Computational Modeling in Biology Network (COMBINE)
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Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology
Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology Michael L. Blinova,g, John H. Gennarib,g, Jonathan R. Karrc,d,g, Ion I. Morarua,g, David P. Nickersone,g, Herbert M. Saurof,g,h aCenter for Cell Analysis and Modeling, UConn Health, Farmington, CT, US bDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, US cIcahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, US dDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, US eAuckland Bioengineering Institute, University of Auckland, Auckland, NZ fDepartment of Bioengineering, University of Washington, Seattle, WA, US gThese authors contributed equally to this work hCorrespondence: [email protected] Abstract Although reproducibility is a core tenet of the scientific method, it remains challenging to re- produce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For exam- ple, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified sev- eral domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology. -
Kinetic Modeling Using Biopax Ontology
Kinetic Modeling using BioPAX ontology Oliver Ruebenacker, Ion. I. Moraru, James C. Schaff, and Michael L. Blinov Center for Cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, 06030 {oruebenacker,moraru,schaff,blinov}@exchange.uchc.edu Abstract – e.g. Cytoscape (http://cytoscape.org, [5]), cPath database http://cbio.mskcc.org/software/cpath, [6]), Thousands of biochemical interactions are PathCase (http://nashua.case.edu/PathwaysWeb), available for download from curated databases such VisANT (http://visant.bu.edu, [7]). However, the as Reactome, Pathway Interaction Database and other current standard for kinetic modeling is Systems sources in the Biological Pathways Exchange Biology Markup Language, SBML ([8], (BioPAX) format. However, the BioPAX ontology http://sbml.org). Both BioPAX and SBML are used to does not encode the necessary information for kinetic encode key information about the participants in modeling and simulation. The current standard for biochemical pathways, their modifications, locations kinetic modeling is the System Biology Markup and interactions, but only SBML can be used directly Language (SBML), but only a small number of models for kinetic modeling, because elements are included in are available in SBML format in public repositories. SBML specifically for the context of a quantitative Additionally, reusing and merging SBML models theory. In contrast, concepts in BioPAX are more presents a significant challenge, because often each abstract. SBML-encoded models typically contain all element has a value only in the context of the given data necessary for simulations, such as molecular model, and information encoding biological meaning species and their concentrations, reactions among is absent. We describe a software system that enables these species, and kinetic laws for these reactions. -
"Using Views of Systems Biology Cloud: Application
Theory Biosci. (2011) 130:45–54 DOI 10.1007/s12064-010-0108-6 ORIGINAL PAPER Using views of Systems Biology Cloud: application for model building Oliver Ruebenacker • Michael Blinov Received: 17 November 2009 / Accepted: 4 July 2010 / Published online: 21 August 2010 Ó Springer-Verlag 2010 Abstract A large and growing network (‘‘cloud’’) of PubMed or database records for pathways, substances and interlinked terms and records of items of Systems Biology processes. A typical model creation (such as in Virtual Cell knowledge is available from the web. These items include modeling and simulation framework (Moraru et al. 2008)) pathways, reactions, substances, literature references, involves specifying species (represented by one type of organisms, and anatomy, all described in different data node), reactions (represented by another type of node), and sets. Here, we discuss how the knowledge from the cloud specify which species serve as reactants, products or cat- can be molded into representations (views) useful for data alysts (represented by different types of arrows connecting visualization and modeling. We discuss methods to create species and reactions). If the model is intended to be and use various views relevant for visualization, modeling, reused, then the user will use knowledge from the literature and model annotations, while hiding irrelevant details to add annotations, such as assigning UniProt (http:// without unacceptable loss or distortion. We show that uniprot.org/) or ChEBI (Degtyarenko et al. 2009) identifi- views are compatible with understanding substances and ers to species nodes and PubMed identifiers to reaction processes as sets of microscopic compounds and events nodes. The connection between the reality described in the respectively, which allows the representation of special- article or database, the model elements (species, reaction, izations and generalizations as subsets and supersets participation, etc.) and the annotation (UniProt and Pub- respectively. -
