Biology subjects

Hunter, P. J.

Publications and source records attributed to Hunter, P. J..

8 recordsLinked to original sources

bioRxiv2026

Genomic regulation of chemo-mechanical stability in plant-derived extracellular vesicles: a multiscale model of composite reinforcement

Plant-derived extracellular vesicles (PDEVs) have emerged as superior candidates for oral drug delivery, exhibiting a gastrointestinal survivability that significantly exceeds that of mammalian exosomes or synthetic liposomes. However, the biophysical rules governing how plant genomic regulation translates into this exceptional mechanical resilience remain unknown. Here, we present a predictive multiscale model of plant-derived extracellular vesicles, linking a parameterised genetic state space to emergent mesoscale mechanics via supra-molecular coarse-grained molecular dynamics (SCG-MD). We demonstrate that the upregulation of sterol methyltransferases (SMT) during the plants theoretical Defence state drives the formation of a phase-separated composite architecture, where rigid domains occupying approximately 36% of the membrane surface area effectively arrest crack propagation. This state achieves a critical rupture tension of 367.0 {+/-} 0.7 mN m-1 corresponding to a 39% increase over the wild-type Ripening state. Crucially, we find that chemical composition alone is insufficient for this reinforcement; vesicles with actively sorted lipid domains (Seeded topology) outperform randomised mixtures (Spontaneous topology) by 23% at identical concentrations. Furthermore, while fluid vesicles stiffen reactively under gastric acid shock (pH 2.5) due to the steric jamming of thermodynamically neutralised headgroups, the Defence state exhibits mechanical homeostasis. These findings suggest that PDEVs function as genetically tunable composite materials, offering a design blueprint for next-generation bio-inspired drug delivery vectors. Ultimately, these theoretical indices provide a predictive biophysical framework awaiting empirical confirmation via in vitro nanomechanical assays.

plant biology↗

bioRxiv2026

A Computational Functional Tissue Unit of the Human Myometrium for In Silico Study of Gestational Excitability and Pathophysiology

The human myometrium undergoes a dramatic transformation during pregnancy, shifting from quiescence to highly synchronised contractility. Understanding this transition is crucial for addressing pathologies such as preterm labour and dystocia (ineffective labour). We present a multi-scale Functional Tissue Unit (FTU) model allowing us to investigate how tissue-level excitability emerges from single-cell electrophysiology. We propose a heterogeneity-driven selection mechanism, wherein a sub-population of cells with high intrinsic excitability dynamically emerges as pace-makers. This active process complements passive depolarisation by interstitial cells, allowing spontaneous excitation to arise without a fixed anatomical pacemaker. Stochastic simulations produced an average burst frequency of 0.047 Hz ({approx}2.8 bursts per minute), closely consistent with clinical measurements of 2-3 contractions per minute during active labour, and demonstrated that this function is robust to spatial topological changes. Furthermore, implementation of inflammation-induced remodelling simulations successfully linked molecular-level changes to a preterm labour phenotype. This model provides a platform for investigating uterine contractility and serves as a component for future whole-organ Physiome models.

bioengineering↗

bioRxiv2026

Energetic analysis of Na+/K+-ATPase using bond graphs

The sodium-potassium ATPase (NKA) accounts for 19-28% of the total ATP consumption in mammals and is critical for maintaining ion homeostasis. Understanding its energetic efficiency is essential for comprehending cellular physiology and pathophysiology. We develop bond graph models of the NKA that ensure thermodynamic consistency by enforcing conservation of mass, charge, and energy. A simplified 6-state model captures biophysics comparable to a 15-state model while remaining computationally tractable. Through detailed energetic analysis, we demonstrate that under physiological conditions for the mammalian cardiac cell, approximately 66% of the energy from ATP hydrolysis is stored as chemical energy in ion gradients, 12% as electrical energy in the membrane potential, and 22% is dissipated as heat, yielding an overall efficiency of ~78%. We investigate how the free energy of ATP hydrolysis ({Delta}GATP), intracellular Na+, and extracellular K+ affect NKA efficiency and activity. A critical threshold exists at {Delta}GATP {approx} -48 kJ/mol below which chemoelectrical transduction drops dramatically, consistent with NKA inhibition under ischemic conditions. The bond graph framework enables quantitative comparison of different NKA models and provides a systematic approach for analyzing ion pumps. SIGNIFICANCEThe sodium-potassium ATPase is one of the bodys most energy-consuming enzymes, yet its energetic efficiency and mechanisms remain incompletely understood. This study presents the first comprehensive energetic analysis using bond graph modeling, guaranteeing thermodynamic consistency. By demonstrating that simplified 6-state models capture essential energetic behaviors of complex 15-state models, we establish bond graphs as a powerful, tractable tool for energetic analysis, model comparison, model selection and validation. The bond graph approach can be applied to other transporters, offering a powerful tool for systems physiology and drug discovery.

