CyberSec Research

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.

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.

We derive a formulation of the First Law of nonequilibrium thermodynamics for biological information-processing systems by partitioning entropy in the Second Law into microscopic and mesoscopic components and by assuming that natural selection promotes optimal information processing and transmission. The resulting framework demonstrates how mesoscopic information-based subsystems can attain nonequilibrium steady states (NESS) sustained by external energy and entropy fluxes, such as those generated by ATP/ADP imbalances in vivo. Moreover, mesoscopic systems may reach NESS before microscopic subsystems, leading to ordered structures in entropy flow analogous to eddies in a moving stream.

Autophagy and migrasome formation constitute critical cellular mechanisms for maintaining cellular homeostasis, however, their potential compensatory interplay remains poorly understood. In this study, we identify VPS39, a core component of the HOPS complex, as a molecular switch coordinating these processes. Genetic ablation of VPS39 not only impairs autophagic flux but also triggers cell migration through RhoA/Rac1 GTPases upregulation, consequently facilitating migrasome formation. Using super-resolution microscopy, we further demonstrate that migrasomes serve as an alternative disposal route for damaged mitochondria during VPS39-induced autophagy impairment, revealing a novel stress adaptation mechanism. Our work establishes a previously unrecognized autophagy-migrasome axis and provides direct visual evidence of organelle quality control via migrasomal extrusion. These findings position VPS39-regulated pathway switching as a potential therapeutic strategy for neurodegenerative diseases characterized by autophagy dysfunction.

We study the dynamics of molecular motor-driven transport into dendritic spines, which are bulbous intracellular compartments in neurons that play a key role in transmitting signals between neurons. We further develop a stochastic model of vesicle transport in [Park, Singh, and Fai, SIAM J. Appl. Math. 82.3 (2022), pp. 793--820] by showing that second-order moments may be neglected. We exploit this property to significantly simplify the model and confirm through numerical simulations that the simplification retains key behaviors of the original agent-based myosin model of vesicle transport. We use the simplified model to explore the vesicle translocation time and probability through dendritic spines as a function of molecular motor parameters, which was previously not practically possible. Relevance to Life Sciences: We find that thinner dendritic spine geometry can greatly reduce the probability of vesicle translocation to the post-synaptic density. The cell may alter molecular motor parameters to compensate, but only to a point. These findings are consistent with the biological literature, where brain disorders are often associated with an excess of long, thin dendritic spines. Mathematical Content: We use a moment-generating function to deduce that second-order moments in motor attachment times may be neglected, and therefore the first-order moment is a sufficient approximation. Using only the mean attachment times and neglecting the variance yields a tractable master equation from which vesicle mean first passage times may be computed directly as a function of geometry and molecular motor parameters.

The emerging field of epigenetics has recently unveiled a dynamic landscape in which gene expression is not determined solely by genetic sequences but also by intricate regulatory mechanisms. This review examines the interactions between these regulatory mechanisms, including DNA methylation and non-coding RNAs (ncRNAs), that orchestrate gene expression fine-tuning for cellular homeostasis and the pathogenesis of a multitude of diseases. We explore long non-coding RNAs (lncRNAs) such as telomeric repeat-containing RNA (TERRA) and Fendrr, highlighting their role in protein regulation to ensure proper gene activation or silencing. Additionally, we explain the therapeutic potential of brain-derived neurotrophic factor (BDNF)-related microRNA 132, which has shown promise in treating chronic illnesses by restoring BDNF levels. Finally, this review covers the role of DNA methyltransferases and ncRNAs in cancer, focusing on how lncRNAs contribute to X chromosome inactivation and interact with chromatin-modifying complexes and DNA methyltransferase inhibitors to reduce cancer cell aggressiveness. By amalgamating the wide array of research in this field, we aim to provide glimpses into the complex entangling of genetics and environment as they control gene expressions.

