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.

In August 2017 under the auspices of the International Union of Physiological Sciences (IUPS), we will launch a new journal: Physiome. The goal of Physiome is to promote, encourage, and support the wide-spread adoption of technologies and workflows that generally improve the ability of scientists to discover existing computational models which are relevant to their work, reproduce the predictions of those models, and understand the scope, limitations, and provenance of the models in order to appropriately reuse suitable models in the quest to address their own hypotheses. As we prepare for the launch, we present here the current status and future plans for Physiome.

UNLABELLED: ICMA, a software framework to create 3D finite element models of the left ventricle from cardiac ultrasound or magnetic resonance imaging (MRI) data, has been made available as an open-source code. The framework is hardware vendor independent and uses speckle tracking (endocardial border detection) on ultrasound (MRI) imaging data in the form of DICOM. Standard American Heart Association segment-based strain analysis can be performed using a browser-based interface. The speckle tracking, border detection and model fitting methods are implemented in C++ using open-source tools. They are wrapped as web services and orchestrated via a JBOSS-based application server. AVAILABILITY AND IMPLEMENTATION: The source code for ICMA is freely available under MPL 1.1 or GPL 2.0 or LGPL 2.1 license at https://github.com/ABI-Software-Laboratory/ICMA and a standalone virtual machine at http://goo.gl/M4lJKH for download. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including nontextual information; distributed data management under security and performance constraints; specialized analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and specialized analytics to define the "physiological envelope" during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

The 12 papers in this special issue focus on medical imaging and image computing in computational physiology. Most of the papers are focused on various aspects of the cardiovascular system across various physiological processes and observational scales.

Although many blood flow models have been constructed for cerebral arterial trees, few models have been reported for their venous counterparts. In this paper, we present a computational model for an anatomically accurate cerebral venous tree which was created from a computed tomography angiography (CTA) image. The topology of the tree containing 42 veins was constructed with 1-D cubic-Hermite finite element mesh. The model was formulated using the reduced Navier-Stokes equations together with an empirical constitutive equation for the vessel wall which takes both distended and compressed states of the wall into account. A robust bifurcation model was also incorporated into the model to evaluate flow across branches. Furthermore, a set of hierarchal inflow pressure boundary conditions were prescribed to close the system of equations. Some assumptions were made to simplify the numerical treatment, e.g., the external pressure was considered as uniform across the venous tree, and a vein was either distended or partially collapsed but not both. Using such a scheme we were able to evaluate the blood flow over several cardiac cycles for the large venous tree. The predicted results from the model were compared with ultrasonic measurements acquired at several sites of the venous tree and agreements have been reached either qualitatively (flow waveform shape) or quantitatively (flow velocity magnitude). We then discuss the significance of this venous model, its potential applications, and also present numerical experiments pertinent to limitations of the proposed model.

In this paper, we apply a 3-D flow model and a 1-D circulation model to the hemodynamic analysis of transjugular intrahepatic portosystemic shunt (TIPS), the therapy for treating acute portal hypertension (PH) induced diseases. Using the 3-D model we are able to simulate the blood flow within a patient-specific TIPS system which was reconstructed from a computed tomography image, and quantify such hemodynamic data as the wall shear stress and flow velocity. The 1-D model is used for the investigation of generic TIPS-induced hepatic circulation phenomena. By incorporating physiological data into the 1-D model we can reproduce some complex flow patterns such as the increased arterial flow after TIPS implantation, the formation of retrograde flow in the portal vein, etc. In particular, our model gives a quantitative analysis of the interplay between TIPS and hepatic flows. In conclusion, the presented computational model can be used for the theoretical analysis of TIPS, in which clinical decisions are often made based on contradictory considerations to balance the procedure-induced complications and the urgency of relieving acute PH symptoms.

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.

MOTIVATION: Integrative mathematical and statistical models of cardiac anatomy and physiology can play a vital role in understanding cardiac disease phenotype and planning therapeutic strategies. However, the accuracy and predictive power of such models is dependent upon the breadth and depth of noninvasive imaging datasets. The Cardiac Atlas Project (CAP) has established a large-scale database of cardiac imaging examinations and associated clinical data in order to develop a shareable, web-accessible, structural and functional atlas of the normal and pathological heart for clinical, research and educational purposes. A goal of CAP is to facilitate collaborative statistical analysis of regional heart shape and wall motion and characterize cardiac function among and within population groups. RESULTS: Three main open-source software components were developed: (i) a database with web-interface; (ii) a modeling client for 3D + time visualization and parametric description of shape and motion; and (iii) open data formats for semantic characterization of models and annotations. The database was implemented using a three-tier architecture utilizing MySQL, JBoss and Dcm4chee, in compliance with the DICOM standard to provide compatibility with existing clinical networks and devices. Parts of Dcm4chee were extended to access image specific attributes as search parameters. To date, approximately 3000 de-identified cardiac imaging examinations are available in the database. All software components developed by the CAP are open source and are freely available under the Mozilla Public License Version 1.1 (http://www.mozilla.org/MPL/MPL-1.1.txt). AVAILABILITY: http://www.cardiacatlas.org CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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/.

