Browsing by Author "Badenhorst, Melinda"

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    Investigating the dynamics of hydrogen peroxide metabolism in the peroxiredoxin system of Saccharomyces cerevisiae
    (Stellenbosch : Stellenbosch University, 2020-03) Badenhorst, Melinda; Rohwer, J. M.; Pillay, Che S.; Stellenbosch University. Faculty of Science. Dept. of Biochemistry.
    ENGLISH ABSTRACT: Reactive oxygen species (ROS) are derivations of molecular oxygen that can have detrimental e ects in cells. ROS can readily react with DNA, proteins and lipids often resulting in the loss of structure integrity of these essential cellular components. Living cells are exposed to a normal level of ROS produced by metabolic processes such as aerobic respiration or immune responses. During oxidative stress, however, the level of ROS increases to such an extent that the mechanisms to neutralize them become exhausted. Hydrogen peroxide (H2O2), being a ROS itself, has been associated with cancer, age-related diseases, human immunode ciency virus infection and cardiovascular diseases. Despite these undesired e ects of H2O2, it has also been recognized as a signaling molecule that functions in important cellular processes such as cell proliferation and di erentiation, immune response and apoptosis. Fortunately, the cell is equipped with an antioxidant system that can neutralize H2O2, while maintaining its levels for important signaling functions: the peroxiredoxin system. Peroxiredoxins belong to a family of redox proteins that are ubiquitously expressed across all kingdoms of life. They form part of a larger redox network that accepts reducing energy upstream from the thioredoxin system (thioredoxin, thioredoxin reductase and NADPH) to reduce H2O2 to H2O. The dynamics of the peroxiredoxin system remain poorly understood. The aim of this study was to further our understanding of the kinetic behaviour of this system and how it metabolizes H2O2 by constructing a kinetic model for the peroxiredoxin system of Saccharomyces cerevisiae. A kinetic model could enable us to determine exactly how this system is able to homeostatically maintain levels of H2O2 for signaling function and antioxidant defense. First, the proteins of the peroxiredoxin system were puri ed using recombinant protein expression techniques. The proteins were then used in spectrophotometric assays to obtain experimental data that were used to estimate the required model parameters. Once the model was constructed and evaluated, a stress condition was simulated by subjecting the system to a pulse-like H2O2 input of varying concentrations. The response of the system with regard to its capacity to ef ciently neutralize H2O2 under these conditions were then determined. The peroxiredoxin system proteins were successfully expressed to a high degree of homogeneity. The spectrophotometric assays and parameter estimations resulted in usable values for parameters to construct a simple kinetic model that could describe the system. Model simulations could further explain some discrepancies found in the experimental data for the H2O2 reduction reaction of the system. Additionally, the pulse-like simulation results demonstrated that when a certain H2O2 concentration threshold is reached, the capacity of the system to e ciently neutralize H2O2 becomes limited.
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    Workflow 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.