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      Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

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          Abstract

          The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.

          Author summary

          There is considerable value to systems and synthetic biology in creating reproducible models. An essential element of reproducibility is the use of community standards, an often challenging undertaking for modelers. This article describes Tellurium Notebook, a tool for developing dynamical models that provides an intuitive approach to building and reusing models built with community standards. Tellurium automates embedding human–readable representations of COMBINE archives in literate coding notebooks, bringing to systems biology this strategy central to other literate notebook systems such as Mathematica. We show that the ability to easily edit this human–readable representation enables users to test models under a variety of conditions, thereby providing a way to create, reuse, and modify standard–encoded models and simulations, regardless of the user’s level of technical knowledge of said standards.

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          Most cited references46

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          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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            COBRApy: COnstraints-Based Reconstruction and Analysis for Python

            Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/
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              Reproducible research in computational science.

              Roger Peng (2011)
              Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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                Author and article information

                Contributors
                Role: MethodologyRole: SoftwareRole: Writing – original draft
                Role: Software
                Role: Software
                Role: Software
                Role: Software
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                June 2018
                15 June 2018
                : 14
                : 6
                : e1006220
                Affiliations
                [1 ] Department of Bioengineering, University of Washington, Seattle, Washington, United States of America
                [2 ] Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
                [3 ] eScience Institute, University of Washington, Seattle, Washington, United States of America
                [4 ] Department of Neurology and Center for Advanced Research on Diagnostic Assays Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
                UCSD, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-9135-0844
                http://orcid.org/0000-0002-0156-8410
                http://orcid.org/0000-0003-1725-179X
                http://orcid.org/0000-0002-3659-6817
                Article
                PCOMPBIOL-D-17-01894
                10.1371/journal.pcbi.1006220
                6021116
                29906293
                80d764a9-ed62-4192-adbc-9476b054a5a6
                © 2018 Medley et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 November 2017
                : 20 May 2018
                Page count
                Figures: 6, Tables: 1, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: GM081070-01
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: GM123032-01A1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: U01HL122199-02
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000936, Gordon and Betty Moore Foundation;
                Award ID: 3835
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000879, Alfred P. Sloan Foundation;
                Award ID: 2013-10-29
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
                Award ID: 031L0054
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: HHSN266200500021C
                Award Recipient :
                Funded by: National Institutes of Health (US)
                Award ID: U19 AI117873
                Award Recipient :
                JKM, KC, LS, SG, and HMS were supported by NIH grants GM081070-01, GM123032-01A1, NHLBI U01HL122199-02. JH is supported by the Moore/Sloan Data Science Environments Project at the University of Washington supported by grants from the Gordon and Betty Moore Foundation Award #3835 ( https://www.moore.org/) and the Alfred P. Sloan Foundation Award #2013-10-29 ( https://sloan.org/). MK is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver LiSyM, grant number 031L0054 ( https://www.bmbf.de/en/index.html). SG was supported by NIAID Modeling Immunity for Biodefense HHSN266200500021C. SCS was supported by NIH grant U19 AI117873. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2018-06-27
                Tellurium is free/open-source software hosted at the home page http://tellurium.analogmachine.org, which includes links to source code ( https://github.com/sys-bio/tellurium) and documentation ( http://tellurium.readthedocs.io). All COMBINE archives referenced in this manuscript are distributed with Tellurium and additionally available at https://github.com/0u812/tellurium-combine-archive-test-cases.

                Quantitative & Systems biology
                Quantitative & Systems biology

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