CyberSec Research

The ability of virus shells to encapsulate a wide range of functional cargoes, especially multiple cargoes - siRNAs, enzymes, and chromophores - has made them an essential tool in biotechnology for advancing drug delivery applications and developing innovative new materials. Here we present a mechanistic study of the processes and pathways that lead to multiple cargo encapsulation in the co-assembly of virus shell proteins with ligand-coated nanoparticles. Based on the structural identification of different intermediates, enabled by the contrast in electron microscopy provided by the metal nanoparticles that play the cargo role, we find that multiple cargo encapsulation occurs by self-assembly via a specific ``assembly line'' pathway that is different from previously described \emph{in vitro} assembly mechanisms of virus-like particles (VLP). The emerging model explains observations that are potentially important for delivery applications, for instance, the pronounced nanoparticle size selectivity.

In molecular communications (MC), inter-symbol interference (ISI) and noise are key factors that degrade communication reliability. Although time-domain equalization can effectively mitigate these effects, it often entails high computational complexity concerning the channel memory. In contrast, frequency-domain equalization (FDE) offers greater computational efficiency but typically requires prior knowledge of the channel model. To address this limitation, this letter proposes FDE techniques based on long short-term memory (LSTM) neural networks, enabling temporal correlation modeling in MC channels to improve ISI and noise suppression. To eliminate the reliance on prior channel information in conventional FDE methods, a supervised training strategy is employed for channel-adaptive equalization. Simulation results demonstrate that the proposed LSTM-FDE significantly reduces the bit error rate compared to traditional FDE and feedforward neural network-based equalizers. This performance gain is attributed to the LSTM's temporal modeling capabilities, which enhance noise suppression and accelerate model convergence, while maintaining comparable computational efficiency.

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.

This paper addresses a profoundly challenging inverse problem that has remained largely unexplored due to its mathematical complexity: the unique identification of all unknown coefficients in a coupled nonlinear system of mixed parabolic-elliptic-elliptic type using only boundary measurements. The system models attraction-repulsion chemotaxis--an advanced mathematical biology framework for studying sophisticated cellular processes--yet despite its significant practical importance, the corresponding inverse problem has never been investigated, representing a true frontier in the field. The mixed-type nature of this system introduces significant theoretical difficulties that render conventional methodologies inadequate, demanding fundamental extensions beyond existing techniques developed for simpler, purely parabolic models. Technically, the problem presents formidable obstacles: the coupling between parabolic and elliptic components creates inherent analytical complications, while the nonlinear structure resists standard approaches. From an applied perspective, the biological relevance adds another layer of complexity, as solutions must maintain physical interpretability through non-negativity constraints. Our work provides a complete theoretical framework for this challenging problem, establishing rigorous unique identifiability results that create a one-to-one correspondence between boundary data and the model's parameters. We demonstrate the power of our general theory through a central biological application: the full parameter recovery for an attraction-repulsion chemotaxis model with logistic growth, thus opening new avenues for quantitative analysis in mathematical biology.

Living cells exhibit a complex organization comprising numerous compartments, among which are RNA- and protein-rich membraneless, liquid-like organelles known as biomolecular condensates. Energy-consuming processes regulate their formation and dissolution, with (de-)phosphorylation by specific enzymes being among the most commonly involved reactions. By employing a model system consisting of a phosphorylatable peptide and homopolymeric RNA, we elucidate how enzymatic activity modulates the growth kinetics and alters the local structure of biomolecular condensates. Under passive condition, time-resolved ultra-small-angle X-ray scattering with synchrotron source reveals a nucleation-driven coalescence mechanism maintained over four decades in time, similar to the coarsening of simple binary fluid mixtures. Coarse-grained molecular dynamics simulations show that peptide-decorated RNA chains assembled shortly after mixing constitute the relevant subunits. In contrast, actively-formed condensates initially display a local mass fractal structure, which gradually matures upon enzymatic activity before condensates undergo coalescence. Both types of condensate eventually reach a steady state but fluorescence recovery after photobleaching indicates a peptide diffusivity twice higher in actively-formed condensates consistently with their loosely-packed local structure. We expect multiscale, integrative approaches implemented with model systems to link effectively the functional properties of membraneless organelles to their formation and dissolution kinetics as regulated by cellular active processes.

