Biology subjects

Nickerson, D. P.

Publications and source records attributed to Nickerson, D. P..

17 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

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

FAIRification of computational models in biology

Computational models are essential for studying complex systems which, particularly in clinical settings, need to be quality-approved and transparent. To enhance the communication of a models features and capabilities, we propose an adaptation of the Findability, Accessibility, Interoperability and Reusability (FAIR) indicators published by the Research Data Alliance to assess models encoded in domain-specific standards, such as those established by COMBINE. The assessments guide FAIRification and add value to models.

systems biology↗

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↗

bioRxiv2022

CASBERT: BERT-Based Retrieval for Compositely Annotated Biosimulation Model Entities

Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of RDF with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is not suitable for most repository users who explore biosimulation models freely without adequate knowledge regarding ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repositorys contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list-like structure. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The simple list-like structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.

bioinformatics↗

bioRxiv2022

SBML to bond graphs: from conversion to composition

The Systems Biology Markup Language (SBML) is a popular software-independent XML-based format for describing models of biological phenomena. The BioModels Database is the largest online repository of SBML models. Several tools and platforms are available to support the reuse and composition of SBML models. However, these tools do not explicitly assess whether models are physically plausibile or thermodynamically consistent. This often leads to ill-posed models that are physically impossible, impeding the development of realistic complex models in biology. Here, we present a framework that can automatically convert SBML models into bond graphs, which imposes energy conservation laws on these models. The new bond graph models are easily mergeable, resulting in physically plausible coupled models. We illustrate this by automatically converting and coupling a model of pyruvate distribution to a model of the pentose phosphate pathway. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/493355v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16f5998org.highwire.dtl.DTLVardef@1630beborg.highwire.dtl.DTLVardef@1a37309org.highwire.dtl.DTLVardef@1d582df_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA framework to convert suitable SBML models of biochemical networks into bond graphs is developed. C_LIO_LIThe framework is applied here to two interconnecting models of metabolism pathways. C_LIO_LIWe automatically integrate the generated bond graph modules. C_LIO_LIWe qualitatively illustrate the functionality of the composed model. C_LI

bioengineering↗

bioRxiv2021

A modular and reusable model of epithelial transport in the proximal convoluted tubule

We review a collection of published renal epithelial transport models, from which we build a consistent and reusable mathematical model able to reproduce many observations and predictions from the literature. The flexible modular model we present here can be adapted to specific configurations of epithelial transport, and in this work we focus on transport in the proximal convoluted tubule of the renal nephron. Our mathematical model of the epithelial proximal convoluted tubule describes the cellular and subcellular mechanisms of the transporters, intracellular buffering, solute fluxes, and other processes. We provide free and open access to the Python implementation to ensure our multiscale proximal tubule model is accessible; enabling the reader to explore the model through setting their own simulations, reproducibility tests, and sensitivity analyses.

bioengineering↗

bioRxiv2021

A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each models components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models. Author summaryDetailed, multi-scale computational models bridging from biomolecular processes to entire organs and bodies have the potential to revolutionise medicine by enabling personalised treatments. One of the key challenges to achieving these models is connecting together the vast number of isolated biosimulation models into a coherent whole. Using recent advances in both modelling techniques and biological standards in the scientific community, we developed an approach to integrate and compose models in a physics-based environment. This provides significant advantages, including the automation of model composition and post-model-composition adjustments. We anticipate that our approach will enable the faster development of realistic and accurate models to understand complex biological systems.

bioengineering↗

bioRxiv2021

SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models

The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.

systems biology↗

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↗

bioRxiv2021

Hierarchical semantic composition of biosimulation models using bond graphs

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition. Author summaryBiological and physiological systems usually involve multiple underlying processes, mechanisms, structures, and phenomena, referred to here as sub-systems. Modelling the whole system every time from scratch requires a huge amount of effort. An alternative is to model each sub-system in a modular fashion, i.e., containing meaningful interfaces for connecting to other modules. Such modules are readily combined to produce a whole-system model. For the combined model to be consistent, modules must be described using the same modelling scheme. One way to achieve this is to use energy-based models that are consistent with the conservation laws of physics. Here, we present an approach that achieves this using bond graphs, which allows modules to be combined faster and more efficiently. First, physically plausible modules are generated using a small number of template modules. Then a meaningful interface is added to each module to automate connection. This approach is illustrated by applying this method to an existing model of the circulatory system and verifying the results against the reference model.

