Chronic intermittent hypoxia remodels catecholaminergic nerve innervation in mouse atria.

Chronic intermittent hypoxia (CIH, a model for sleep apnoea) is a major risk factor for several cardiovascular diseases. Autonomic imbalance (sympathetic overactivity and parasympathetic withdrawal) has emerged as a causal contributor of CIH-induced cardiovascular disease. Previously, we showed that CIH remodels the parasympathetic pathway. However, whether CIH induces remodelling of the cardiac sympathetic innervation remains unknown. Mice (male, C57BL/6J, 2-3 months) were exposed to either room air (RA, 21% O2 ) or CIH (alternating 21% and 5.7% O2 , every 6 min, 10 h day-1 ) for 8-10 weeks. Flat-mounts of their left and right atria were immunohistochemically labelled for tyrosine hydroxylase (TH, a sympathetic marker). Using a confocal microscope (or fluorescence microscope) and Neurlocudia 360 digitization and tracing system, we scanned both the left and right atria and quantitatively analysed the sympathetic axon density in both groups. The segmentation data was mapped onto a 3D mouse heart scaffold. Our findings indicated that CIH significantly remodelled the TH immunoreactive (-IR) innervation of the atria by increasing its density at the sinoatrial node, the auricles and the major veins attached to the atria (P < 0.05, n = 7). Additionally, CIH increased the branching points of TH-IR axons and decreased the distance between varicosities. Abnormal patterns of TH-IR axons around intrinsic cardiac ganglia were also found following CIH. We postulate that the increased sympathetic innervation may further amplify the effects of enhanced CIH-induced central sympathetic drive to the heart. Our work provides an anatomical foundation for the understanding of CIH-induced autonomic imbalance. KEY POINTS: Chronic intermittent hypoxia (CIH, a model for sleep apnoea) causes sympathetic overactivity, cardiovascular remodelling and hypertension. We determined the effect of CIH on sympathetic innervation of the mouse atria. In vivo CIH for 8-10 weeks resulted in an aberrant axonal pattern around the principal neurons within intrinsic cardiac ganglia and an increase in the density, branching point, tortuosity of catecholaminergic axons and atrial wall thickness. Utilizing mapping tool available from NIH (SPARC) Program, the topographical distribution of the catecholaminergic innervation of the atria were integrated into a novel 3D heart scaffold for precise anatomical distribution and holistic quantitative comparison between normal and CIH mice. This work provides a unique neuroanatomical understanding of the pathophysiology of CIH-induced autonomic remodelling.

A novel modular modeling approach for understanding different electromechanics between left and right heart in rat.

While ion channels and transporters involved in excitation-contraction coupling have been linked and constructed as comprehensive computational models, validation of whether each individual component of a model can be reused has not been previously attempted. Here we address this issue while using a novel modular modeling approach to investigate the underlying mechanism for the differences between left ventricle (LV) and right ventricle (RV). Our model was developed from modules constructed using the module assembly principles of the CellML model markup language. The components of three existing separate models of cardiac function were disassembled as to create smaller modules, validated individually, and then the component parts were combined into a new integrative model of a rat ventricular myocyte. The model was implemented in OpenCOR using the CellML standard in order to ensure reproducibility. Simulated action potential (AP), Ca2+ transient, and tension were in close agreement with our experimental measurements: LV AP showed a prolonged duration and a more prominent plateau compared with RV AP; Ca2+ transient showed prolonged duration and slow decay in LV compared to RV; the peak value and relaxation of tension were larger and slower, respectively, in LV compared to RV. Our novel approach of module-based mathematical modeling has established that the ionic mechanisms underlying the APs and Ca2+ handling play a role in the variation in force production between ventricles. This simulation process also provides a useful way to reuse and elaborate upon existing models in order to develop a new model.

A method for investigating spatiotemporal growth patterns at cell and tissue levels during C-looping in the embryonic chick heart.

