Browsing by Author "Van Staden, Charles Theo"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Towards a kinetic model of the Entner-Doudoroff pathway in Zymomonas mobilis
    (Stellenbosch : Stellenbosch University, 2014-12) Van Staden, Charles Theo; Rohwer, Johann M.; Snoep, Jacky L.; Stellenbosch University. Faculty of Science. Dept. of Biochemistry.
    ENGLISH ABSTRACT: Metabolic networks of cellular systems are complex, in that there are numerous components with multiple non-linear interactions. To understand how these networks work they are often split into manageable pieces and studied individually. However, an individual part is unable to account for the complex properties of systems. In order to study these interactions the eld of systems biology has developed. Systems biology makes use of computers to construct models as a method to describe aspects of living systems. Using cellular pathways, kinetic models of metabolic pathways can be constructed and used as a tool to study the biological systems and provide a quantitative description. This thesis describes the quantitative analysis of a bacterium using a systems biology approach. Zymomonas mobilis is a rod shaped, Gram-negative, non-mobile facultative anaerobe and has one of the fastest observed fermentations, yet least energy e cient extractions found in nature. Furthermore it is the only known micro-organism to use the Entner-Doudoro (2-keto-3-deoxy-6- phosphogluconate) pathway anaerobically. The low energy yield of fermentation in Z. mobilis is a result of the usage of the Entner-Doudoro glycolytic pathway, which has half the energy yield per mol substrate compared to the well known Embden-Meyerhof-Parnas glycolytic pathway. The work presented in this thesis forms part of a larger project to compare glycolytic regulation in di erent micro-organisms Z. mobilis, Escherichia coli, Saccharomyces cerevisiae and Lactococcus lactis. These organisms were chosen based on their usage of di erent glycolytic mechanisms. Kinetic models are suitable tools to draw a comparison between these organisms. The emphasis here is on the construction of a kinetic model of the Entner-Doudoroff glycolytic pathway as it occurs in Z. mobilis. The aim of this thesis was to characterise as many of the Entner-Doudoro pathway enzymes as possible, under standard conditions. This was done using enzyme assays, to obtain the kinetic parameters of each of the enzymes. Microtitre plate assays were used to characterise most of the enzymes of the Entner-Doudoro pathway. However, not all characterisations could be done using plate assay methods, as some intermediates were not commercially available to perform coupled assays. Nuclear magnetic resonance (NMR) spectroscopy was used to characterise these enzymes. These experimentally obtained parameters were then incorporated in a mathematical framework. Time simulations on the initial model were unable to reach a steady-state, with a build up of metabolic intermediates. A secondary model was constructed (using calculated maximal activities) which allowed us to identify discrepancies in the initial model. This showed that the experimentally determined maximal activities of three enzymes in lower glycolysis were unrealistically low, which might be due to protein denaturation by sonication. A nal model was constructed which incorporated a correction factor for these three enzymes. The models' predicted output (steady-state concentrations and ux) was compared to that of either literature or experimentally determined values, as a method to validate the model. The model output compared well to literature values. The constructed and partially validated kinetic model was then used as an analytical tool to identify points of control and regulation of glycolysis in Z. mobilis. The model presented in this work was also compared to published models. Our model relies much less on literature obtained values, and uses kinetic parameters experimentally determined under the same conditions. The parameters of the published models were obtained from the literature and in many instances, the assay conditions for these parameters were set-up to yield the maximum activity under non-physiological conditions. Furthermore, the number of excluded or assumed parameters is much less in our model. However, introduction of a milder, more predictable extraction technique for preparing cell lysates, should be considered for future work, to obtain the parameters that was not determined during this study. The published models do include reactions not included in our model (e.g ATP metabolism), which should be considered for inclusion, as we strive to construct a detailed kinetic model of glycolysis in Z. mobilis in the future.
  • Thumbnail Image
    Item
    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.