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Results for “Systems Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Synthetic multicolor antigen-stabilizable nanobody platform for intersectional labelling and functional imaging.

We present a synthetic toolkit of antigen-stabilizable fluorescent nanobodies (VIS-Fbs) spanning the entire visible spectrum from 450 nm to 660 nm. By engineering over twenty fluorescent proteins (FPs) and biosensors into eight nanobodies, we established a generalizable design of VIS-Fbs, which fluoresce brightly only upon binding to cognate antigens. Our synthetic approach includes constitutive, photoactivatable and photoswitchable FPs, and intensiometric FP-based biosensors. VIS-Fbs carrying biosensors enable simultaneous monitoring of two metabolites at confined locations, while FP-based VIS-Fbs targeting biosensors allow ratiometric functional imaging in the mouse brain. We further used VIS-Fbs to track endogenous {beta}-catenin dynamics in zebrafish embryos during normal development and under Wnt/{beta}-catenin signaling modulation. VIS-Fbs provide background-free visualization of intracellular proteins, multicolor detection of multiple antigens, and selective targeting of defined cell populations and compartments. This synthetic biology-driven platform enables precise studies of protein dynamics, cellular processes, and complex biological systems with high specificity and minimal background.

synthetic biology↗
bioRxiv2020

In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer. Accumulating evidence indicates the tumor microenvironment is highly associated with tumorigenesis through regulation of cellular physiology, signaling systems, and gene expression profiles of cancer cells. Yet the mechanisms by which the microenvironment evolves from normal pancreas architecture to precursor lesions and invasive cancer is poorly understood. Obtaining high-content and high-resolution information from a complex tumor microenvironment in large volumetric landscapes represents a key challenge in the field of cancer biology. To address this challenge, we established a novel method to reconstruct three-dimensional (3D) centimeter-scale tissues containing billions of cells from serially sectioned histological samples, utilizing deep learning approaches to recognize eight distinct tissue subtypes from hematoxylin and eosin stained sections at micrometer and single-cell resolution. Using samples from a range of normal, precancerous, and invasive pancreatic cancer tissue, we map in 3D modes of cancer invasion in the tumor microenvironment, and emphasize the need for further 3D quantification of biological systems.

cancer biology↗
bioRxiv2025

A ratiometric pH sensor for Gram-positive and Gram-negative bacteria

Fluctuating environments can lead to phenotypic heterogeneity within a monoclonal bacterial population, especially in response to antibiotics or the human immune system. Methods are required to analyze the physiology of single cells to understand how individual cells interact with their environment and adapt to pH stress. We report a ratiometric, fluorescent probe to sense cytoplasmic pH in bacteria. Our probes are based on hemicyanine dyes and are taken up into both Gram-positive and Gram-negative bacteria. The probes react preferentially with OH- over other nucleophiles in biological systems. The response to pH changes is reversible and rapid, allowing for the real-time tracking of pH fluctuations. The sensing of these probes was tuned to allow for monitoring fluctuations around neutrality and biologically relevant acidifications. These probes were validated for cytoplasmic pH sensing in Escherichia coli, Staphylococcus epidermidis, and a clinically isolated methicillin-resistant Staphylococcus aureus (MRSA) strain. Furthermore, the probes enabled the identification of pH-sensitive phenotypes and monitored phagocytosis of virulent clinical strains in immune cells. Our probes are a promising tool for detecting phenotypic heterogeneity within bacterial populations and may help unravel the physiological state of resistant or persistent strains of clinical relevance.

microbiology↗
bioRxiv2025

Hydrodynamic coupling drives bacterial flagellar synchrony

Synchronization is a fundamental phenomenon observed across a wide range of physical, chemical, and biological systems. Even under the low-Reynolds-number conditions that govern microbial motility, synchronization of rhythmic processes is considered essential for diverse activities ranging from single cells to populations, yet experimental evidence remains scarce. Here, we performed rotation measurements of microbeads attached to two truncated bacterial flagella and observed the emergence of phase synchronization between these nanoscale active motors. The resulting intermittent in-phase synchronization was analyzed using a hydrodynamic model incorporating elastic deformation of the flagella, showing that stronger hydrodynamic coupling promotes more stable phase-locking. This study experimentally demonstrates fluid-mediated biological synchronization using bacterial rotary machinery, deepening our understanding of the physical principles underlying rhythmic phenomena and self-organization in living systems.