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.

This FAIRsharing record describes: The MIASE Guidelines, initiated by the BioModels.net effort, are a community effort to identify the Minimal Information About a Simulation Experiment, necessary to enable the reproducible simulation experiments. Consequently, the MIASE Guidelines list the information that a modeller needs to provide to enable the execution and reproduction of a numerical simulation experiment, derived from a given set of quantitative models. MIASE is a set of guidelines suitable for use with any structured format for simulation experiments. As such, MIASE is designed to help modelers and software tools to exchange their simulation settings and to foster collaboration.

Multiscale modeling is required for linking physiological processes operating at the organ and tissue levels to signal transduction networks and other subcellular processes. Several XML markup languages, including CellML, have been developed to encode models and to facilitate the building of model repositories and general purpose software tools. Progress in this area is described and illustrated with reference to the heart Physiome Project which aims to understand cardiac arrhythmias in terms of structure-function relations from proteins up to cells, tissues and organs.

SUMMARY: The CellML Model Repository provides free access to over 330 biological models. The vast majority of these models are derived from published, peer-reviewed papers. Model curation is an important and ongoing process to ensure the CellML model is able to accurately reproduce the published results. As the CellML community grows, and more people add their models to the repository, model annotation will become increasingly important to facilitate data searches and information retrieval. AVAILABILITY: The CellML Model Repository is publicly accessible at http://www.cellml.org/models.

The Physiome Project is an international collaboration to provide a framework for understanding human physiology, from proteins and cells to tissues and organs, with multiscale models that use computational techniques derived from engineering and software approaches from computer science. With the increasing interest in modeling living systems from research scientists in many branches of mathematics, physics, and engineering, it is timely to review what has been achieved, what lessons can be learned from the efforts so far, and what needs to be done to facilitate the international collaboration that is essential to the project's success. In particular, we review the development of models at spatial scales from genes and proteins to the whole body, and the development of standards, tools, and databases to facilitate multiscale modeling. Some applications of the physiome models are described, including applications in medical diagnostics, the design of medical devices, virtual surgery, surgical training, and medical education.

We present a method to estimate left ventricular (LV) motion based on three-dimensional (3-D) images that can be derived from any anatomical tomographic or 3-D modality, such as echocardiography, computed tomography, or magnetic resonance imaging. A finite element mesh of the LV was constructed to fit the geometry of the wall. The mesh was deformed by optimizing the nodal parameters to the motion of a sparse number of fiducial markers that were manually tracked in the images through the cardiac cycle. A parameter distribution model (PDM) of LV deformations was obtained from a database of MR tagging studies. This was used to filter the calculated deformation and incorporate a priori information on likely motions. The estimated deformation obtained from 13 normal untagged studies was compared with the deformation obtained from MR tagging. The end systolic (ES) circumferential and longitudinal strain values matched well with a mean difference of 0.1 +/- 3.2% and 0.3 +/- 3.0%, respectively. The calculated apex-base twist angle at ES had a mean difference of 1.0 +/- 2.3 degrees. We conclude that fiducial marker fitting in conjunction with a PDM provides accurate reconstruction of LV deformation in normal subjects.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

An anatomically detailed 3-D computer graphic model of the eye and surrounding face within a virtual environment has been implemented for use in a surgical simulator. The simulator forms part of a teleoperated micro-surgical robotic system being developed for eye surgery. The model has been designed to both visually and mechanically simulate features of the human eye by coupling computer graphic realism with finite element analysis.

A microrobot having two high-performance parallel drive limbs has been developed for manipulation, surgery, and dynamic mechanical testing of very small objects such as single living cells. The end-points of each limb move in overlapping spherical workspaces of 1 mm diameter with minimum open- and closed-loop movements of 1 nm and 10 nm, respectively. With optimal nonlinear model-based controllers the limbs can move at up to 2 m/s relative to each other. A variety of end effectors, including ferroelectric polymer microgrippers, may be attached to the limbs to permit cell manipulation. A 3D laser vision system with resolution of 50 to 100 nm has been developed to provide the microrobot with volume images containing magnitude, phase, polarization, and special information. A macro version of the microrobot has been built to enable force-reflecting teleoperation of the microrobot. The telemicrorobot system permits both microscopic objects and continuum models to be felt. A high-performance parallel computer has been designed to meet the substantial computational and control requirements of the system.>