BioSimulators: a central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

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 annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Three-dimensional histology: tools and application to quantitative assessment of cell-type distribution in rabbit heart.

AIMS: Cardiac histo-anatomical organization is a major determinant of function. Changes in tissue structure are a relevant factor in normal and disease development, and form targets of therapeutic interventions. The purpose of this study was to test tools aimed to allow quantitative assessment of cell-type distribution from large histology and magnetic resonance imaging- (MRI) based datasets. METHODS AND RESULTS: Rabbit heart fixation during cardioplegic arrest and MRI were followed by serial sectioning of the whole heart and light-microscopic imaging of trichrome-stained tissue. Segmentation techniques developed specifically for this project were applied to segment myocardial tissue in the MRI and histology datasets. In addition, histology slices were segmented into myocytes, connective tissue, and undefined. A bounding surface, containing the whole heart, was established for both MRI and histology. Volumes contained in the bounding surface (called 'anatomical volume'), as well as that identified as containing any of the above tissue categories (called 'morphological volume'), were calculated. The anatomical volume was 7.8 cm(3) in MRI, and this reduced to 4.9 cm(3) after histological processing, representing an 'anatomical' shrinkage by 37.2%. The morphological volume decreased by 48% between MRI and histology, highlighting the presence of additional tissue-level shrinkage (e.g. an increase in interstitial cleft space). The ratio of pixels classified as containing myocytes to pixels identified as non-myocytes was roughly 6:1 (61.6 vs. 9.8%; the remaining fraction of 28.6% was 'undefined'). CONCLUSION: Qualitative and quantitative differentiation between myocytes and connective tissue, using state-of-the-art high-resolution serial histology techniques, allows identification of cell-type distribution in whole-heart datasets. Comparison with MRI illustrates a pronounced reduction in anatomical and morphological volumes during histology processing.

The simulation experiment description markup language (SED-ML): language specification for level 1 version 5.

Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

Electrotonic suppression of early afterdepolarizations in the neonatal rat ventricular myocyte monolayer.

Pathologies that result in early afterdepolarizations (EADs) are a known trigger for tachyarrhythmias, but the conditions that cause surrounding tissue to conduct or suppress EADs are poorly understood. Here we introduce a cell culture model of EAD propagation consisting of monolayers of cultured neonatal rat ventricular myocytes treated with anthopleurin-A (AP-A). AP-A-treated monolayers display a cycle length dependent prolongation of action potential duration (245 ms untreated, vs. 610 ms at 1 Hz and 1200 ms at 0.5 Hz for AP-A-treated monolayers). In contrast, isolated single cells treated with AP-A develop prominent irregular oscillations with a frequency of 2.5 Hz, and a variable prolongation of the action potential duration of up to several seconds. To investigate whether electrotonic interactions between coupled cells modulates EAD formation, cell connectivity was reduced by RNA silencing gap junction Cx43. In contrast to well-connected monolayers, gap junction silenced monolayers display bradycardia-dependent plateau oscillations consistent with EADs. Further, simulations of a cell displaying EADs electrically connected to a cell with normal action potentials show a coupling strength-dependent suppression of EADs consistent with the experimental results. These results suggest that electrotonic effects may play a critical role in EAD-mediated arrhythmogenesis.

How the Hodgkin-Huxley equations inspired the Cardiac Physiome Project.

Early modelling of cardiac cells (1960-1980) was based on extensions of the Hodgkin-Huxley nerve axon equations with additional channels incorporated, but after 1980 it became clear that processes other than ion channel gating were also critical in generating electrical activity. This article reviews the development of models representing almost all cell types in the heart, many different species, and the software tools that have been created to facilitate the cardiac Physiome Project.

The Physiome Model Repository 2.

MOTIVATION: The Physiome Model Repository 2 (PMR2) software was created as part of the IUPS Physiome Project (Hunter and Borg, 2003), and today it serves as the foundation for the CellML model repository. Key advantages brought to the end user by PMR2 include: facilities for model exchange, enhanced collaboration and a detailed change history for each model. AVAILABILITY: PMR2 is available under an open source license at http://www.cellml.org/tools/pmr/; a fully functional instance of this software can be accessed at http://models.physiomeproject.org/.

Revision history aware repositories of computational models of biological systems.

BACKGROUND: Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. RESULTS: We have extended the Physiome Model Repository software to be fully revision history aware, by building it on top of Mercurial, an existing DVCS. We have demonstrated the utility of this approach, when used in conjunction with the model composition facilities in CellML, to build and understand more complex models. We have also demonstrated the ability of the repository software to present version history to casual users over the web, and to highlight specific versions which are likely to be useful to users. CONCLUSIONS: Providing facilities for maintaining and using revision history information is an important part of building a useful repository of computational models, as this information is useful both for understanding the source of and justification for parts of a model, and to facilitate automated processes such as merges. The availability of fully revision history aware repositories, and associated tools, will therefore be of significant benefit to the community.

Axial stretch of rat single ventricular cardiomyocytes causes an acute and transient increase in Ca2+ spark rate.