biophysics↗
bioRxiv2020

Single-molecule light-sheet microscopy with local nanopipette delivery

Detection of single molecules in biological systems has rapidly increased in resolution over the past decade. However, delivery of single molecules has remained a challenge. Currently there is no effective method that can both introduce a precise amount of molecules onto or into a single cell at a defined position, and then image the cellular response. Here we have combined light sheet microscopy with local delivery, using a nanopipette, to accurately deliver individual proteins to a defined position. We call this method local delivery selective plane illumination microscopy (ldSPIM). ldSPIM uses a nanopipette and the ionic feedback current at the nanopipette tip to control the position from which molecules are delivered. The number of proteins delivered can be controlled by varying the voltage applied. For single-molecule detection, we implemented single-objective SPIM using a reflective atomic force microscopy cantilever to create a 2{micro}m thin sheet. Using this setup, we demonstrate that ldSPIM can deliver single fluorescently-labeled proteins onto the plasma membrane of HK293 cells or into the cytoplasm. Next, we deposited aggregates of amyloid-{beta}, which causes proteotoxicity relevant to Alzheimers disease, onto a single macrophage stably expressing a MyDD88-eGFP fusion construct. Whole-cell imaging in 3D mode enables live detection of MyDD88 accumulation and formation of MyDDosome signaling complexes, as a result of aggregate-induced triggering of toll-like receptor 4. Overall, we demonstrate a novel multifunctional imaging system capable of precise delivery of single proteins to a specific location on the cell surface or inside the cytoplasm and high-speed 3D detection at single-molecule resolution within live cells. Statement of SignificanceThis paper describes and validates a new method to study biological processes based on the controlled local delivery of molecules onto or into the cell, combined with single molecule imaging using light sheet microscopy. we not only demonstrate the instruments capability of delivering controlled numbers of molecules to a defined position, down to the level of single molecules, but also its potential in study of the triggering of the innate immune response by protein aggregates, a key process in the development of neurodegenerative diseases such as Alzheimers disease. The same approach could be applied to a wide range of other important biological processes allowing them to be followed in live cells in real-time, hence it will be of great interest to the biophysical community.

biophysics↗
bioRxiv2025

Distinct alpha-synuclein strains derived from Parkinson's disease patient tissues trigger differential inclusion pathology in a novel biosensor cell model

Background-Synuclein (Syn) can misfold and aggregate to form fibrillar {beta}-sheet-rich aggregates ("strains") that are phosphorylated (p-Syn) and deposited into intracellular inclusions in the brain, the pathological hallmark of synucleinopathies including Parkinsons disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Previously, we reported that seed amplification assays such as real-time quaking-induced conversion (RT-QuIC) amplifies and detects Syn strains from the patient skin. However, whether skin-derived Syn strains induce disease-specific pathological features in a biological system is unknown. MethodsWe generated a U251 human glioblastoma cell line expressing fluorescently tagged Syn carrying the PD-linked A53T mutation and Forster resonance energy transfer (FRET)-based U251 biosensor cells. Using fluorescence microscopy coupled with in situ detergent extraction, FRET-Flow cytometry and high-content confocal imaging, we examined the pathological burden and morphology of p-Syn inclusions seeded by RT-QuIC-amplified patient skin and brain Syn strains in Syn-expressing U251 cells, FRET-based Syn biosensor cells and Syn biosensor cell-derived neurons. ResultsU251 cells allow robust and rapid in situ detection of detergent-insoluble intracellular Syn inclusions triggered by exogenous Syn seeds. In U251 FRET-based biosensor cells, PD skin-amplified strains induce a greater pathological burden and distinct p-Syn inclusion morphology from PD brain-amplified and DLB skin-amplified strains. Inclusion morphology of DLB and MSA skin- and brain-amplified strains are comparable. Furthermore, skin-amplified Syn strains induce neuronal inclusions and cause degeneration of induced neurons reprogrammed from the U251 biosensor cells. Finally, biosensor cell-propagated PD skin Syn strains induce higher seeding activity measured by RT-QuIC than PD brain and DLB skin Syn strains, linking intracellular pathological burden to in vitro seeding activity. ConclusionsWe report the detection of distinct PD strains derived from patient skin and brain tissues that trigger unique pathological phenotypes in U251 Syn biosensor cells and cause degeneration of reprogrammed neurons from the same cell lineage. Moreover, DLB and MSA skin Syn strains mimic pathological features of their brain Syn strains in these biosensor cells. Therefore, the U251 Syn biosensor cell model is a robust tool to potentially discriminate PD and DLB synucleinopathies and to study Syn tissue- and strain-specific etiology and pathogenesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/646513v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@acf0a5org.highwire.dtl.DTLVardef@1eb0d17org.highwire.dtl.DTLVardef@a89c75org.highwire.dtl.DTLVardef@1b7eca4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗
bioRxiv2026

