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PyCoTools: a Python toolbox for COPASI.
Welsh, Ciaran M; Fullard, Nicola; Proctor, Carole J; Martinez-Guimera, Alvaro; Isfort, Robert J; Bascom, Charles C; Tasseff, Ryan; Przyborski, Stefan A; Shanley, Daryl P.
Afiliação
  • Welsh CM; Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.
  • Fullard N; Department of Biosciences, Durham University, Durham, UK.
  • Proctor CJ; Institute of Cellular Medicine, Newcastle University, Newcastle, UK.
  • Martinez-Guimera A; Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.
  • Isfort RJ; The Proctor & Gamble Company, Cincinnati, USA.
  • Bascom CC; The Proctor & Gamble Company, Cincinnati, USA.
  • Tasseff R; The Proctor & Gamble Company, Cincinnati, USA.
  • Przyborski SA; Department of Biosciences, Durham University, Durham, UK.
  • Shanley DP; Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.
Bioinformatics ; 34(21): 3702-3710, 2018 11 01.
Article em En | MEDLINE | ID: mdl-29790940
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-ß 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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Documentação Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Documentação Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article
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