We developed a workflow using multi-scale and multi-disciplinary experimental and computational approaches to analyze C-looping (the first phase of cardiac looping) of the chick across four developing hearts. We provide the first 3D datasets for the C-looping heart with cell to organism level information, including datasets of heart images and segmented myocardial cells within the heart. We used these datasets to investigate, as a proof-of-concept, the differential spatiotemporal patterns of growth at both the cellular and tissue levels, and demonstrate how geometrical changes of C-looping at the tissue level are linked to growth features at the cellular level. Our methodological pipeline provides preliminary results for qualitative and quantitative evidence of various cellular and tissue features as potential candidates regarding the mechanism of C-looping. This pipeline can be used and extended in future studies to include larger specimen samples for detailed analyses of, and potentially new insights into, cardiac C-looping.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome Project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science.

Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics.

The value of digital twins for prototyping controllers or interventions in a sandbox environment are well-established in engineering and physics. However, this is challenging for biophysics trying to seamlessly compose models of multiple spatial and temporal scale behavior into the digital twin. Two challenges stand out as constraining progress: (i) ensuring physical consistency of conservation laws across composite models and (ii) drawing useful and timely clinical and scientific information from conceptually and computationally complex models. Challenge (i) can be robustly addressed with bondgraphs. However, challenge (ii) is exacerbated using this approach. The complexity question can be looked at from multiple angles. First from the perspective of discretizations that reflect underlying biophysics (functional tissue units) and secondly by exploring maximum entropy as the principle guiding multicellular biophysics. Statistical mechanics, long applied to understanding emergent phenomena from atomic physics, coupled with the observation that cellular architecture in tissue is orchestrated by biophysical constraints on metabolism and communication, shows conceptual promise. This architecture along with cell specific properties can be used to define tissue specific network motifs associated with energetic contributions. Complexity can be addressed based on energy considerations and finding mean measures of dependent variables. A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.

Computational Modelling of Glucose Uptake by SGLT1 and Apical GLUT2 in the Enterocyte.

It has been suggested that glucose absorption in the small intestine depends on both constitutively expressed SGLT1 and translocated GLUT2 in the brush border membrane, especially in the presence of high levels of luminal glucose. Here, we present a computational model of non-isotonic glucose uptake by small intestinal epithelial cells. The model incorporates apical uptake via SGLT1 and GLUT2, basolateral efflux into the blood via GLUT2, and cellular volume changes in response to non-isotonic conditions. The dependence of glucose absorption on luminal glucose, blood flow rate, and inlet blood glucose concentration is studied. Uptake via apical GLUT2 is found to be sensitive to all these factors. Under a range of conditions, the maximum apical GLUT2 flux is about half of the SGLT1 flux and is achieved at high luminal glucose (> 50 mM), high blood flow rates, and low inlet blood concentrations. In contrast, SGLT1 flux is less sensitive to these factors. When luminal glucose concentration is less than 10 mM, apical GLUT2 serves as an efflux pathway for glucose to move from the blood to the lumen. The model results indicate that translocation of GLUT2 from the basolateral to the apical membrane increases glucose uptake into the cell; however, the reduction of efflux capacity results in a decrease in net absorption. Recruitment of GLUT2 from a cytosolic pool elicits a 10-20% increase in absorption for luminal glucose levels in the a 20-100 mM range. Increased SGLT1 activity also leads to a roughly 20% increase in absorption. A concomitant increase in blood supply results in a larger increase in absorption. Increases in apical glucose transporter activity help to minimise cell volume changes by reducing the osmotic gradient between the cell and the lumen.

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.

Our natural "makeup" reveals more than it hides: Modeling the skin and its microbiome.

Skin is our primary interface with the environment. A structurally and functionally complex organ that hosts a dynamic ecosystem of microbes, and synthesizes many compounds that affect our well-being and psychosocial interactions. It is a natural platform of signal exchange between internal organs, skin resident microbes, and the environment. These interactions have gained a great deal of attention due to the increased prevalence of atopic diseases, and the co-occurrence of multiple allergic diseases related to allergic sensitization in early life. Despite significant advances in experimentally characterizing the skin, its microbial ecology, and disease phenotypes, high-levels of variability in these characteristics even for the same clinical phenotype are observed. Addressing this variability and resolving the relevant biological processes requires a systems approach. This review presents some of our current understanding of the skin, skin-immune, skin-neuroendocrine, skin-microbiome interactions, and computer-based modeling approaches to simulate this ecosystem in the context of health and disease. The review highlights the need for a systems-based understanding of this sophisticated ecosystem. This article is categorized under: Infectious Diseases > Computational Models.