Separating biological signal from statistical artifact in trait trade-off inference

Trade-offs are commonly assumed when the statistical parameters used to define biological traits are negatively correlated. However, parameter correlations can emerge from the geometry of the model, rather than underlying biological signal. We characterize this phenomenon generally, and develop a framework to parse biological signal from statistical artifact in parameter correlations. Using the widely reported trade-off between thermal sensitivity and static upper critical thermal limit as a case study, we show that an extreme correlation between these parameters is expected even in the absence of a real trade-off. However, our method reveals that there is, in fact, a biological correlation underneath this spurious artifact. We confirm this finding, as independent genome-wide association analysis reveals genetic variation among trade-off phenotypes, but none corresponding to net thermal tolerance, suggesting that adaptive capacity to a warming climate may be severely limited. This particular trade-off is therefore biologically supported, illustrating how biological signal can be obscured by estimation artifact, thus evading correlation-based detection. We broaden our analysis, showing that artifact is present across biological systems and model types, although its manifestation may vary. Analytical results and simulations reveal how sampling design, model choice and parameterization govern the artifact, providing practical guidance for experimental planning. Our findings identify both a problem and a solution: correlations among model parameters cannot be interpreted biologically without first accounting for the structure imposed by the model and data, but genuine biological relationships can still be recovered when that structure is explicitly modeled.

evolutionary biology↗
bioRxiv2026

RAPTORGraph: graph-based pathway modeling for causal discovery in single-cell perturbations

Experiments involving the perturbation of individual cells are central to understanding cellular mechanisms and can accelerate drug discovery. Causal representation learning allows us to uncover the latent factors that regulate biological systems and predict the impact of novel perturbations. Unfortunately, existing methods fail to address intervention spillover in a closed-world setting where intervention targets are known a priori, such as in Perturb-seq experiments, due to their reliance on dense encoders. Furthermore, incorporating curated biological pathways into the model imposes a confirmatory bias, forcing it to explain the data through preexisting pathways and reducing the set of hypotheses the model can explore, while discarding novel signals that lie outside the annotated pathways. In this work, we introduce RAPTORGraph, a {beta}-variational autoencoder with a GraphPathway encoder that explicitly models complex gene-to-gene interactions within learned pathways. Moreover, our model's preconditioning isolates the influence of perturbed genes, yielding clean, single-node latent interventions required for identifiable causal discovery and eliminating spillover. Finally, we train the model on data preprocessed with optimal-transport alignment, which guarantees a well-defined mapping between control and perturbed samples and further stabilizes the learned latent representations. We demonstrate that RAPTORGraph improves state-of-the-art performance on downstream analyses of unseen perturbations, such as non-additive interactions, while performing competitively on objective metrics, such as mean squared error and maximum mean discrepancy. The source code is available at https://github.com/sXperfect/RAPTORGraph.

bioinformatics↗
bioRxiv2026

Alignment-free phylogenetic inference via hyperbolic protein language models

Conventional phylogenetic methods rely on multiple sequence alignments which are computationally intensive and often fail for highly divergent lineages. Here, we introduce LucaPhylo, an alignment-free framework that infers evolutionary relationships directly from unaligned sequences. Through a cascaded learning strategy LucaPhylo integrates protein language models with hyperbolic geometry, a representation space naturally suited to hierarchical branching, to capture deep evolutionary constraints without explicit homology matching. Using highly divergent RNA virosphere as a test case, LucaPhylo places unaligned sequences into phylogenetic trees with an accuracy comparable to leading alignment-based tree construction tools, while retaining divergent sequences that conventional pipelines frequently discard. It further enables the integration of divergent viral lineages into phylogenetic trees, thereby expanding the evolutionary landscape of RNA viruses. Together, LucaPhylo establishes an AI-driven, alignment-free paradigm for phylogenetic inference and provides a robust computational foundation for resolving deep evolutionary relationships among RNA viruses and other biological systems.

