Workflow for data analysis in experimental and computational systems biology : using Python as glue

Badenhorst, Melinda ; Barry, Christopher J. ; Swanepoel, Christiaan J. ; Van Staden, Charles Theo ; Wissing, Julian ; Rohwer, Johann M. (2019-07-18)

CITATION: Badenhorst, M., et al. 2019. Workflow for data analysis in experimental and computational systems biology : using python as glue. Processes, 7(7):460, doi:10.3390/pr7070460.

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Publication of this article was funded by the Stellenbosch University Open Access Fund


ENGLISH ABSTRACT: Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.

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