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Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’

Laboratory for Molecular Systems Biology, Department of Biochemistry, Stellenbosch University, Stellenbosch 7600, South Africa
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These authors contributed equally to this work.
Processes 2019, 7(7), 460; https://doi.org/10.3390/pr7070460
Received: 10 June 2019 / Revised: 10 July 2019 / Accepted: 11 July 2019 / Published: 18 July 2019
(This article belongs to the Special Issue In Silico Metabolic Modeling and Engineering)
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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. View Full-Text
Keywords: enzyme kinetics; Jupyter notebook; kinetic modelling; Matplotlib; NMR spectroscopy; optimisation; parametrisation; PySCeS; SciPy; validation enzyme kinetics; Jupyter notebook; kinetic modelling; Matplotlib; NMR spectroscopy; optimisation; parametrisation; PySCeS; SciPy; validation
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Badenhorst, M.; Barry, C.J.; Swanepoel, C.J.; van Staden, C.T.; Wissing, J.; Rohwer, J.M. Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’. Processes 2019, 7, 460.

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