Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

[1]  E. Klipp,et al.  Retrieval, alignment, and clustering of computational models based on semantic annotations , 2011, Molecular systems biology.

[2]  M. Ashburner,et al.  An ontology for cell types , 2005, Genome Biology.

[3]  John H. Gennari,et al.  An OWL knowledge base for classifying and querying collections of physiological models: A prototype human physiome , 2013, ICBO.

[4]  Jian Zhang,et al.  Protein Ontology: a controlled structured network of protein entities , 2013, Nucleic Acids Res..

[5]  Christoph Steinbeck,et al.  The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013 , 2012, Nucleic Acids Res..

[6]  Edmund J Crampin,et al.  A metabolite-sensitive, thermodynamically constrained model of cardiac cross-bridge cycling: implications for force development during ischemia. , 2010, Biophysical journal.

[7]  John H. Gennari,et al.  A Reappraisal of How to Build Modular, Reusable Models of Biological Systems , 2014, PLoS Comput. Biol..

[8]  Robert Hoehndorf,et al.  Representing physiological processes and their participants with PhysioMaps , 2013, J. Biomed. Semant..

[9]  Edda Klipp,et al.  Propagating semantic information in biochemical network models , 2012, BMC Bioinformatics.

[10]  Peter J. Hunter,et al.  An Overview of CellML 1.1, a Biological Model Description Language , 2003, Simul..

[11]  Erik Butterworth,et al.  JSim, an open-source modeling system for data analysis , 2013, F1000Research.

[12]  Theo Arts,et al.  Advances in Semantic Representation for Multiscale Biosimulation: A Case Study in Merging Models , 2009, Pacific Symposium on Biocomputing.

[13]  Mary E. Mangan,et al.  The Adult Mouse Anatomical Dictionary: a tool for annotating and integrating data , 2005, Genome Biology.

[14]  Cornelius Rosse,et al.  A Reference Ontology for Bioinformatics: The Foundational Model of Anatomy , 2003 .

[15]  John H. Gennari,et al.  Multiple ontologies in action: Composite annotations for biosimulation models , 2011, J. Biomed. Informatics.

[16]  S. Niederer,et al.  A mathematical model of the slow force response to stretch in rat ventricular myocytes. , 2007, Biophysical journal.

[17]  Jessica A. Turner,et al.  Modeling biomedical experimental processes with OBI , 2010, J. Biomed. Semant..

[18]  Melanie I. Stefan,et al.  BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models , 2010, BMC Systems Biology.

[19]  L. Stein,et al.  OWL Web Ontology Language - Reference , 2004 .

[20]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[21]  Gene Ontology Consortium.  The Gene Ontology (GO) database and informatics resource , 2003 .

[22]  J. Rice,et al.  Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. , 2008, Biophysical journal.

[23]  Ian Horrocks,et al.  OWL Web Ontology Language Reference-W3C Recommen-dation , 2004 .

[24]  Peter J. Hunter,et al.  OpenCOR: a modular and interoperable approach to computational biology , 2015, Front. Physiol..

[25]  Mary Shimoyama,et al.  Multiscale Modeling and Data Integration in the Virtual Physiological Rat Project , 2012, Annals of Biomedical Engineering.

[26]  John H. Gennari,et al.  Ontology of physics for biology: representing physical dependencies as a basis for biological processes , 2013, Journal of Biomedical Semantics.

[27]  Jonna R Terkildsen,et al.  Using Physiome standards to couple cellular functions for rat cardiac excitation–contraction , 2008, Experimental physiology.

[28]  P. Hunter,et al.  A quantitative analysis of cardiac myocyte relaxation: a simulation study. , 2006, Biophysical journal.

[29]  José L. V. Mejino,et al.  A reference ontology for biomedical informatics: the Foundational Model of Anatomy , 2003, J. Biomed. Informatics.

[30]  W. Giles,et al.  A mathematical model of action potential heterogeneity in adult rat left ventricular myocytes. , 2001, Biophysical journal.

[31]  John H. Gennari,et al.  Physical Properties of Biological Entities: An Introduction to the Ontology of Physics for Biology , 2011, PloS one.

[32]  A. Tanskanen,et al.  A simplified local control model of calcium-induced calcium release in cardiac ventricular myocytes. , 2004, Biophysical journal.