Modeling Without Borders: Creating and Annotating Vcell Models Using the Web
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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. -
Rule-Based Modeling of Biochemical Systems with Bionetgen
Rule-Based Modeling of Biochemical Systems with BioNetGen James R. Faeder1, Michael L. Blinov2, and William S. Hlavacek3 Once a model specification is processed by BNG2, Short Abstract — Rule-based modeling involves the models can be exported in one of a variety of formats, representation of molecules as structured objects and including MATLAB M-files and Systems Biology Markup molecular interactions as rules for transforming the attributes language (SBML) (3). BNG2 can also generate a network of of these objects. The approach allows systematic incorporation species and reactions for simulation using either an ODE of site-specific details about protein-protein interactions into a solver or Gillespie’s stochastic simulation algorithm (4). The model for the dynamics of a signal-transduction system, as well as other applications. The consequences of protein-protein dashed line in Fig. 1 indicates that the network generation interactions are difficult to specify and track with a and stochastic simulation engines are coupled so that for conventional modeling approach because of the large number large reaction networks species and reactions can be of protein phosphoforms and protein complexes that these generated on-the-fly as needed (5, 6). Recently, particle- interactions potentially generate. Here, we focus on how a rule- based simulation methods (7, 8) have been developed that based model is specified in the BioNetGen language (BNGL) avoid the computational costs associated with explicit and how a model specification is analyzed using the BioNetGen generation of the reachable network of species and software tool. We also discuss new developments in rule-based interactions, which can become a major bottleneck for the modeling that should enable the construction and analysis of simulation of even moderate-sized rule-based models (9). -
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"> -
Quantitative Cell Biology with the Virtual Cellq
570 Review TRENDS in Cell Biology Vol.13 No.11 November 2003 Quantitative cell biology with the Virtual Cellq Boris M. Slepchenko1, James C. Schaff1, Ian Macara2 and Leslie M. Loew1 1Center For Biomedical Imaging Technology and National Resource for Cell Analysis and Modeling, Department of Physiology, University of Connecticut Health Center, Farmington, CT 06030, USA 2Center for Cell Signaling and Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA Cell biological processes are controlled by an interact- techniques such as patch-clamp single-channel recording. ing set of biochemical and electrophysiological events Once such data are acquired, a crucial and obvious that are distributed within complex cellular structures. challenge is to determine how these, often disparate and Computational models, comprising quantitative data complex, details can explain the cellular process under on the interacting molecular participants in these events, investigation. The ideal way to meet this challenge is to provide a means for applying the scientific method to integrate and organize the data into a predictive model. these complex systems. The Virtual Cell is a compu- Indeed, the value of computational approaches in cell tational environment designed for cell biologists, to biology has been increasingly recognized as demonstrated facilitate the construction of models and the generation by several recent reviews [4–6] and a book [37]. However, of predictive simulations from them. This review sum- for cell biologists, formulating a mathematical model marizes how a Virtual Cell model is assembled and based on quantitative data and solving its equations to describes the physical principles underlying the calcu- generate simulations can present significant technical lations that are performed. -
Vcell Quick Start Guide
Virtual Cell Quick Start Guide 1. Pre-requisites: a. Java (version 1.4.2 or later; 1.5.x recommended) b. An Internet connection (broadband, no firewall recommended). 2. Go to http://www.vcell.org/ a. If you are a new user, first register and create a user name and password. b. If you are registered, navigate to the “VCell” page. 3. Click on one of the 2 links to run VCell as an application or as an applet: a. The software will download and start automatically (it can take a couple of minutes the first time or whenever VCell was updated since your last login) – unless you have pop-up blockers enabled and you are trying to run it as an applet, in which case you must click on the new link “Run the Virtual Cell” after logging in. • Tip: Most operating systems and browsers will work as long as you are running Java 1.4 or later, and there are no real hardware requirements. However, check the “Technical requirements” link for recommended configurations. b. VCell as an application will us Java webstart and will give the option to install a local shortcut. • Tip: Whenever you launch VCell from the local shortcut, if you are connected to the Internet, it will automatically check for and download any updated version. • Tip: You can run the local VCell application without an Internet connection and without logging in to the model database; in this case, however, you will only be able to save your work by using File Æ Export, and you will not be able to run simulations and view results until logging in. -
