High-throughput functional curation of cellular electrophysiology models

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<ul><li><p>ce</p><p>ies Roa</p><p>sion,</p><p>tativn uenhrotoco</p><p>intern(VP</p><p>a correct representation of the original equations, and an under-standing of the functional capabilities of the model.</p><p>reuse, adaptation or combination of existing models to studya specic question of interest. By reusing existing model parametersets and formulations it is hoped that the functionality present inthe original model will be inherited in the model used for a newstudy. This poses two challenges. Firstly, with increasing numbersof models and concurrent increases in complexity, identifying thesubset of models (or none at all), that exhibit a desired phenomenarequired for a particular study becomes increasingly challenging.</p><p>* Corresponding author.E-mail addresses: jonathan.cooper@comlab.ox.ac.uk (J. Cooper), gary.mirams@</p><p>dpag.ox.ac.uk (G.R. Mirams), steven.niederer@kcl.ac.uk (S.A. Niederer).1</p><p>Contents lists availab</p><p>Progress in Biophysics a</p><p>journal homepage: www.elsev</p><p>Progress in Biophysics and Molecular Biology 107 (2011) 11e20Joint rst author.(Bassingthwaighte, 2000; Cooper et al., 2010) is the integration andcoupling of existing biophysical mathematical models, that representboth specic physiological function, and how this function changes inthe presence of pathologies or pharmacological agents. This philos-ophy enables the development of new integrated models that repre-sent interactions and regulatory mechanisms that span acrossmultiple spatial and temporal scales and are intrinsic to biologicalsystems. These integrated models enable the study of complex feed-back loops andemergentphenomena, andprovide anessential tool forcombining extensive disparate experimental and clinical data sets.However, to reuse a mathematical model effectively requires both</p><p>curation process to verify that they conform to language speci-cations, and that they can reproduce gures and results from theoriginal publication. To minimise the chance that error is intro-duced in the reproduction of paper results, the simulation protocolsnecessary to reproduce paper gures can also be encoded (e.g.Nickerson and Buist, 2009). This transparent process of publishingcurated model equations and protocols in standard formatsprovides users with increased condence that the model is a validrepresentation of the original work.</p><p>The availability of these models and the corresponding con-dence in their implementation greatly reduces barriers for the1. Introduction</p><p>1.1. Aims</p><p>Central to the philosophy of theand Virtual Physiological Human0079-6107/$ e see front matter 2011 Elsevier Ltd.doi:10.1016/j.pbiomolbio.2011.06.003in the response of these models, even when the models represent the same species and temperature,demonstrates the importance of knowing the functional characteristics of a model prior to its reuse.We also discuss the wider implications for this approach, in improving the selection of models for</p><p>reuse, enabling the identication of models that exhibit particular experimentally observed phenomena,and making the incremental development of models more robust.</p><p> 2011 Elsevier Ltd. All rights reserved.</p><p>ational IUPS PhysiomeH) modelling efforts</p><p>The rst of these requirements has been met through thedevelopment of community standards for representing mathe-matical models (e.g. CellML (Lloyd et al., 2004) and SBML (Huckaet al., 2004)) and the publication of models in these formats inpublic access databases (Li et al., 2010; Lloyd et al., 2008). Thesereference descriptions are tested and documented as part of theSimulation</p><p>models using two test cases. The S1eS2 response is evaluated to characterise the models restitutioncurves, and their L-type calcium channel currentevoltage curves are evaluated. The signicant variationCellMLProtocol</p><p>study we demonstrate the efcacy of this simulation environment on 31 cardiac electrophysiology cellOriginal Research</p><p>High-throughput functional curation of</p><p>Jonathan Cooper a,*, Gary R. Mirams b,1, Steven A. NaOxford University Computing Laboratory, University of Oxford, Wolfson Building, ParkbDepartment of Physiology, Anatomy &amp; Genetics, University of Oxford, Oxford, UKcBiomedical Engineering Department, Imaging Sciences &amp; Biomedical Engineering Divi</p><p>a r t i c l e i n f o</p><p>Article history:Available online 15 June 2011</p><p>Keywords:Cardiac electrophysiology</p><p>a b s t r a c t</p><p>Effective reuse of a quantimodel equations, but also ascope of its application. Toment that provides high-tarbitrary user-dened proAll rights reserved.llular electrophysiology models</p><p>derer c</p><p>d, Oxford OX1 3QD, UK</p><p>Kings College London, UK</p><p>e mathematical model requires not just access to curated versions of thenderstanding of the functional capabilities of the model, and the advisableable this functional curation we have developed a simulation environ-ughput evaluation of a mathematical models functional response to anl, and optionally compares the results against