We investigate acute effects of axial stretch, applied by carbon fibers (CFs), on diastolic Ca2+ spark rate in rat isolated cardiomyocytes. CFs were attached either to both cell ends (to maximize the stretched region), or to the center and one end of the cell (to compare responses in stretched and nonstretched half-cells). Sarcomere length was increased by 8.01+/-0.94% in the stretched cell fraction, and time series of XY confocal images were recorded to monitor diastolic Ca2+ spark frequency and dynamics. Whole-cell stretch causes an acute increase of Ca2+ spark rate (to 130.7+/-6.4%) within 5 seconds, followed by a return to near background levels (to 104.4+/-5.1%) within 1 minute of sustained distension. Spark rate increased only in the stretched cell region, without significant differences in spark amplitude, time to peak, and decay time constants of sparks in stretched and nonstretched areas. Block of stretch-activated ion channels (2 micromol/L GsMTx-4), perfusion with Na+/Ca2+-free solution, and block of nitric oxide synthesis (1 mmol/L L-NAME) all had no effect on the stretch-induced acute increase in Ca2+ spark rate. Conversely, interference with cytoskeletal integrity (2 hours of 10 micromol/L colchicine) abolished the response. Subsequent electron microscopic tomography confirmed the close approximation of microtubules with the T-tubular-sarcoplasmic reticulum complex (to within approximately 10(-8)m). In conclusion, axial stretch of rat cardiomyocytes acutely and transiently increases sarcoplasmic reticulum Ca2+ spark rate via a mechanism that is independent of sarcolemmal stretch-activated ion channels, nitric oxide synthesis, or availability of extracellular calcium but that requires cytoskeletal integrity. The potential of microtubule-mediated modulation of ryanodine receptor function warrants further investigation.

The simulation experiment description markup language (SED-ML): language specification for level 1 version 4.

Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.

An overview of the CellML API and its implementation.

BACKGROUND: CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models.However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML. RESULTS: We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages.We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. CONCLUSIONS: Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.

CellML 2.0.

We present here CellML 2.0, an XML-based language for describing and exchanging mathematical models of physiological systems. MathML embedded in CellML documents is used to define the underlying mathematics of models. Models consist of a network of reusable components, each with variables and equations giving relationships between those variables. Models may import other models to create systems of increasing complexity. CellML 2.0 is defined by the normative specification presented here, prescribing the CellML syntax and the rules by which it should be used. The normative specification is intended primarily for the developers of software tools which directly consume CellML syntax. Users of CellML models may prefer to browse the informative rendering of the specification (https://cellml.org/specifications/cellml_2.0/) which extends the normative specification with explanations of the rules combined with examples of their usage.

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models.

Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.

Dimensionality in cardiac modelling.

The development of mathematical models of the heart has been an ongoing concern for many decades. The initial focus of this work was on single cell models that incorporate varyingly detailed descriptions of the mechanisms that give rise to experimentally observed action potential shapes. Clinically relevant heart rhythm disturbances, however, are multicellular phenomena, and there have been many initiatives to develop multidimensional representations of cardiac electromechanical activity. Here, we discuss the merits of dimensionality, from 0D single cell models, to 1D cell strands, 2D planes and 3D volumes, for the simulation of normal and disturbed rhythmicity. We specifically look at models of: (i) the origin and spread of cardiac excitation from the sino-atrial node into atrial tissue, and (ii) stretch-activated channel effects on ventricular cell and tissue activity. Simulation of the spread of normal and disturbed cardiac excitation requires multicellular models. 1D architectures suffer from limitations in neighbouring tissue effects on individual cells, but they can (with some modification) be applied to the simulation of normal spread of excitation or, in ring-like structures, re-entry simulation (colliding wave fronts, tachycardia). 2D models overcome many of the limitations imposed by models of lower dimensionality, and can be applied to the study of complex co-existing re-entry patterns or even fibrillation. 3D implementations are closest to reality, as they allow investigation of scroll waves. Our results suggest that 2D models offer a good compromise between computational resources, complexity of electrophysiological models, and applicability to basic research, and that they should be considered as an important stepping-stone towards anatomically detailed simulations. This highlights the need to identify and use the most appropriate model for any given task. The notion of a single and ultimate model is as useful as the idea of a universal mechanical tool for all possible repairs and servicing requirements in daily life. The ideal model will be as simple as possible and as complex as necessary for the particular question raised.

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.

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.

Mechanical induction of arrhythmias during ventricular repolarization: modeling cellular mechanisms and their interaction in two dimensions.

Nonpenetrating mechanical stimulation of the precordial chest is particularly likely to instantaneously induce sustained rhythm disturbances if timed to coincide with ventricular repolarization. A number of possible mechanisms have been proposed, including mechanoelectric feedback acting via stretch-activated ion channels. The cellular effects of such channel activation have been studied and mathematically modeled in great detail. In this study, we investigate their dynamic interaction with the trailing wave of action potential repolarization in a two-dimensional model of ventricular tissue. The model identifies how stretch activation of cation-nonselective ion channels causes ectopic excitation in fully repolarized tissue and functional block of conduction at the intersection of the mechanical stimulus and the repolarization wave end, which may give rise to both trigger and sustaining mechanisms of ventricular arrhythmia. Simulation of stretch activation of K(+)-selective ion channels alone is insufficient in causing instantaneous arrhythmia, although it may, via action potential shortening, contribute to its sustenance.

Standards and tools supporting collaborative development of the virtual physiological human.

The development of a virtual physiological human has an ambitious goal that requires the participation of a large and diverse community of scientists. To be successful in achieving this goal, members of this community must be able to share their work and easily collaborate on new developments and novel applications of existing work. To aid in this, various standardization projects have evolved as part of the Physiome community, as well as supporting computational tools and infrastructure. We present here an overview of the current state of these standardization efforts and key tools that support the collaborative development, integration, and exchange of computational physiology models under the Physiome umbrella.