Model annotation and discovery with the Physiome Model Repository.

BACKGROUND: Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models 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. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. RESULTS: We 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. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. CONCLUSION: 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. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

Computational Modeling of Glucose Uptake in the Enterocyte.

Absorption of glucose across the epithelial cells of the small intestine is a key process in human nutrition and initiates signaling cascades that regulate metabolic homeostasis. Validated and predictive mathematical models of glucose transport in intestinal epithelial cells are essential for interpreting experimental data, generating hypotheses, and understanding the contributions of and interactions between transport pathways. Here we report on the development of such a model that, in contrast to existing models, incorporates mechanistic descriptions of all relevant transport proteins and is implemented in the CellML framework. The model is validated against experimental and simulation data from the literature. It is then used to elucidate the relative contributions of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2) proteins in published measurements of glucose absorption from human intestinal epithelial cell lines. The model predicts that the contribution of SGLT1 dominates at low extracellular glucose concentrations (<20 mM) and short exposure times (<60 s) while the GLUT2 contribution is more significant at high glucose concentrations and long durations. Implementation in CellML permitted a modular structure in which the model was composed by reusing existing models of the individual transporters. The final structure also permits transparent changes of the model components and parameter values in order to facilitate model reuse, extension, and customization (for example, to simplify, or add complexity to specific transporter/pathway models, or reuse the model as a component of a larger framework) and carry out parameter sensitivity studies.

Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations.

We propose a detailed CellML model of the human cerebral circulation that runs faster than real time on a desktop computer and is designed for use in clinical settings when the speed of response is important. A lumped parameter mathematical model, which is based on a one-dimensional formulation of the flow of an incompressible fluid in distensible vessels, is constructed using a bond graph formulation to ensure mass conservation and energy conservation. The model includes arterial vessels with geometric and anatomical data based on the ADAN circulation model. The peripheral beds are represented by lumped parameter compartments. We compare the hemodynamics predicted by the bond graph formulation of the cerebral circulation with that given by a classical one-dimensional Navier-Stokes model working on top of the whole-body ADAN model. Outputs from the bond graph model, including the pressure and flow signatures and blood volumes, are compared with physiological data.

Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research.

The role of computational modeling for biomechanics research and related clinical care will be increasingly prominent. The biomechanics community has been developing computational models routinely for exploration of the mechanics and mechanobiology of diverse biological structures. As a result, a large array of models, data, and discipline-specific simulation software has emerged to support endeavors in computational biomechanics. Sharing computational models and related data and simulation software has first become a utilitarian interest, and now, it is a necessity. Exchange of models, in support of knowledge exchange provided by scholarly publishing, has important implications. Specifically, model sharing can facilitate assessment of reproducibility in computational biomechanics and can provide an opportunity for repurposing and reuse, and a venue for medical training. The community's desire to investigate biological and biomechanical phenomena crossing multiple systems, scales, and physical domains, also motivates sharing of modeling resources as blending of models developed by domain experts will be a required step for comprehensive simulation studies as well as the enhancement of their rigor and reproducibility. The goal of this paper is to understand current perspectives in the biomechanics community for the sharing of computational models and related resources. Opinions on opportunities, challenges, and pathways to model sharing, particularly as part of the scholarly publishing workflow, were sought. A group of journal editors and a handful of investigators active in computational biomechanics were approached to collect short opinion pieces as a part of a larger effort of the IEEE EMBS Computational Biology and the Physiome Technical Committee to address model reproducibility through publications. A synthesis of these opinion pieces indicates that the community recognizes the necessity and usefulness of model sharing. There is a strong will to facilitate model sharing, and there are corresponding initiatives by the scientific journals. Outside the publishing enterprise, infrastructure to facilitate model sharing in biomechanics exists, and simulation software developers are interested in accommodating the community's needs for sharing of modeling resources. Encouragement for the use of standardized markups, concerns related to quality assurance, acknowledgement of increased burden, and importance of stewardship of resources are noted. In the short-term, it is advisable that the community builds upon recent strategies and experiments with new pathways for continued demonstration of model sharing, its promotion, and its utility. Nonetheless, the need for a long-term strategy to unify approaches in sharing computational models and related resources is acknowledged. Development of a sustainable platform supported by a culture of open model sharing will likely evolve through continued and inclusive discussions bringing all stakeholders at the table, e.g., by possibly establishing a consortium.