Living cells sense noisy biochemical signals crucial for survival, yet models incorporating intracellular signaling are limited. This study examines how cells sense chemotactic concentrations through phosphorylation readouts in Ca2+ signaling, which is ubiquitous in most eukaryotic cells. Using stochastic simulations and analytical calculations we find that concentration sensing remains robust to variations in cytoplasmic reaction rates once they exceed a certain value, suggesting a potential evolutionary advantage that allows cells to optimize other signaling tasks without compromising concentration sensing accuracy. Our analysis demonstrates theoretically that Dictyostelium is capable of sensing very low concentrations of cyclic adenosine monophosphate (cAMP) as is experimentally seen.

Electrochemical phenomena in biology often unfold in confined geometries where micrometer- to millimeter-scale domains coexist with nanometer-scale interfacial diffuse charge layers. We analyze a model lipid membrane-electrolyte system where an ion channel-like current flows across the membrane while parallel electrodes simultaneously apply a step voltage, emulating an extrinsic electric field. Matched asymptotic expansions of the Poisson-Nernst-Planck equations show that, under physiological conditions, the diffuse charge layers rapidly reach a quasi-steady state, and the bulk electrolyte remains electroneutral. As a result, all free charge is confined to the nanometer-scale screening layers at the membrane and electrode interfaces. The bulk electric potential satisfies Laplace's equation, and is dynamically coupled to the interfacial layers through time-dependent boundary conditions. This multiscale coupling partitions the space-time response into distinct regimes. At sufficiently long times, we show that the system can be represented by an equivalent circuit analogous to those used in classical cable theory. We derive closed-form expressions of the transmembrane potential within each regime, and verify them against nonlinear numerical simulations. Our results show how electrode-induced screening and confinement effects influence the electrochemical response over multiple length and time scales in biological systems.

We present a novel nonlinear state transition model for inositol 1,4,5-trisphosphate receptors (IP$_3$Rs) that incorporates a pre-activated state, as suggested by electron microscopy observations. Our model provides a theoretical framework for the biphasic Ca$^{2+}$ dependence of IP$_3$Rs and accurately reproduces their experimentally observed state distribution under saturating IP$_3$ conditions. By integrating receptor dynamics with cytoplasmic and endoplasmic reticulum (ER) calcium exchange, we simulate IP$_3$R-mediated Ca$^{2+}$ oscillations governed by six key conformational states. A pivotal finding is that IP$_3$ regulates these oscillations in a switch-like manner: once a critical IP$_3$ concentration is reached, the system abruptly transitions to sustained, constant-amplitude oscillations that quickly terminate when the concentration exceeds a secondary threshold. These results underscore the crucial role of the pre-activated state in modulating calcium signaling.

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.

Biomolecular condensates form on timescales of seconds in cells upon environmental or compositional changes. Condensate formation is thus argued to act as a mechanism for sensing such changes and quickly initiating downstream processes, such as forming stress granules in response to heat stress and amplifying cGAS enzymatic activity upon detection of cytosolic DNA. Here, we show that phase separation allows cells to discriminate small concentration differences on finite, biologically relevant timescales. We propose optimal sensing protocols, which use the sharp onset of phase separation. We show how, given experimentally measured rates, cells can achieve rapid and robust sensing of concentration differences of 1% on a timescale of minutes, offering an alternative to classical biochemical mechanisms.

BACKGROUND: Primary aldosteronism is the most common form of secondary hypertension. The most frequent genetic cause of aldosterone-producing adenomas is somatic mutations in the potassium channel KCNJ5. They affect the ion selectivity of the channel, with sodium influx leading to cell membrane depolarization and activation of calcium signaling, the major trigger for aldosterone biosynthesis. METHODS: To investigate how KCNJ5 mutations lead to the development of aldosterone-producing adenomas, we established an adrenocortical cell model in which sodium entry into the cells can be modulated on demand using chemogenetic tools [H295R-S2 $\alpha$7-5HT3-R ($\alpha$7-5HT3 receptor) cells]. We investigated their functional and molecular characteristics with regard to aldosterone biosynthesis and cell proliferation. RESULTS: A clonal cell line with stable expression of the chimeric $\alpha$7-5HT3-R in H295R-S2 (human adrenocortical carcinoma cell line, Strain 2) cells was obtained. Increased sodium entry through $\alpha$7-5HT3-R upon stimulation with uPSEM-817 (uPharmacologically Selective Effector Molecule-817) led to cell membrane depolarization, opening of voltage-gated Ca 2+ channels, and increased intracellular Ca 2+ concentrations, resulting in the stimulation of CYP11B2 expression and increased aldosterone biosynthesis. Increased intracellular sodium influx did not increase proliferation but rather induced apoptosis. RNA sequencing and steroidome analyses revealed unique profiles associated with Na + entry, with only partial overlap with Ang II (angiotensin II) or potassium-induced changes. CONCLUSIONS: H295R-S2 $\alpha$7-5HT3-R cells are a new model reproducing the major features of cells harboring KCNJ5 mutations. Increased expression of CYP11B2 and stimulation of the mineralocorticoid biosynthesis pathway are associated with a decrease of cell proliferation and an increase of apoptosis, indicating that additional events may be required for the development of aldosterone-producing adenomas.