Academic Year 2007 Computing Project Group Report SBML-ABC, A
Academic year 2007 Computing project Group report SBML-ABC, a package for data simulation, parameter inference and model selection Imperial College- Division of Molecular Bioscience MSc. Bioinformatics Nathan Harmston, David Knowles, Sarah Langley, Hang Phan Supervisor: Professor Michael Stumpf June 13, 2008 Contents 1 Introduction 1 1.1 Background ...................................... 1 2 Features and dependencies 2 2.1 Project outline ..................................... 2 2.2 Key features ...................................... 2 2.2.1 SBML ..................................... 2 2.2.2 Stochastic simulation ............................. 4 2.2.3 Deterministic simulation ........................... 4 2.2.4 ABC inference ................................ 5 2.3 Interfaces ....................................... 5 2.3.1 CAPI ..................................... 5 2.3.2 Command line interface ........................... 5 2.3.3 Python .................................... 5 2.3.4 R ....................................... 6 2.4 Dependencies ..................................... 6 3 Methods 8 3.1 SBML Adaptor .................................... 8 3.2 Stochastic simulation ................................. 8 3.2.1 Random number generator .......................... 9 3.2.2 Multicompartmental Gillespie algorithm ................... 9 3.2.3 Tau leaping .................................. 10 3.2.4 Chemical Langevin Equation ......................... 11 3.3 Deterministic algorithms ............................... 11 3.3.1 ODE solvers ................................ -
The Biopax Community Standard for Pathway Data Sharing
Supplementary Table S1. An example BioPAX file describing the phosphorylation and activation of CHK2 by ATM in human. Data was originally obtained from the Reactome database8. <?xml version="1.0"?> <rdf:RDF xmlns="http://www.biopax.org/examples/myExample#" xmlns:bp="http://www.biopax.org/release/biopax-level3.owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xml:base="http://www.biopax.org/examples/myExample"> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://www.biopax.org/release/biopax-level3.owl"/> </owl:Ontology> <rdf:Property rdf:about="http://www.biopax.org/release/biopax- level3.owl#direction"/> <bp:Protein rdf:ID="Protein_5"> <bp:dataSource> <bp:Provenance rdf:ID="Provenance_3"> <bp:displayName rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >Reactome (http://reactome.org)</bp:displayName> <bp:xref> <bp:PublicationXref rdf:ID="pubmed"> <bp:year rdf:datatype="http://www.w3.org/2001/XMLSchema#int" >2003</bp:year> <bp:db rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >pubmed</bp:db> <bp:title rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Genome Knowledgebase: a resource for biologists and bioinformaticists.</bp:title> <bp:id rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >15338623</bp:id> </bp:PublicationXref> </bp:xref> </bp:Provenance> </bp:dataSource> <bp:cellularLocation> <bp:CellularLocationVocabulary rdf:ID="CellularLocationVocabulary_6"> -
TUTORIAL.Pdf
Systems Biology Toolbox for MATLAB A computational platform for research in Systems Biology Tutorial www.sbtoolbox.org Henning Schmidt, [email protected] Vision ° The Systems Biology Toolbox for MATLAB offers systems biologists an open and user extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological systems. Henning Schmidt, [email protected] www.sbtoolbox.org Tutorial Outline ° General introduction to the toolbox ° Using the toolbox documentation ° Building models and simulation ° Import/Export of models ° Using the toolbox functions - examples ° Mass conservation and simple model reduction ° Steady-state analysis and stability ° Bifurcation analysis ° Parameter sensitivity analysis (metabolic control analysis) ° In silico experiments and the representation of measurement data ° Parameter estimation ° Localization of mechanisms leading to oscillations and bistability ° Network identification ° Writing your own functions for the toolbox ° Modifying existing MATLAB models for use with the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org ° General introduction to the toolbox Henning Schmidt, [email protected] www.sbtoolbox.org Model Development Cycle Graphical Modelling (CellDesigner, PathwayLab, etc.) Model export to graphical Model import modelling tool to SB Toolbox Modelling, Simulation, Analysis, Identification, etc. (SB Toolbox) M e M a o s d u e re l m m e o n Henning Schmidt, [email protected] -
Modeling Without Borders: Creating and Annotating Vcell Models Using the Web
