns as well as performing delayed match-to-sample (DMS) tasks that require the retention of stimulus identity. Fast neural dynamics establish metastable states, while slow neural dynamics maintain task-relevant information and modulate the stability of these states to enable temporal processing. We systematically analyzed how factors such as neuronal gain, external input strength (contextual cues), and task difficulty influence the temporal properties of neural activity sequences - specifically, dwell time within patterns and transition times between successive patterns. We found that these factors flexibly modulate the stability of metastable states. Our findings provide a unified mechanism for understanding various forms of temporal modulation and suggest a novel computational role for neural timescale diversity in dynamically adapting cognitive performance to changing environmental demands.

This study investigates the influence of aneurysm evolution on hemodynamic characteristics within the sac region. Using computational fluid dynamics (CFD), blood flow through the parent vessel and aneurysm sac was analyzed to assess the impact on wall shear stress (WSS), time-averaged wall shear stress (TAWSS), and the oscillatory shear index (OSI), key indicators of rupture risk. Additionally, Relative Residence Time (RRT) and Endothelial Cell Activation Potential (ECAP) were examined to provide a broader understanding of the aneurysm's hemodynamic environment. Six distinct cerebral aneurysm (CA) models, all from individuals of the same gender, were selected to minimize gender-related variability. Results showed that unruptured cases exhibited higher WSS and TAWSS, along with lower OSI and RRT values patterns consistent with stable flow conditions supporting vascular integrity. In contrast, ruptured cases had lower WSS and TAWSS, coupled with elevated OSI and RRT, suggesting disturbed and oscillatory flow commonly linked to aneurysm wall weakening. ECAP was also higher in ruptured cases, indicating increased endothelial activation under unstable flow. Notably, areas with the highest OSI and RRT often aligned with vortex centers, reinforcing the association between disturbed flow and aneurysm instability. These findings highlight the value of combining multiple hemodynamic parameters for rupture risk assessment. Including RRT and ECAP provides deeper insight into flow endothelium-interactions, offering a stronger basis for evaluating aneurysm stability and guiding treatment decisions.