evolutionary biology↗
bioRxiv2024

Novel insights into IKur modulation by Lgi3-4: Implications in atrial fibrillation

BackgroundPatients with atrial fibrillation (AF) exhibit a reduction in the ultrarapid outward potassium current (IKur) conducted by KV1.5 channels. Ion channels are closely modulated by regulatory subunits, forming macromolecular complexes known as channelosomes. One such regulatory family is the leucine-rich glioma-inactivated protein family (Lgi1-4), which has been shown to interact with KV1, modifying their trafficking and/or biophysical properties in neurons. However, the expression and impact of these proteins in the heart is still unknown. We investigated the role of Lgi3-4 proteins in cardiac electrophysiology, focusing specifically on IKur, and their potential contribution to the pathophysiology of AF. MethodsWe used three complementary biological systems, including heterologous COS-7, HEK297 and CHO cells, AAV-mediated cardiac-specific Lgi4 gene transfer in mice (Lgi4 mice), and human samples from patients in sinus rhythm and AF. Our multidisciplinary approach included immunolocalization, patch clamping, surface ECG, transvenous catheter-mediated intracardiac stimulation, and molecular biology techniques. ResultsOnly Lgi3 and Lgi4 were expressed in the human heart. In human atrial tissue and heterologous cells, Lgi3 and Lgi4 interacted with KV1.5 channels. In HEK293 cells, Lgi3-4 impaired KV1.5/KV{beta} association, partially reversing the KV{beta}-induced N-type inactivation and reducing IKur amplitude. On surface ECG, the QRS interval was prolonged, and impulse conduction was impaired in cardiac-specific Lgi4 mice compared with control. In isolated ventricular cardiomyocytes from Lgi4 mice, early action potential repolarization was prolonged compared to control cardiomyocytes. These results correlated with the reduced KV1.5 membrane expression and IKur density observed in Lgi4 cardiomyocytes and HEK293 cells. Notably, Lgi4 protein expression was lower in atrial tissue from patients with AF than sinus rhythm patients. The reduction in Lgi4 protein levels in AF was also associated with an altered colocalization with KV1.5 channels, suggesting potential disruptions in their functional interactions. ConclusionsLgi3-4 proteins are new components of the KV1.5 channelosome. They modulate IKur by interfering with KV1.5 interaction with the KV{beta} subunit. Importantly, Lgi4 is dysregulated differently in paroxysmal versus permanent AF. The results improved the understanding of this most common type of arrhythmia and identified Lgi proteins as a new potential target for treatment. NOVELTY AND SIGNIFICANCEWhat is known? O_LILeucine-rich glioma-inactivated protein family (Lgi1-4) exert an important role in the nervous system and neurological diseases. In neurons, certain Lgi proteins interact with KV1 channels, modifying their trafficking and/or biophysical properties. C_LIO_LIIn cardiomyocytes, the activation of KV1.5 channels generates the ultrarapid outward potassium current (IKur), which is essential for the initial phase of human atrial repolarization, and it is dysregulated in AF. C_LIO_LIChanges in the properties or functional expression of some KV1.5 interacting proteins have crucial pathophysiological consequences. C_LI What new information does this article contribute? O_LIWe demonstrate that Lgi3-4 are novel components of KV1.5 channelosome, modulating IKur and hence human atrial electrophysiology. Lgi3-4 proteins decrease IKur by interfering with the interaction between KV1.5 and KV{beta} subunits. C_LIO_LIThe decrease in IKur in cardiac-specific mouse model expressing Lgi4 slows the early repolarization in the action potential, as well as produce electrophysiological changes in the surface ECG and the cardiac conduction system. C_LIO_LILgi4 is dysregulated differently in paroxysmal (PX) versus permanent (PM) AF, thus shedding light into the mechanisms underlying this cardiac arrhythmia. C_LI

physiology↗
bioRxiv2024

Optimal network sizes for most robust Turing patterns

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, surprisingly consisting only of a handful of molecular species, thus significantly increasing their identifiability in biological systems. This optimal size emerges from a tradeoff between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.

biophysics↗
bioRxiv2017

The application of zeta diversity as a continuous measure of compositional change in ecology

