Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle, UK.
Department of Biosciences, Durham University, Durham, UK.
Bioinformatics. 2018 Nov 1;34(21):3702-3710. doi: 10.1093/bioinformatics/bty409.
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.
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.
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 data are available at Bioinformatics online.
COPASI 是一个用于构建、模拟和分析生化网络动态模型的开源软件包。COPASI 主要用于图形用户界面,但通常需要能够通过高级接口以编程方式访问 COPASI 的功能。
PyCoTools 是一个 Python 包,旨在为 COPASI 任务提供高级接口,重点是模型校准。PyCoTools 允许构建 COPASI 模型并执行 COPASI 任务的一个子集,包括时间过程、参数扫描和参数估计。提供了额外的“复合”任务,这些任务将 COPASI 任务用作构建块,用于提高参数估计的吞吐量、进行可识别性分析和执行模型选择。PyCoTools 支持对参数估计数据进行探索性数据分析,以协助排查模型校准问题。我们通过提出一个模型选择问题来演示 PyCoTools,该问题旨在在实际场景中展示 PyCoTools。模型选择问题的目的是测试三种替代假设在解释随时间变化的 TGF-β对新生真皮成纤维细胞的实验数据方面的可行性。PyCoTools 用于批判性地分析参数估计,并提出模型改进策略。
PyCoTools 可以使用命令“pip install pycotools”从 Python 包索引 (PyPI) 下载,也可以直接从 GitHub(https://github.com/CiaranWelsh/pycotools)下载。文档在 http://pycotools.readthedocs.io 上。
补充数据可在生物信息学在线获得。