Accurate regulation of calcium release is essential for cellular signaling, with the spatial distribution of ryanodine receptors (RyRs) playing a critical role. In this study, we present a nonlinear spatial network model that simulates RyR spatial organization to investigate calcium release dynamics by integrating RyR behavior, calcium buffering, and calsequestrin (CSQ) regulation. The model successfully reproduces calcium sparks, shedding light on their initiation, duration, and termination mechanisms under clamped calcium conditions. Our simulations demonstrate that RyR clusters act as on-off switches for calcium release, producing short-lived calcium quarks and longer-lasting calcium sparks based on distinct activation patterns. Spark termination is governed by calcium gradients and stochastic RyR dynamics, with CSQ facilitating RyR closure and spark termination. We also uncover the dual role of CSQ as both a calcium buffer and a regulator of RyRs. Elevated CSQ levels prolong calcium release due to buffering effects, while CSQ-RyR interactions induce excessive refractoriness, a phenomenon linked to pathological conditions such as ventricular arrhythmias. Dysregulated CSQ function disrupts the on-off switching behavior of RyRs, impairing calcium release dynamics. These findings provide new insights into RyR-mediated calcium signaling, highlighting CSQ's pivotal role in maintaining calcium homeostasis and its implications for pathological conditions. This work advances the understanding of calcium spark regulation and underscores its significance for cardiomyocyte function.

The mitotic spindle partitions chromosomes during cell division by connecting the poles to kinetochores through microtubules (MTs). Their plus-ends, facing the chromosomes, exhibit dynamic instability, which is critical for proper attachment. The poleward flux implicates the displacement of Mts towards the spindle poles, while plus-ends polymerise. It may result from minus-end depolymerisation (treadmilling), sliding by kinesins (e.g., Kinesin-5), or pushing by chromokinesins. Intriguingly, such flux had not been reported in the C. elegans zygote, despite homologs of flux-associated proteins being present. To investigate this, we fluorescently labelled Mts and used photobleaching. We observed no global flux; instead, the bleached zone's edges moved inward. The centrosome-facing front reflected MT dynamic instability, but the chromosome-facing front showed faster recovery, suggesting an additional mechanism. This extra velocity was spatially restricted to the vicinity of chromosomes, suggesting that only the kinetochore Mts may undergo flux. Supporting this, flux required key kinetochore regulators: NDC-80, $\text{CLS-2}^\text{CLASP}$, and $\text{ZYG-9}^\text{XMAP215}$. Flux declined as metaphase progressed, correlating with the attachment maturation from lateral to end-on, and was suppressed by SKA-1 recruitment. Classic treadmilling was unlikely, as most kinetochore MTs in C. $elegans$ do not reach spindle poles. Instead, depleting $\text{KLP-18}^\text{KIF15}$, a kinesin that cross-links and organises Mts during meiosis, reduced front movement. We propose that only kinetochore Mts undergo flux, sliding along the spindle Mts, likely powered by KLP-18. This localised sliding contrasts with global flux seen in other systems, and aligns with observations in human cells showing flux reduction as chromosome-to-pole distance increases.

In a 2018 paper and a subsequent article published in 2023, researchers reported that mitochondria maintain temperatures 10-15 degrees higher than the surrounding cytoplasm - a finding that deviates by 5 to 6 orders of magnitude from theoretical predictions based on Fourier s law of heat conduction. In 2022, we proposed a solution to this apparent paradox. In the present perspective, we build upon that framework and introduce new ideas to further unravel how a biological membrane - whether of an organelle or a whole cell - can become significantly warmer than its environment. We propose that proteins embedded in the inner mitochondrial membrane (IMM) can be modeled as ratchet engines, introducing a novel, previously overlooked mode of heat transfer. This mechanism, coupled with localized heat release during the cyclical dehydration-translocation-hydration of ions through membrane proteins, may generate transient but substantial temperature spikes. In the case of protons, the cycle additionally includes deprotonation before translocation and protonation after. The cumulative effect of these microscopic events across the three-dimensional surface of the IMM can account for the elevated temperatures detected by molecular probes. We also offer a hypothesis based on quantum chemical calculations on how such probes might detect these fleeting thermal signatures.