Zeta diversity provides the average number of shared species across n sites (or shared operational taxonomic units (OTUs) across n cases). It quantifies the variation in species composition of multiple assemblages in space and time to capture the contribution of the full suite of narrow, intermediate and wide-ranging species to biotic heterogeneity. Zeta diversity was proposed for measuring compositional turnover in plant and animal assemblages, but is equally relevant for application to any biological system that can be characterised by a row by column incidence matrix. Here we illustrate the application of zeta diversity to explore compositional change in empirical data, and how observed patterns may be interpreted. We use 10 datasets from a broad range of scales and levels of biological organisation - from DNA molecules to microbes, plants and birds - including one of the original data sets used by R.H. Whittaker in the 1960s to express compositional change and distance decay using beta diversity. The applications show (i) how different sampling schemes used during the calculation of zeta diversity may be appropriate for different data types and ecological questions, (ii) how higher orders of zeta may in some cases better detect shifts, transitions or periodicity, and importantly (iii) the relative roles of rare versus common species in driving patterns of compositional change. By exploring the application of zeta diversity across this broad range of contexts, our goal is to demonstrate its value as a tool for understanding continuous biodiversity turnover and as a metric for filling the empirical gap that exists on spatial or temporal change in compositional diversity.

ecology↗
bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗
bioRxiv2018

Identifying Mechanisms of Regulation to Model Carbon Flux During Heat Stress And Generate Testable Hypotheses

Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described method used to identify single nucleotide polymorphism (SNPs) associated with metabolic changes (Gieger et al., 2008). We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and hypothesizes novel biology relevant to the heat stress response.

genomics↗
bioRxiv2020

Navigating through chemical space and evolutionary time across the Australian continent in plant genus Eremophila

Eremophila is the largest genus in the plant tribe Myoporeae (Scrophulariaceae) and exhibits incredible morphological diversity across the Australian continent. The Australian Aboriginal Peoples recognize many Eremophila species as important sources of traditional medicine, the most frequently used plant parts being the leaves. Recent phylogenetic studies have revealed complex evolutionary relationships between Eremophila and related genera in the tribe. Unique and structurally diverse metabolites, particularly diterpenoids, are also a feature of plants in this group. To assess the full dimension of the chemical space of the tribe Myoporeae, we investigated the metabolite diversity in a chemo-evolutionary framework applying a combination of molecular phylogenetic and state-of-the-art computational metabolomics tools to build a dataset involving leaf samples from a total of 291 specimens of Eremophila and allied genera. The chemo-evolutionary relationships are expounded into a systematic context by integration of information about leaf morphology (resin and hairiness), environmental factors (pollination and geographical distribution) and medicinal properties (traditional medicinal uses and antibacterial studies) augmenting our understanding of complex interactions in biological systems.

plant biology↗
bioRxiv2021

Multi-modal imaging reveals dynamic interactions of Staphylococcus aureus within human neutrophils

Staphylococcus aureus is an important human pathogen that causes a wide range of infections. Neutrophils are an essential component of our innate immune system and understanding S. aureus-neutrophil interactions on a sub-cellular level is crucial to developing new therapeutic strategies to promote immunity during S. aureus infections. To this end we have developed a multi-modal imaging platform capable of following host-pathogen processes in biological systems, this is achieved by switching imaging modalities between a low photo-toxicity and low resolution imaging modality through an increasing illumination intensity to achieve live super-resolution imaging. This novel imaging platform was applied to the study of human neutrophils infected by S. aureus. We show that we can image different infection stages of S. aureus in live neutrophils with super resolution microscopy. We see evidence of binary fission occurring in intracellular S. aureus within a neutrophil.

biophysics↗
bioRxiv2024

Mapping the human hematopoietic stem and progenitor cell hierarchy through integrated single-cell proteomics and transcriptomics

Single-cell transcriptomics (scRNA-seq) has enabled the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, since proteins are the main functional entities in cells, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leverage recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate the first scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2,500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identify proteins that are important for stem cell quiescence, which were not indicated by their mRNA transcripts, and demonstrate functional expression covariance during differentiation that is only detectable on protein level. Finally, we show that modeling translation dynamics can infer cell progression during differentiation and explain 45% more protein variation from mRNA than linear correlation. Our work serves as a framework for future single-cell multi-omics studies across biological systems.

cell biology↗
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↗