experimental data. In this</p><p>le at ScienceDirect</p><p>nd Molecular Biology</p><p>ier .com/locate/pbiomolbio</p></li><li><p>Secondly, if a new model is created it is important to conrm bysimulation, rather than assume, that it maintains all of the func-</p><p>SED-ML are tightly tied to a particular model, with all references tovariables being model specic, hence typically precluding thedenition of a single protocol that can be applied to multiple</p><p>J. Cooper et al. / Progress in Biophysics and Molecular Biology 107 (2011) 11e2012tionality present in the reused models. Therefore, contrary to thegoals of the language standards, the increased availability ofmathematical model equations, and the ease of their reuse withouta good understanding of their capabilities, is likely to increasemodel misuse and introduce unintended errors into simulationresults (Smith et al., 2007).</p><p>Examples of many of the challenges facing model reuse withinthe VPH and Physiome projects are demonstrated by cardiac cellmodels, aeld that ismature enough to havemultiple generations ofmodels and corresponding model reuse. Cardiac electrophysiologycell models have developed over the past 50 years, routinely buildon existing models and exhibit complex multiscale functionality.Furthermore previous test cases, where only two models werecompared, have demonstrated discrepancies in function betweenmodels that purport to represent a common species, cell type andtemperature (Cherry and Fenton, 2007; Niederer et al., 2009). Thelarge number of cardiac cellular electrophysiology models available,representing both specic animal species but also specic regions orsub-regions of the heart, have been developed to study a diverserange of cardiac physiology, including variations in temperature,pacing rate, ionic concentrations, sex, and age (Clayton et al., 2010).This diversity means that when approaching a new area of interestan applicable model may already exist, however, evaluating thesuitability of current models for a specic situation is not currentlyreadily assessed.</p><p>In this paper we consider, design and implement the infrastruc-ture necessary to perform high-throughput functional curation ofmodelsdsubjecting a large number of existing models to commonly-used electrophysiology protocols. Thus wemay examinewhich of themodels are capable of replicating experimentally observed results,and evaluate their suitability for reuse in future modelling studies.</p><p>1.2. Existing tools and extensions</p><p>Functional curation requires the capacity to perform simulationprotocols on multiple cell models, post-process model outputs andreport the results. The proposed implementation for functionalcuration is outlined in Fig. 1. There are three distinct types ofcomponents that need to be dened or developed. Firstly, languagecomponents are required to provide a formal denition of modelequations, simulation protocols, post-processing steps, and desiredoutputs. Secondly, interface components need to be dened toallow these different languages to be translated into commonvariable names and units. Thirdly, compute components arerequired to interpret the languages, implement simulation proto-cols, perform simulations, post-process model outputs, andproduce graphical plots.</p><p>Presently there are recognized community standard languageswhich meet the requirements for some of the language compo-nents. As noted above, CellML and SBML provide convenientlanguage standards to formally dene cell model equations. Twomark-up language projects currently provide standards for deningboth model simulation protocols and graphical outputsdSED-ML(Khn and Le Novre, 2008), and the CellML Simulation andGraphing Metadata draft standards2 CSM and CGM respectively;used in (Nickerson and Buist, 2009, for example). These couldpotentially be used for dening simulation protocols for multiplemodels. However, in their current forms both languages do nothave sufcient functionality for our needs. Protocols dened in</p><p>2 http://www.cellml.org/specications/metadata/simulations and http://www.cellml.org/specications/metadata/graphs.models; it is thus incapable of supporting the necessary interfacing.Current implementations of both languages only support time-course simulationsda single solve of model equations. Neitherlanguage is capable of expressing parameter sweeps, or any generalform of repeated simulation (although such features are plannedfor later versions of SED-ML). Finally, neither language supportsanything more than extremely basic post-processing steps.3</p><p>The interface between the protocol language, model equations,and simulation platform is a key aspect of functional curation, inorder to support running a protocol on multiple models andcoherently comparing the results. Since variable names in modelsare arbitrary, a mapping is required labelling variables so as touniquely relate them to a biophysical quantity or protocol construct.Both SBML and CellML support such labelling through RDF anno-tations, using terms taken from ontologies (Beard et al., 2009; LeNovre and Finney, 2005).