Roadmap for cardiovascular circulation model.

Computational models of many aspects of the mammalian cardiovascular circulation have been developed. Indeed, along with orthopaedics, this area of physiology is one that has attracted much interest from engineers, presumably because the equations governing blood flow in the vascular system are well understood and can be solved with well-established numerical techniques. Unfortunately, there have been only a few attempts to create a comprehensive public domain resource for cardiovascular researchers. In this paper we propose a roadmap for developing an open source cardiovascular circulation model. The model should be registered to the musculo-skeletal system. The computational infrastructure for the cardiovascular model should provide for near real-time computation of blood flow and pressure in all parts of the body. The model should deal with vascular beds in all tissues, and the computational infrastructure for the model should provide links into CellML models of cell function and tissue function. In this work we review the literature associated with 1D blood flow modelling in the cardiovascular system, discuss model encoding standards, software and a model repository. We then describe the coordinate systems used to define the vascular geometry, derive the equations and discuss the implementation of these coupled equations in the open source computational software OpenCMISS. Finally, some preliminary results are presented and plans outlined for the next steps in the development of the model, the computational software and the graphical user interface for accessing the model.

Modular modelling with Physiome standards.

KEY POINTS: The complexity of computational models is increasing, supported by research in modelling tools and frameworks. But relatively little thought has gone into design principles for complex models. We propose a set of design principles for complex model construction with the Physiome standard modelling protocol CellML. By following the principles, models are generated that are extensible and are themselves suitable for reuse in larger models of increasing complexity. We illustrate these principles with examples including an architectural prototype linking, for the first time, electrophysiology, thermodynamically compliant metabolism, signal transduction, gene regulation and synthetic biology. The design principles complement other Physiome research projects, facilitating the application of virtual experiment protocols and model analysis techniques to assist the modelling community in creating libraries of composable, characterised and simulatable quantitative descriptions of physiology. ABSTRACT: The ability to produce and customise complex computational models has great potential to have a positive impact on human health. As the field develops towards whole-cell models and linking such models in multi-scale frameworks to encompass tissue, organ, or organism levels, reuse of previous modelling efforts will become increasingly necessary. Any modelling group wishing to reuse existing computational models as modules for their own work faces many challenges in the context of construction, storage, retrieval, documentation and analysis of such modules. Physiome standards, frameworks and tools seek to address several of these challenges, especially for models expressed in the modular protocol CellML. Aside from providing a general ability to produce modules, there has been relatively little research work on architectural principles of CellML models that will enable reuse at larger scales. To complement and support the existing tools and frameworks, we develop a set of principles to address this consideration. The principles are illustrated with examples that couple electrophysiology, signalling, metabolism, gene regulation and synthetic biology, together forming an architectural prototype for whole-cell modelling (including human intervention) in CellML. Such models illustrate how testable units of quantitative biophysical simulation can be constructed. Finally, future relationships between modular models so constructed and Physiome frameworks and tools are discussed, with particular reference to how such frameworks and tools can in turn be extended to complement and gain more benefit from the results of applying the principles.

The virtual esophagus: investigating esophageal functions in silico.

Esophageal and gastroesophageal junction (GEJ) diseases are highly prevalent worldwide and are a significant socioeconomic burden. Recently, applications of multiscale mathematical models of the upper gastrointestinal tract have gained attention. These in silico investigations can contribute to the development of a virtual esophagus modeling framework as part of the larger GIome and Physiome initiatives. There are also other modeling investigations that have potential screening and treatment applications. These models incorporate detailed anatomical models of the esophagus and GEJ, tissue biomechanical properties and bolus transport, sensory properties, and, potentially, bioelectrical models of the neural and myogenic pathways of esophageal and GEJ functions. A next step is to improve the integration between the different components of the virtual esophagus, encoding standards, and simulation environments to perform more realistic simulations of normal and pathophysiological functions. Ultimately, the models will be validated and will provide predictive evaluations of the effects of novel endoscopic, surgical, and pharmaceutical treatment options and will facilitate the clinical translation of these treatments.