Biodynamo: a New Platform for Large-Scale Biological Simulation
Lukas Johannes Breitwieser, BSc BioDynaMo: A New Platform for Large-Scale Biological Simulation MASTER’S THESIS to achieve the university degree of Diplom-Ingenieur Master’s degree programme Software Engineering and Management submitted to Graz University of Technology Supervisor Ass.Prof. Dipl.-Ing. Dr.techn. Christian Steger Institute for Technical Informatics Advisor Ass.Prof. Dipl.-Ing. Dr.techn. Christian Steger Dr. Fons Rademakers (CERN) November 23, 2016 AFFIDAVIT I declare that I have authored this thesis independently, that I have not used other than the declared sources/resources, and that I have explicitly indicated all ma- terial which has been quoted either literally or by content from the sources used. The text document uploaded to TUGRAZonline is identical to the present master‘s thesis. Date Signature This thesis is dedicated to my parents Monika and Fritz Breitwieser Without their endless love, support and encouragement I would not be where I am today. iii Acknowledgment This thesis would not have been possible without the support of a number of people. I want to thank my supervisor Christian Steger for his guidance, support, and input throughout the creation of this document. Furthermore, I would like to express my deep gratitude towards the entire CERN openlab team, Alberto Di Meglio, Marco Manca and Fons Rademakers for giving me the opportunity to work on this intriguing project. Furthermore, I would like to thank them for vivid discussions, their valuable feedback, scientific freedom and possibility to grow. My gratitude is extended to Roman Bauer from Newcastle University (UK) for his helpful insights about computational biology detailed explanations and valuable feedback. -
Modeling Using Biopax Standard
Modeling using BioPax standard Oliver Ruebenacker1 and Michael L. Blinov1 biological identification for each element of the model The key issue that should be resolved when making makes BioPax standard a recipe for reusable modeling reusable modeling modules is species recognition: are modules. species S1 of model 1 and species S2 of model 2 identical Currently, several online resources provide and should they be mapped onto the same species in a information about pathways in BioPax format: NetPath – merged model? When merging models, species Signal Transduction Pathways [5], Reactome - a curated recognition should be automated. We address this issue knowledgebase of biological pathways [6], and some others, by providing a modeling process compatible with with more resources aiming at BioPax representation. These Biological Pathways Exchange (BioPax) standard [1]. resources store complete pathways, pathways participants, and individual interactions. Due to principal differences Keywords — Model, Pathway data, BioPax, SBML, the between SBML and BioPax, there are no one-to-one Virtual Cell. translators from BioPax to SBML. We have implemented import of pathway data in BioPax standard into the Virtual he main format currently used for encoding of pathway Cell modeling framework [7], using Jena (Java application Tmodels is Systems Biology Markup Language (SBML) programming interface that provides support for Resource [2], which is designed mainly to enable the exchange of Description Framework). The imported data forms a model biochemical networks between different software packages skeleton where each species being automatically related to with little or no human intervention. SBML model contains some database entry. This model skeleton may lack data necessary for simulation, such as species, interactions simulation-related information (such as concentrations, among species, and kinetic laws for these interactions. -
Integrating Biopax Knowledge with SBML Models