</p><p>To compare outputs correctly requires a second interfacecomponent for interpreting and converting the physical unitsassociated with variables, since again models vary in the units usedfor the same quantity. Every quantity in a model and protocol musthave its units indicated, and suitable scaling conversions can beadded automatically (Cooper and McKeever, 2008). More complexconversions may also be required, utilizing biophysical knowledgeto dene conversion functions that reference quantities within themodel using ontological terms, as above. A common case withincardiac modelling is converting ionic currents, for example toconvert a current dened in Amperes per unit area (of a cellmembrane) to a current dened in Amperes requiresmultiplicationby the cell surface area (Cooper et al., in this issue).</p><p>While there are several simulation environments available thatsupport simulation of CellML models (see Garny et al., 2008), nonehave the capabilities required of a functional curation system. BothCOR (Garny et al., 2003) and OpenCell (formerly known as PCEnv,Garny et al., 2008), for example, support only time-course simulationsof single models through a graphical user interface, and Chaste (Pitt-Francis et al., 2009) has primarily used CellML models within cardiactissue simulations. We are therefore developing a new open sourcesystem, based on and extending the Chaste simulation platform, inorder to provide high-throughput evaluation of models functionalresponses to arbitrary user-dened protocols. Since available protocollanguages, and in particular SED-ML, do not provide sufcientexpressivity to encode the protocols we wish to run (as describedabove), we are also proposing novel language features to support suchuses. We focus on complex post-processing andmodel modications,whichhave not been central in the developmentof existing languages.</p><p>The features of our functional curation implementation, andparticularly the protocol language features incorporated within it,are detailed in Section 2. Case studies demonstrating the capabil-ities of the system are described in Section 2.5, and the resultsshown in Section 3. Finally, in Section 4 we discuss some salientfeatures of our solution within the wider context of physiologicalmodelling, nishing with some concluding thoughts in Section 5.</p><p>2. Methods</p><p>In this section we outline our proposed implementation offunctional curation, treating the steps in the order given in Fig. 1,</p><p>3 The only post-processing supported is essentially map-type operations,</p><p>applying an operator element-wise to one or more arrays of outputs to producea new array. CGM also includes some limited ltering capabilities.</p></li><li><p>uk/chaste.</p><p>conditions. Setting initial conditions may take the form of settingthe values of model variables at the start of a simulation, or addi-</p><p>moAll iand</p><p>and2.1. Language interfaces</p><p>Applying a protocol language to a repository of arbitrary modelsrequires a machine readable mapping of variable names and unitsbetween the protocol description and each of the models. This isachieved through metadata labelling of variables within the cellfollowed by two test cases that demonstrate the utility of thismethod. Our implementation is guided by twin aims: to producea systemwith sufcient expressive power to be able to run a rangeof complex protocols, and yet also for the protocol language to berelatively straightforward to implement. The software is opensource, and available to download from http://www.comlab.ox.ac.</p><p>Fig. 1. Components of a functional curation system. The steps involved are: (1) annotate(3) run simulation; (4) post-process model outputs; and (5) graphically plot results.mentation is ltered through an interface denition, ensuring that biophysical entities</p><p>J. Cooper et al. / Progress in Biophysicsmodel les and the development of functional unit conversion.</p><p>2.1.1. Metadata labellingTo apply the protocols dened in the protocol language onto</p><p>existing models requires a translation of variable references in theprotocol to the variable names in themodel, allowing the protocol tointerfacewith amodel purely at themathematical level. Rather thanidentifying variables by name, or by location within the model le,we thus require models to be annotated with biological metadata,using an ontology to provide standard names for entities of interest.This technique is independent of a specic ontologydprovidingthat the model and protocol use the same ontology terms.</p><p>2.1.2. Unit conversionThe diversity of modelling strategies and frameworks, com-</p><p>poundedwith the range ofmeasurement techniquesmeans that thesame biological function can be represented by equations withincompatible units. This can be demonstratedwith a cellmembranecurrent as described above, ormore subtly a concentrationux fromthe cytosol to an organelle with the units of Mole per litre persecond does not explicitly state the volume of the concentration.This means it could be a concentration from the cytosol or theorganelle. If comparing two uxes from two models with theseunits, they must be with reference to the same volume for validcomparisons to be...</p></li></ul>

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