IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1006220.html
   My bibliography  Save this article

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

Author

Listed:
  • J Kyle Medley
  • Kiri Choi
  • Matthias König
  • Lucian Smith
  • Stanley Gu
  • Joseph Hellerstein
  • Stuart C Sealfon
  • Herbert M Sauro

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.

Suggested Citation

  • J Kyle Medley & Kiri Choi & Matthias König & Lucian Smith & Stanley Gu & Joseph Hellerstein & Stuart C Sealfon & Herbert M Sauro, 2018. "Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-24, June.
  • Handle: RePEc:plo:pcbi00:1006220
    DOI: 10.1371/journal.pcbi.1006220
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006220
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006220&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1006220?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Aaron Mobley & Suzanne K Linder & Russell Braeuer & Lee M Ellis & Leonard Zwelling, 2013. "A Survey on Data Reproducibility in Cancer Research Provides Insights into Our Limited Ability to Translate Findings from the Laboratory to the Clinic," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-4, May.
    2. Geir Kjetil Sandve & Anton Nekrutenko & James Taylor & Eivind Hovig, 2013. "Ten Simple Rules for Reproducible Computational Research," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-4, October.
    3. Michael B. Elowitz & Stanislas Leibler, 2000. "A synthetic oscillatory network of transcriptional regulators," Nature, Nature, vol. 403(6767), pages 335-338, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin Wang & Bo Huang & Xuefeng Xia & Zhirong Sun, 2006. "Funneled Landscape Leads to Robustness of Cell Networks: Yeast Cell Cycle," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-10, November.
    2. Giovanni Russo & Mario di Bernardo & Eduardo D Sontag, 2010. "Global Entrainment of Transcriptional Systems to Periodic Inputs," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-26, April.
    3. Avraham E Mayo & Yaakov Setty & Seagull Shavit & Alon Zaslaver & Uri Alon, 2006. "Plasticity of the cis-Regulatory Input Function of a Gene," PLOS Biology, Public Library of Science, vol. 4(4), pages 1-1, March.
    4. Ankit Gupta & Mustafa Khammash, 2022. "Frequency spectra and the color of cellular noise," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Ci Kong & Yin Yang & Tiancong Qi & Shuyi Zhang, 2025. "Predictive genetic circuit design for phenotype reprogramming in plants," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    6. Bottani, Samuel & Grammaticos, Basile, 2008. "A simple model of genetic oscillations through regulated degradation," Chaos, Solitons & Fractals, Elsevier, vol. 38(5), pages 1468-1482.
    7. Margherita Carletti & Malay Banerjee, 2019. "A Backward Technique for Demographic Noise in Biological Ordinary Differential Equation Models," Mathematics, MDPI, vol. 7(12), pages 1-16, December.
    8. Weiyue Ji & Handuo Shi & Haoqian Zhang & Rui Sun & Jingyi Xi & Dingqiao Wen & Jingchen Feng & Yiwei Chen & Xiao Qin & Yanrong Ma & Wenhan Luo & Linna Deng & Hanchi Lin & Ruofan Yu & Qi Ouyang, 2013. "A Formalized Design Process for Bacterial Consortia That Perform Logic Computing," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
    9. Konstantinos I Papadimitriou & Guy-Bart V Stan & Emmanuel M Drakakis, 2013. "Systematic Computation of Nonlinear Cellular and Molecular Dynamics with Low-Power CytoMimetic Circuits: A Simulation Study," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-24, February.
    10. Inés P Mariño & Alexey Zaikin & Joaquín Míguez, 2017. "A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-25, August.
    11. Zhdanov, Vladimir P., 2012. "Periodic perturbation of genetic oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 45(5), pages 577-587.
    12. Dhiman, Aman & Poria, Swarup, 2018. "Allee effect induced diversity in evolutionary dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 32-38.
    13. Javier Santos-Moreno & Eve Tasiudi & Hadiastri Kusumawardhani & Joerg Stelling & Yolanda Schaerli, 2023. "Robustness and innovation in synthetic genotype networks," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    14. Chen Jia & Ramon Grima, 2024. "Holimap: an accurate and efficient method for solving stochastic gene network dynamics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. T. Ochiai & J. C. Nacher, 2007. "Stochastic analysis of autoregulatory gene expression dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 14(4), pages 377-388, November.
    16. Jacopo Gabrielli & Roberto Di Blasi & Cleo Kontoravdi & Francesca Ceroni, 2025. "Degradation bottlenecks and resource competition in transiently and stably engineered mammalian cells," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    17. Thomas B. Kepler & Timothy C. Elston, 2001. "Stochasticity in Transcriptional Regulation: Origins, Consequences and Mathematical Representations," Working Papers 01-06-033, Santa Fe Institute.
    18. Luis Mier-y-Terán-Romero & Mary Silber & Vassily Hatzimanikatis, 2010. "The Origins of Time-Delay in Template Biopolymerization Processes," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-15, April.
    19. Ashty S. Karim & Dylan M. Brown & Chloé M. Archuleta & Sharisse Grannan & Ludmilla Aristilde & Yogesh Goyal & Josh N. Leonard & Niall M. Mangan & Arthur Prindle & Gabriel J. Rocklin & Keith J. Tyo & L, 2024. "Deconstructing synthetic biology across scales: a conceptual approach for training synthetic biologists," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    20. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1006220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.