Cells integrate signals and make decisions about their future state in short amounts of time. A lot of theoretical effort has gone into asking how to best design gene regulatory circuits that fulfill a given function, yet little is known about the constraints that performing that function in a small amount of time imposes on circuit architectures. Using an optimization framework, we explore the properties of a class of promoter architectures that distinguish small differences in transcription factor concentrations under time constraints. We show that the full temporal trajectory of gene activity allows for faster decisions than its integrated activity represented by the total number of transcribed mRNA. The topology of promoter architectures that allow for rapidly distinguishing low transcription factor concentrations result in a low, shallow, and non cooperative response, while at high concentrations, the response is high and cooperative. In the presence of non-cognate ligands, networks with fast and accurate decision times need not be optimally selective, especially if discrimination is difficult. While optimal networks are generically out of equilibrium, the energy associated with that irreversibility is only modest, and negligible at small concentrations. Instead, our results highlight the crucial role of rate-limiting steps imposed by biophysical constraints.

We investigate proteins within heterogeneous cell membranes where non-equilibrium phenomena arises from spatial variations in concentration and temperature. We develop simulation methods building on non-equilibrium statistical mechanics to obtain stochastic hybrid continuum-discrete descriptions which track individual protein dynamics, spatially varying concentration fluctuations, and thermal exchanges. We investigate biological mechanisms for protein positioning and patterning within membranes and factors in thermal gradient sensing. We also study the kinetics of Brownian motion of particles with temperature variations within energy landscapes arising from heterogeneous microstructures within membranes. The introduced approaches provide self-consistent models for studying biophysical mechanisms involving the drift-diffusion dynamics of individual proteins and energy exchanges and fluctuations between the thermal and mechanical parts of the system. The methods also can be used for studying related non-equilibrium effects in other biological systems and soft materials.

Cancer cells are often seen to prefer glycolytic metabolism over oxidative phosphorylation even in the presence of oxygen-a phenomenon termed the Warburg effect. Despite significant strides in the decades since its discovery, a clear basis is yet to be established for the Warburg effect and why cancer cells show such a preference for aerobic glycolysis. In this review, we draw on what is known about similar metabolic shifts both in normal mammalian physiology and overflow metabolism in microbes to shed new light on whether aerobic glycolysis in cancer represents some form of optimisation of cellular metabolism. From microbes to cancer, we find that metabolic shifts favouring glycolysis are sometimes driven by the need for faster growth, but the growth rate is by no means a universal goal of optimal metabolism. Instead, optimisation goals at the cellular level are often multi-faceted and any given metabolic state must be considered in the context of both its energetic costs and benefits over a range of environmental contexts. For this purpose, we identify the conceptual framework of resource allocation as a potential testbed for the investigation of the cost-benefit balance of cellular metabolic strategies. Such a framework is also readily integrated with dynamical systems modelling, making it a promising avenue for new answers to the age-old question of why cells, from cancers to microbes, choose the metabolic strategies they do.

Nearly a decade ago it was discovered that the spherical cell body of the alga $Chlamydomonas~reinhardtii$ can act as a lens to concentrate incoming light onto the cell's membrane-bound photoreceptor and thereby affect phototaxis. Since many nearly transparent cells in marine environments have complex, often non-axisymmetric shapes, this observation raises fundamental, yet little-explored questions in biological optics about light refraction by the bodies of microorganisms. There are two distinct contexts for such questions: the $absorption$ problem for $incoming$ light, typified by photosynthetic activity taking place in the chloroplasts of green algae, and the $emission$ problem for $outgoing$ light, where the paradigm is bioluminescence emitted from scintillons within dinoflagellates. Here we examine both of these aspects of ``algal optics" in the special case where the absorption or emission is localized in structures that are small relative to the overall organism size, taking into account both refraction and reflections at the cell-water boundary. Analytical and numerical results are developed for the distribution of light intensities inside and outside the body, and we establish certain duality relationships that connect the incoming and outgoing problems. For strongly non-spherical shapes we find lensing effects that may have implications for photosynthetic activity and for the angular distribution of light emitted during bioluminescent flashes.