biophysics↗

bioRxiv2026

The biophysical basis of enterocyte homeostasis

We present an approach to analysing cell homeostasis using a bond graph modelling approach that ensures that the conservation laws of physics (conservation of mass, charge, and energy, respectively) are satisfied for the interdependent biochemical, electrical, mechanical, and thermal energy storage mechanisms operating within the cell. We apply the bond graph approach to several cell membrane transport mechanisms and then consider how physics constrains intracellular electrolyte homeostasis for enterocytes (the epithelial absorptive cells of the gut). The model includes the electrogenic sodium-potassium ATPase pump (NKA), the glucose transporter (GLUT2), and an inwardly rectifying potassium channel, all in the basolateral membrane, and the electrogenic sodium-driven glucose transporter (SGLT1) in the apical membrane. Glycolysis converts the imported glucose to ATP to drive NKA. For specified levels of sodium, potassium, and glucose in the blood, the model demonstrates how enterocytes absorb sodium and glucose from the gut and transfer glucose to the blood while maintaining the membrane potential and homeostasis of intracellular sodium and potassium. The Gibbs free energy available from the ATP hydrolysis ensures that the cell operates as a sodium battery with a high external to internal ratio of sodium concentration in order to provide the energy for many other cellular transport processes. We show that the 3:2 stoichiometry of Na+/K+ exchange in NKA, coupled with 2:1 Na+/glucose cotransport in SGLT1, a 1:2:2 ratio between glucose consumption and ATP and water production in glycolysis, and K+ and glucose efflux through Kir and GLUT2, respectively, provides a balanced system that maintains homeostasis of intracellular Na+, K+, glucose, ATP and water, and homeostasis of the membrane potential, under varying levels of transport of glucose from the gut to the blood. We also show how the flux expressions for SLC transporters, ATPase pumps and ion channels can all be expressed in a consistent and thermodynamically valid way.

bioengineering↗

bioRxiv2025

High reelin expression can explain why the entorhinal cortex is a cradle for Alzheimer's disease

The entorhinal cortex (EC) plays a crucial role in memory functions. Long before the clinical symptoms of Alzheimers disease (AD) emerge, it has already undergone significant degeneration, making it a primary site for the onset of the disease. The reasons for this remain elusive. It was recently shown that in layer II neurons of the anterolateral entorhinal cortex (alECLII neurons), which are especially prone to display a very early increase in intracellular amounts of amyloid-{beta} peptide (A{beta}) and hyperphosphorylated tau protein (p-tau), the large glycoprotein reelin binds to A{beta}, suggesting that reelin functions as a sink for intracellular A{beta}. The expression of reelin is extraordinarily high in alECLII neurons compared to most other cortical neurons. Here, we show by computational modeling that, in a senescent physiology predisposing to frequent inflammation-driven A{beta}42 production bursts, the intracellular amount of A{beta}42-reelin complexes can accumulate to extraordinarily high levels in alECLII neurons compared to the vast majority of cortical neurons. This explains experimental data showing that intracellular accumulations of A{beta}42 positive material ranged from 20 to 80% of the total cytoplasmic volume in EC neurons from patients with sporadic AD. We also show that this extreme intracellular aggregation can cause the accumulation of detrimental hyperphosphorylated tau fragments. Thus, when exposed to recurrent AD-promoting stress, the exceptionally high expression of reelin in alECLII neurons appears to be instrumental in their early demise relative to other cortical neurons.

neuroscience↗

bioRxiv2024

Energy-based bond graph models of glucose transport with SLC transporters

The SLC (solute carrier) superfamily mediates the passive transport of small molecules across apical and basolateral cell membranes in nearly all tissues. In this paper we employ bond graph approaches to develop models of SLC transporters that conserve mass, charge and energy, respectively, and which can be parameterised for a specific cell and tissue type for which the experimental kinetic data is available. We show how analytic expressions that preserve thermodynamic consistency can be derived for a representative four- or six-state model, given reasonable assumptions associated with steady-state flux conditions. We present details on fitting parameters for SLC2A2 (a GLUT transporter) and SLC5A1 (an SGLT transporter) to experimental data and show how well the steady-state flux expressions match the full kinetic analysis. Since the bond graph approach will not be familiar to many readers, we provide a detailed description of the approach and illustrate its application to a number of familiar biophysical processes. SIGNIFICANCEPhysiological systems typically involve coupled mechanical, electrical and chemical processes, with energy acting as a universal currency across these domains. We propose a new visual representation for all components of these processes using bond graphs. Bringing all physical processes under one consistent framework greatly simplifies the task of understanding multiscale physiological processes. This energy-based framework, which is the 0D version of a more general 3D port-Hamiltonian theory, can be used to model all lumped parameter physiological processes. A small number of bond graph templates can be used to model all members of the large SLC transporter family, and reduced thermodynamically consistent steady-state flux models provide a useful simplification for many situations. Glucose transport is chosen here to illustrate the bond graph approach because it represents the first step in cell metabolic processes, where energy conservation needs to be a fundamental characteristic of quantitative models. Our future work on cell metabolism will build on the foundation established here.

biophysics↗

bioRxiv2021

The SPARC DRC: Building a resource for the autonomic nervous system community

The Data and Resource Center (DRC) of the NIH-funded SPARC program is developing databases, connectivity maps and simulation tools for the mammalian autonomic nervous system. The experimental data and mathematical models supplied to the DRC by the SPARC consortium are curated, annotated and semantically linked via a single knowledgebase. A data portal has been developed that allows discovery of data and models both via semantic search and via an interface that includes Google Map-like 2D flatmaps for displaying connectivity, and 3D anatomical organ scaffolds that provide a common coordinate framework for cross-species comparisons. We discuss examples that illustrate the data pipeline, which includes data upload, curation, segmentation (for image data), registration against the flatmaps and scaffolds, and finally display via the web portal, including the link to freely available online computational facilities that will enable neuromodulation hypotheses to be investigated by the autonomic neuroscience community and device manufacturers.

neuroscience↗

bioRxiv2018

Model Annotation and Discovery with the Physiome Model Repository

MotivationMathematics and physics based computational models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models encoded in computable form help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse; tools and guidelines to facilitate semantic description of mathematical models; and repositories in which to archive, share, and discover models. Biologists and clinicians can leverage these resources to investigate specific questions and hypotheses.\n\nResultsWe have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. In helping a user to discover relevant models this tool will provide users with suggestions for similar or alternative models they may wish to explore or utilize in their model based on the models they would like to use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research.\n\nAvailability and implementationhttps://github.com/dewancse/model-discovery-tool

bioinformatics↗