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

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dc.contributor.authorBadenhorst, Melindaen_ZA
dc.contributor.authorBarry, Christopher J.en_ZA
dc.contributor.authorSwanepoel, Christiaan J.en_ZA
dc.contributor.authorVan Staden, Charles Theoen_ZA
dc.contributor.authorWissing, Julianen_ZA
dc.contributor.authorRohwer, Johann M.en_ZA
dc.date.accessioned2019-07-31T07:29:56Z
dc.date.available2019-07-31T07:29:56Z
dc.date.issued2019-07-18
dc.descriptionCITATION: 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.en_ZA
dc.descriptionThe original publication is available at https://www.mdpi.comen_ZA
dc.descriptionPublication of this article was funded by the Stellenbosch University Open Access Funden_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.urihttps://www.mdpi.com/2227-9717/7/7/460
dc.description.versionPublisher's versionen_ZA
dc.format.extent17 pages : illustrationsen_ZA
dc.identifier.citationBadenhorst, 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/pr7070460en_ZA
dc.identifier.issn2227-9717 (online)
dc.identifier.otherdoi:10.3390/pr7070460
dc.identifier.urihttp://hdl.handle.net/10019.1/106332
dc.language.isoen_ZAen_ZA
dc.publisherMDPIen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectEnzyme kineticsen_ZA
dc.subjectData analysis (Quantitative research)en_ZA
dc.subjectComputational biologyen_ZA
dc.subjectNuclear Magnetic Resonance (NMR) spectroscopyen_ZA
dc.subjectBiology, Experimentalen_ZA
dc.titleWorkflow for data analysis in experimental and computational systems biology : using Python as glueen_ZA
dc.typeArticleen_ZA
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