Integrating BioPAX knowledge with SBML models O. Ruebenacker, I.I. Moraru, J.S. Schaff, M. L. Blinov Center for cell Analysis and Modeling, University of Connecticut Health Center Farmington, CT, USA E-mail: [email protected] Abstract Online databases store thousands of molecular interactions and pathways, and numerous modeling software tools provide users with an interface to create and simulate mathematical models of such interactions. However, the two most widespread used standards for storing pathway data (Biological Pathway Exchange; BioPAX) and for exchanging mathematical models of pathways (Systems Biology Markup Langiuage; SBML) are structurally and semantically different. Conversion between formats (making data present in one format available in another format) based on simple one-to-one mappings may lead to loss or distortion of data, is difficult to automate, and often impractical and/or erroneous. This seriously limits the integration of knowledge data and models. In this paper we introduce an approach for such integration based on a bridging format that we named Systems Biology Pathway Exchange (SBPAX) alluding to SBML and BioPAX. It facilitates conversion between data in different formats by a combination of one-to- one mappings to and from SBPAX and operations within the SBPAX data. The concept of SBPAX is to provide a flexible description expanding around essential pathway data – basically the common subset of all formats describing processes, the substances participating in these processes and their locations. SBPAX can act as a repository for molecular interaction data from a variety of sources in different formats, and the information about their relative relationships, thus providing a platform for converting between formats and documenting assumptions used during conversion, gluing (identifying related elements across different formats) and merging (creating a coherent set of data from multiple sources) data. -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer, -
Emerging Whole-Cell Modeling Principles and Methods
Available online at www.sciencedirect.com ScienceDirect Emerging whole-cell modeling principles and methods 1,2,3 1,2,3 1,2,3 Arthur P Goldberg , Bala´ zs Szigeti , Yin Hoon Chew , 1,2,3 1,2 1,2 John AP Sekar , Yosef D Roth and Jonathan R Karr Whole-cell computational models aim to predict cellular models should predict, or how to achieve WC models. phenotypes from genotype by representing the entire genome, Nevertheless, we and others are beginning to leverage the structure and concentration of each molecular species, advances in measurement technology, bioinformatics, each molecular interaction, and the extracellular environment. rule-based modeling, and multi-algorithmic simulation Whole-cell models have great potential to transform to develop WC models [4 ,5 ,6,7 ,8 ,9 ]. However, sub- bioscience, bioengineering, and medicine. However, numerous stantial work remains to achieve WC models [10 ,11 ]. challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in To build consensus on WC modeling, we propose a set of measurement technology, bioinformatics, data sharing, rule- key physical and chemical mechanisms that WC models based modeling, and multi-algorithmic simulation to build the should aim to represent, and a set of key phenotypes that first whole-cell models. We anticipate that ongoing efforts to WC models should aim to predict. We also summarize develop scalable whole-cell modeling tools will enable the experimental and computational progress that is dramatically more comprehensive and more accurate models, making WC modeling feasible, and outline several tech- including models of human cells. nological advances that would help accelerate WC modeling. -
Modelbricks—Modules for Reproducible Modeling Improving Model Annotation and Provenance
www.nature.com/npjsba PERSPECTIVE OPEN ModelBricks—modules for reproducible modeling improving model annotation and provenance Ann E. Cowan 1,2, Pedro Mendes 1,3,4 and Michael L. Blinov 1,5* Most computational models in biology are built and intended for “single-use”; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible—it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. -
1 a Simple and Efficient Pipeline for Construction, Merging, Expansion
bioRxiv preprint doi: https://doi.org/10.1101/2020.11.09.373407; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models Cemal Erdem1,*, Ethan M. Bensman2, Arnab Mutsuddy1, Michael M. Saint-Antoine3, Mehdi Bouhaddou4, Robert C. Blake5, Will Dodd1, Sean M. Gross6, Laura M. Heiser6, F. Alex Feltus7,8,9, and Marc R. Birtwistle1,10,* 1 Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC 2 Computer Science, School of Computing, Clemson University, Clemson, SC 3 Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 4 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 5 Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 6 Department of Biomedical Engineering, Oregon Health & Sciences University, Portland, OR 7 Department of Genetics and Biochemistry, Clemson University, Clemson, SC 8 Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC 9 Center for Human Genetics, Clemson University, Clemson, SC 10 Lead Contact * Correspondence: [email protected] (C.E.) and [email protected] (M.R.B.) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.09.373407; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ABSTRACT The current era of big biomedical data accumulation and availability brings data integration opportunities for leveraging its totality to make new discoveries and/or clinically predictive models.