ArticleBioinformatics (Oxford, England)2018
PyCoTools: a Python toolbox for COPASI.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
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- Computational modeling of complex bioenergetic mechanisms that modulate CD4+ T cell effector and regulatory functions.NPJ systems biology and applications · 2022Article
- Robustness of the Krebs Cycle under Physiological Conditions and in Cancer: New Clues for Evaluating Metabolism-Modifying Drug Therapies.Biomedicines · 2022Article
- A Negative Feedback Loop and Transcription Factor Cooperation Regulate Zonal Gene Induction by 2, 3, 7, 8-Tetrachlorodibenzo-p-Dioxin in the Mouse Liver.Hepatology communications · 2022Article
- Bayesian calibration, process modeling and uncertainty quantification in biotechnology.PLoS computational biology · 2022Article
- A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.Neuroinformatics · 2022Article
- HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.PLoS computational biology · 2021Article
- pyFOOMB: Python framework for object oriented modeling of bioprocesses.Engineering in life sciences · 2021Article
- A compiler for biological networks on silicon chips.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Motivation: COPASI is an open source software package for constructing, simulating and analyzing dynamic models of biochemical networks. COPASI is primarily intended to be used with a graphical user interface but often it is desirable to be able to access COPASI features programmatically, with a high level interface. Results: PyCoTools is a Python package aimed at providing a high level interface to COPASI tasks with an emphasis on model calibration. PyCoTools enables the construction of COPASI models and the execution of a subset of COPASI tasks including time courses, parameter scans and parameter estimations. Additional 'composite' tasks which use COPASI tasks as building blocks are available for increasing parameter estimation throughput, performing identifiability analysis and performing model selection. PyCoTools supports exploratory data analysis on parameter estimation data to assist with troubleshooting model calibrations. We demonstrate PyCoTools by posing a model selection problem designed to show case PyCoTools within a realistic scenario. The aim of the model selection problem is to test the feasibility of three alternative hypotheses in explaining experimental data derived from neonatal dermal fibroblasts in response to TGF-β over time. PyCoTools is used to critically analyze the parameter estimations and propose strategies for model improvement. Availability and implementation: PyCoTools can be downloaded from the Python Package Index (PyPI) using the command 'pip install pycotools' or directly from GitHub (https://github.com/CiaranWelsh/pycotools). Documentation at http://pycotools.readthedocs.io. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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