A physiome interoperability roadmap for personalized drug development.

The goal of developing therapies and dosage regimes for characterized subgroups of the general population can be facilitated by the use of simulation models able to incorporate information about inter-individual variability in drug disposition (pharmacokinetics), toxicity and response effect (pharmacodynamics). Such observed variability can have multiple causes at various scales, ranging from gross anatomical differences to differences in genome sequence. Relevant data for many of these aspects, particularly related to molecular assays (known as '-omics'), are available in online resources, but identification and assignment to appropriate model variables and parameters is a significant bottleneck in the model development process. Through its efforts to standardize annotation with consequent increase in data usability, the human physiome project has a vital role in improving productivity in model development and, thus, the development of personalized therapy regimes. Here, we review the current status of personalized medicine in clinical practice, outline some of the challenges that must be overcome in order to expand its applicability, and discuss the relevance of personalized medicine to the more widespread challenges being faced in drug discovery and development. We then review some of (i) the key data resources available for use in model development and (ii) the potential areas where advances made within the physiome modelling community could contribute to physiologically based pharmacokinetic and physiologically based pharmacokinetic/pharmacodynamic modelling in support of personalized drug development. We conclude by proposing a roadmap to further guide the physiome community in its on-going efforts to improve data usability, and integration with modelling efforts in the support of personalized medicine development.

OpenCOR: a modular and interoperable approach to computational biology.

Computational biologists have been developing standards and formats for nearly two decades, with the aim of easing the description and exchange of experimental data, mathematical models, simulation experiments, etc. One of those efforts is CellML (cellml.org), an XML-based markup language for the encoding of mathematical models. Early CellML-based environments include COR and OpenCell. However, both of those tools have limitations and were eventually replaced with OpenCOR (opencor.ws). OpenCOR is an open source modeling environment that is supported on Windows, Linux and OS X. It relies on a modular approach, which means that all of its features come in the form of plugins. Those plugins can be used to organize, edit, simulate and analyze models encoded in the CellML format. We start with an introduction to CellML and two of its early adopters, which limitations eventually led to the development of OpenCOR. We then go onto describing the general philosophy behind OpenCOR, as well as describing its openness and its development process. Next, we illustrate various aspects of OpenCOR, such as its user interface and some of the plugins that come bundled with it (e.g., its editing and simulation plugins). Finally, we discuss some of the advantages and limitations of OpenCOR before drawing some concluding remarks.

Inferring intra-cellular mechanics using geometric metamorphosis: A preliminary study.

Mechanotransduction plays an important role in sub-cellular processes and is an active area of research. Determining the forces/strains that the intra-cellular structures experience is vital for developing quantitative models of cellular behavior. Established techniques such as traction force microscopy, digital image correlation etc. track surface forces and kinematics of intra-cellular structures. However, difficulties arise when cells cannot be seeded on micro-patterned substrates or the intra-cellular structures vary (unstable landmarks). Here, we applied geometric metamorphosis, a global image registration method, to determine the kinematic profile of a cell during cell division. The method does not require stable landmarks, the registration is non-local in nature and constraints such as volume conservation can be enforced. The cell wall was tracked over time and a sequence of transformations relating the cell wall at the start of cytokinesis to the configuration prior to the daughters completely separate was determined. These transformations are associated with a scalar metric and a statistical atlas describing the wall kinematics from multiple tracking's of the wall shape is constructed. Using these transformations, the cellular kinematics can be described using a Lagrangian frame of reference and the evolution of a material point property can be easily modeled. To demonstrate this, we use the kinematic data derived from the atlas along with a model of stress-fiber (de)formation dynamics to simulate the stress-fiber configuration as the cell domain deforms.