bioengineering↗

bioRxiv2020

Gatekeeper helix activates Golgi SM protein Sly1 and directly mediates close-range vesicle tethering

The essential Golgi protein Sly1 is a member of the SM (Sec1/mammalian Unc-18) family of SNARE chaperones. Sly1 was originally identified through gain-of-function alleles that bypass requirements for diverse vesicle tethering factors. Employing genetic analyses and chemically defined reconstitutions of ER-Golgi fusion, we discovered that a loop conserved among Sly1 family members is not only autoinhibitory, but also acts as a positive effector. An amphipathic helix within the loop directly binds high-curvature membranes; membrane binding is required for relief of Sly1 autoinhibition and allows Sly1 to directly tether incoming vesicles to the Qa-SNARE on the target organelle. The SLY1-20 allele bypasses requirements for diverse tethering factors but loses this functionality if Sly1 membrane binding is impaired. We propose that long-range tethers, including Golgins and multisubunit tethering complexes, hand off vesicles to Sly1, which then tethers at close range to activate SNARE assembly and fusion in the early secretory pathway.

cell biology↗

bioRxiv2019

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g. variables, equations, or entire models) by utilising the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first chunked into phrases and annotated against ontology classes and predicates utilising different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMEDs approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries. Comparison of NLIMEDs behaviour against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

bioinformatics↗

bioRxiv2019

Epithelial Modelling Platform: A Tool for Model Discovery and Assembly with the Physiome Model Repository

In this paper we present a web-based platform enabling scientists to construct a novel epithelial transport model to investigate their hypotheses, aided by building on existing models discovered in the Physiome Model Repository (PMR). We have comprehensively annotated a cohort of epithelial transport models deposited in the PMR as a seeding collection of building blocks that are freely available for reuse. On the platform, users are able to semantically display models for visualization, graphical editing, and model assembly. In addition, we leverage web services from the European Bioinformatics Institute (EBI) to help rank similar models based on the suggestions provided by the platform. In addition to potential use in biomedical and clinical research, novice modellers could use our platform as a learning tool. The source code and links to a live demonstration of the platform are available at https://github.com/dewancse/ epithelial-modelling-platform.

bioinformatics↗

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↗

bioRxiv2017

Sec17 (α-SNAP) And An SM-Tethering Complex Control The Outcome Of SNARE Zippering In Vitro And In Vivo

Zippering of SNARE complexes spanning docked membranes is essential for most intracellular fusion events. Here we explore how SNARE regulators operate on discrete zippering states. The formation of a metastable trans-complex, catalyzed by HOPS and its SM subunit Vps33, is followed by subsequent zippering transitions that increase the probability of fusion. Operating independently of Sec18 catalysis, Sec17 either inhibits or stimulates SNARE-mediated fusion. If HOPS or Vps33 are absent, Sec17 inhibits fusion at an early stage. Thus, HOPS and Vps33 accelerate SNARE zippering, particularly in the presence of otherwise inhibitory Sec17. Once SNAREs are partially-zipped, Sec17 promotes fusion in either the presence or absence of HOPS -- but with faster kinetics when HOPS is absent. Our data further indicate that Sec17 promotes fusion both through its direct penetration of the membrane and by enhancing C-terminal SNARE zippering. In a working model, the interplay among Sec17, Sec18, SMs, and SNARE zippering can explain why SM proteins are indispensable for SNARE-mediated fusion in vivo.\n\nImpact statementSec17 is shown to have divergent effects on pre-fusion SNARE complex activity, depending on the state of SNARE zippering. HOPS, an SM-tether complex, controls the outcome of Sec17-SNARE engagement. The results suggest a coherent working model for SM activity in vivo.

biochemistry↗