Modeling without Borders: Creating and Annotating VCell Models Using the Web Michael L. Blinov, Oliver Ruebenacker, James C. Schaff, and Ion I. Moraru Center for Cell Analysis and Modeling University of Connecticut Health Center Farmington, CT 06030, USA {blinov,oruebenacker,schaff,moraru}@exchange.uchc.edu http:vcell.org/sybil Abstract. Biological research is becoming increasingly complex and data-rich, with multiple public databases providing a variety of resources: hundreds of thousands of substances and interactions, hundreds of ready to use models, controlled terms for locations and reaction types, links to reference materials (data and/or publications), etc. Mathematical mod- eling can be used to integrate this complex data and create quantita- tive, testable predictions based on the current state of knowledge of a biological process. Data retrieval, visualization, flexible querying, and model annotation for future reuse, are some of the important require- ments for modeling-based research in the modern age. Here we describe an approach that we implement within the popular Virtual Cell (VCell) modeling and simulation framework in order to help connect the mod- eling community with the web of machine-processable systems biology knowledge. A new software application, called SyBiL (Systems Biology Linker), has been designed and developed for simultaneous querying of multiple systems biology knowledge bases and data sources, such as web repositories, databases, and user files, and converting the extracted and refined data into model elements. Integration of SyBiL as a component of VCell makes these capabilities easily available to a wide modeling community. Keywords: Biological databases, mathematical modeling, VCell, data conversion. 1 Introduction Mathematical modeling is increasingly necessarry to investigate the function of molecular pathways and networks, and a growing number of resources that fa- cilitate computational approaches are becoming available. -
An SBML Overview
An SBML Overview Michael Hucka, Ph.D. Control and Dynamical Systems Division of Engineering and Applied Science California Institute of Technology Pasadena, CA, USA 1 So much is known, and yet, not nearly enough... 2 Must weave solutions using different methods & tools 3 Common side-effect: compatibility problems 4 Models represent knowledge to be exchanged 5 SBML 6 SBML = Systems Biology Markup Language Format for representing quantitative models • Defines object model + rules for its use - Serialized to XML Neutral with respect to modeling framework • ODE vs. stochastic vs. ... A lingua franca for software • Not procedural 7 Some basics of SBML model encoding ๏ Well-stirred compartments c n 8 Some basics of SBML model encoding c protein A protein B n gene mRNAn mRNAc 9 Some basics of SBML model encoding ๏ Reactions can involve any species anywhere c protein A protein B n gene mRNAn mRNAc 10 Some basics of SBML model encoding ๏ Reactions can cross compartment boundaries c protein A protein B n gene mRNAn mRNAc 11 Some basics of SBML model encoding ๏ Reaction/process rates can be (almost) arbitrary formulas c protein A f1(x) protein B n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 12 Some basics of SBML model encoding ๏ “Rules”: equations expressing relationships in addition to reaction sys. g1(x) c g (x) 2 protein A f1(x) protein B . n f5(x) f2(x) gene f4(x) mRNAn f3(x) mRNAc 13 Some basics of SBML model encoding ๏ “Events”: discontinuous actions triggered by system conditions g1(x) c g (x) 2 protein A f1(x) protein B . -
Systems Biology Graphical Notation Systems Biology Markup
Systems Biology Markup Language (SBML) 2057 S models and tacit rule-based models have long been used in product development and safety testing in Systems Biology Graphical Notation various engineering disciplines, such as aerospace engineering and electronic circuit design. It can be ▶ SBGN envisaged that predictive approaches is expected to increase significantly in the coming years in drug dis- covery too. The extent of omics-scale data and the advances in systems technologies to enable compre- Systems Biology Markup Language hension of such large complex data in the form of (SBML) meaningful biological models are promising to help in this process. The power of systems biology methods Michael Hucka is such that it may become possible in the not-too Computing and Mathematical Sciences, distant future, that a disease could get diagnosed in California Institute of Technology, Pasadena, a clinical setting and characterized at the systems level CA, USA with precise genotype and phenotype definitions, lead- ing all the way up to predictive quantitative titrations of the available remedies and finally personalized Synonyms prescriptions. SBML References Boran D, Iyengar R (2010) Systems approaches to polyphar- Definition macology and drug discovery. Curr Opin Drug Discov Dev 13(3):297–309 SBML (the Systems Biology Markup Language) is Butcher EC, Berg EL et al (2004) Systems biology in drug a representation format, based upon XML, used for discovery. Nat Biotechnol 22(10):1253–1259 Forst CV (2006) Host-pathogen systems biology. Drug Discov communicating and storing computational models of Today 11:220–227 biological processes (Hucka et al. 2003). SBML can Glaab E, Baudot A et al (2012) EnrichNet: network-based represent many different classes of biological phenom- gene set enrichment analysis. -