Aims. Resistance to targeted therapies remains a major challenge in EGFR-mutant non-small cell lung cancer (NSCLC). Here, we describe a novel metabolic adaptation in osimertinib-resistant cells characterized by elevated acetate levels and activation of an unconventional pyruvate-acetaldehyde-acetate (PAA) shunt. Methods. Integrated transcriptomic, exometabolomic, and functional analyses reveal suppression of canonical metabolic pathways and upregulation of ALDH2 and ALDH7A1, that mediate the NADP+-dependent oxidation of acetaldehyde to acetate, generating NADPH. Results. This shift generates reducing power essential for biosynthesis and redox balance under conditions of oxidative pentose phosphate inhibition. These metabolic changes promote endurance in resistant cells and rewire the interplay between glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle, offering a de novo bypass for anaplerosis and bioenergetics. Systematic metabolite profiling revealed distinct transcriptomic and metabolic signatures distinguishing resistant from drug sensitive parental cells. Conclusions. Together, these findings depict a unique, resistance-driven adaptive metabolic shift and uncover potential therapeutic vulnerabilities in osimertinib-resistant NSCLC.

Control of transcription presides over a vast array of biological processes including through gene regulatory circuits that exhibit multistability. Two- and three-gene network motifs are often found to be critical parts of the repertoire of metabolic and developmental pathways. Theoretical models of these circuits, however, typically vary parameters such as dissociation constants, transcription rates, and degradation rates without specifying precisely how these parameters are controlled biologically. In this paper, we examine the role of effector molecules, which can alter the concentrations of the active transcription factors that control regulation, and are ubiquitous to regulatory processes across biological settings. We specifically consider allosteric regulation in the context of extending the standard bistable switch to three-gene networks, and explore the rich multistable dynamics exhibited in these architectures as a function of effector concentrations. We then study how the conditions required for tristability and more complex dynamics, and the bifurcations in dynamic phase space upon tuning effector concentrations, evolve under various interpretations of regulatory circuit mechanics, the underlying activity of inducers, and perturbations thereof. Notably, the biological mechanism by which we model effector control over dual-function proteins transforms not only the phenotypic trend of dynamic tuning but also the set of available dynamic regimes. In this way, we determine key parameters and regulatory features that drive phenotypic decisions, and offer an experimentally tunable structure for encoding inducible multistable behavior arising from both single and dual-function allosteric transcription factors.

$\textit{Salmonella}$ expresses bacterial microcompartments (MCPs) upon 1,2-propanediol exposure. MCPs are nanoscale protein-bound shells that encase enzymes for the cofactor-dependent 1,2-propanediol metabolism. They are hypothesized to limit exposure to the toxic intermediate, propionaldehyde, decrease cofactor involvement in competing reactions, and enhance flux. We construct a mass-action mathematical model of purified MCPs and calibrate parameters to measured metabolite concentrations. We constrain mass-action kinetic parameters to previously estimated Michaelis-Menten parameters. We identified two distinct fits with different dynamics in the pathway product, propionate, but similar goodness of fit. Across fits, we inferred that the MCP 1,2-propanediol and propionaldehyde permeability should be greater than $10^{-6}$ and $10^{-8}$ m/s, respectively. Our results identify parameter ranges consistent with prevailing theories that MCPs impose preferential diffusion to 1,2-propanediol over propionaldehyde, and sequester toxic propionaldehyde away from the cell cytosol. The bimodality of the posterior distribution arises from bimodality in the estimated coenzyme-A (CoA) permeability and inhibition rates. The MCP permeability to CoA was inferred to be either less than $10^{-8.8}$ m/s or greater than $10^{-7.3}$ m/s. In a high CoA permeability environment with low rates of CoA inhibition, enzymes produced metabolites by recycling (NAD+)/(NADH). In a low CoA permeability environment with high rates of CoA inhibition, enzymes required external NAD+/H to produce metabolites. Dynamics are consistent with prevailing hypotheses about MCP function to sequester toxic propionaldehyde, and additional collection of data points between 6 and 24 hours or characterization of enzyme inhibition rates could further reduce uncertainty and provide better permeability estimates.

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 consistent 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.