Browsing by Author "Wissing, Julian"
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- ItemAn investigation into the effects of macromolecular crowding on the kinetics of upper glycolytic enzymes in Saccharomyces cerevisiae(Stellenbosch : Stellenbosch University, 2020-03) Wissing, Julian; Rohwer, Johann; Stellenbosch University. Faculty of Science. Dept. of Biochemistry.ENGLISH ABSTRACT: In order for mathematical models of metabolism to accurately emulate experimental data the conditions in which parameter values are obtained must be close to the actual in vivo environment. However, this is traditionally not the case, with enzyme kinetic studies usually taking place in conditions which are ideal for the enzyme being studied and can be far removed from the actual native conditions the enzyme would be found in. An aspect of the intracellular environment which has not been extensiely covered is the large quantity of diﬀerent macromolecules which occupy it, known as macromolecular crowding. The space occupied by these macromolecules has thermodynamic and kinetic consequences which are not taken into consideration. In this study we mimicked a crowded environment by using the inert polymers PEG 8000 and Ficoll 70 and studied how they aﬀected enzyme kinetic parameter estimates at diﬀerent concentrations. NMR spectroscopy was used to obtain timecourse data for the upper glycolytic enzymes, phosphoglucose isomerase (PGI) and phosphofructokinase (PFK), in cell lysate. Parameter estimates were obtained by ﬁtting NMR time-course data to a kinetic model based on rate equations for the two enzymes. The identiﬁability of each parameter was also determined and could be used to analyse the accuracy of parameter estimation. The aim of this study was to determine the eﬀects of macromolecular crowding on enzyme kinetics and to explore if these eﬀects should be considered when trying to simulate in vivo-like conditions when studying enzyme kinetics. In our results macromolecular crowding was shown to aﬀect the parameter estimates for both enzymes, in particular decreasing their maximal activity, increasing the binding aﬃnity of PFK for fructose-6-phosphate (F6P), and decreasing its aﬃnity for adenosine tri-phosphate (ATP).
- ItemWorkflow for data analysis in experimental and computational systems biology : using Python as glue(MDPI, 2019-07-18) Badenhorst, Melinda; Barry, Christopher J.; Swanepoel, Christiaan J.; Van Staden, Charles Theo; Wissing, Julian; Rohwer, Johann M.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.