Principled Annotation of Quantitative Models in Systems Biology
Principled annotation of quantitative models in Systems Biology Nicolas Le Novère, EMBL-EBI Computational model in SysBio Computational model in SysBio Not a bimolecular interaction Computational model in SysBio Not a bimolecular interaction complexes Computational model in SysBio Not a bimolecular interaction complexes Non-phos and phos of the same Computational model in SysBio Why not C2P? Not a bimolecular interaction complexes Non-phos and phos of the same Computational model in SysBio Tyson JJ (1991) Modeling the cell division cycle: cdc2 and cyclin interactions. Proc. Natl. Acad. Sci. U.S.A. 88: 7328-7332 http://www.ebi.ac.uk/biomodels/ BIOMD0000000005 What do-we want to do with it f(x,y) u(x,y) g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) What do-we want to do with it f(x,y) u(x,y) g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Encapsulation a(i,j) b(i,j) g(i,j) What do-we want to do with it f(x,y) u(x,y) Communication g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Encapsulation a(i,j) b(i,j) g(i,j) What do-we want to do with it f(x,y) u(x,y) Communication g(x,y) h(x,y) Integration f(x,y) u(x,y) g(x,y) h(x,y) Standards of representation Encapsulation Interfaces (ontologies) a(i,j) Data resources b(i,j) g(i,j) Is SBML enough? What's missing? An SBML model lists participants, but does not identify them. -
Controlled Vocabularies and Semantics in Systems Biology
Molecular Systems Biology 7; Article number 543; doi:10.1038/msb.2011.77 Citation: Molecular Systems Biology 7: 543 & 2011 EMBO and Macmillan Publishers Limited All rights reserved 1744-4292/11 www.molecularsystemsbiology.com PERSPECTIVE Controlled vocabularies and semantics in systems biology Me´lanie Courtot1,19, Nick Juty2,19, Christian Knu¨pfer3,19, provides semantic information about the model compo- Dagmar Waltemath4,19, Anna Zhukova2,19, Andreas Dra¨ger5, nents. The Kinetic Simulation Algorithm Ontology (KiSAO) Michel Dumontier6, Andrew Finney7, Martin Golebiewski8, supplies information about existing algorithms available Janna Hastings2, Stefan Hoops9, Sarah Keating2, Douglas B for the simulation of systems biology models, their characterization and interrelationships. The Terminology Kell10,11, Samuel Kerrien2, James Lawson12, Allyson Lister13,14, for the Description of Dynamics (TEDDY) categorizes James Lu15, Rainer Machne16, Pedro Mendes10,17, 14 2 10,17 dynamical features of the simulation results and general Matthew Pocock , Nicolas Rodriguez , Alice Villeger , systems behavior. The provision of semantic information 13 2 2 Darren J Wilkinson , Sarala Wimalaratne , Camille Laibe , extends a model’s longevity and facilitates its reuse. It 18 2, Michael Hucka and Nicolas Le Nove`re * provides useful insight into the biology of modeled processes, and may be used to make informed decisions 1 Terry Fox Laboratory, Vancouver, Canada, on subsequent simulation experiments. 2 Department of Computational Neurobiology, EMBL European Bioinformatics -
DOE Systems Biology Knowledgebase Implementation Plan
DOE Systems Biology Knowledgebase Implementation Plan As part of the U.S. Department of Energy’s (DOE) Office of Science, the Office of Biological and Environmental Research (BER) supports fundamental research and technology development aimed at achieving predictive, systems-level understand- ing of complex biological and environmental systems to advance DOE missions in energy, climate, and environment. DOE Contact Susan Gregurick 301.903.7672, [email protected] Office of Biological and Environmental Research U.S. Department of Energy Office of Science www.science.doe.gov/Program_Offices/BER.htm Acknowledgements The DOE Office of Biological and Environmental Research appreciates the vision and leadership exhibited by Bob Cottingham and Brian Davison (both from Oak Ridge National Laboratory) over the past year to conceptualize and guide the effort to create the DOE Systems Biology Knowledgebase Implementation Plan. Furthermore, we are grateful for the valuable contributions from about 300 members of the scientific community to organize, participate in, and provide the intellectual output of 5 work- shops, which culminated with the implementation plan. The plan was rendered into its current form by the efforts of the Biological and Environmental Research Information System (Oak Ridge National Laboratory). The report is available via • www.genomicscience.energy.gov/compbio/ • www.science.doe.gov/ober/BER_workshops.html • www.systemsbiologyknowledgebase.org Suggested citation for entire report: U.S. DOE. 2010. DOE Systems Biology Knowledgebase Implementation Plan. U.S. Department of Energy Office of Science (www.genomicscience.energy.gov/compbio/). DOE Systems Biology Knowledgebase Implementation Plan September 30, 2010 Office of Biological and Environmental Research The document is available via genomicscience.energy.gov/compbio/.