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BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis

Russell Kai Jie Ngo, View ORCID ProfileJing Wui Yeoh, Gerald Horng Wei Fan, Wilbert Keat Siang Loh, View ORCID ProfileChueh Loo Poh
doi: https://doi.org/10.1101/2021.02.23.432592
Russell Kai Jie Ngo
1Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
2Life Sciences Institute, NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore
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Jing Wui Yeoh
2Life Sciences Institute, NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore
3Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Gerald Horng Wei Fan
1Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
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Wilbert Keat Siang Loh
1Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
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Chueh Loo Poh
1Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
2Life Sciences Institute, NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore
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  • For correspondence: poh.chuehloo@nus.edu.sg
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Abstract

Summary Modelling in Synthetic Biology constitutes a powerful tool in our continuous search for improved performance with rational Design-Build-Test-Learn approach. In particular, kinetic models unravel system dynamics, enabling system analysis for guiding experimental designs. However, a systematic yet modular pipeline that allows one to identify the “right” model and guide the experimental designs while tracing the entire model development and analysis is still lacking. Here, we introduce a unified python package, BMSS2, which offers the principal tools in model development and analysis—simulation, Bayesian parameter inference, global sensitivity analysis, with an emphasis on model selection, and a priori and a posteriori identifiability analysis. The whole package is database-driven to support interactive retrieving and storing of models for reusability. This allows ease of manipulation and deposition of models for the model selection and analysis process, thus enabling better utilization of models in guiding experimental designs.

Availability and Implementation The python package and examples are available on https://github.com/EngBioNUS/BMSS2. A web page which allows users to browse and download the models (SBML format) in MBase is also available with the link provided on GitHub.

Supplementary Information Supplementary data is available.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/EngBioNUS/BMSS2

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 24, 2021.
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BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis
Russell Kai Jie Ngo, Jing Wui Yeoh, Gerald Horng Wei Fan, Wilbert Keat Siang Loh, Chueh Loo Poh
bioRxiv 2021.02.23.432592; doi: https://doi.org/10.1101/2021.02.23.432592
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BMSS2: a unified database-driven modelling tool for systematic model selection and identifiability analysis
Russell Kai Jie Ngo, Jing Wui Yeoh, Gerald Horng Wei Fan, Wilbert Keat Siang Loh, Chueh Loo Poh
bioRxiv 2021.02.23.432592; doi: https://doi.org/10.1101/2021.02.23.432592

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