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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Bearup, Daniel James
Languages: English
Types: Doctoral thesis
Subjects: QP
The introduction of antibacterial drugs in the middle of the last century heralded a new era in\ud the treatment of infectious disease. However the parallel emergence of antibiotic resistance and\ud decline in new drug discovery threatens these advances. The development of new antibacterials\ud must therefore be a high priority.\ud The biosynthesis of the bacterial cell wall is the target for several clinically important antibacterials.\ud This extracellular structure is essential for bacterial viability due to its role in the\ud prevention of cell lysis under osmotic pressure. Its principal structural component, peptidoglycan,\ud is a polymer of alternating N-acetyl-glucosamine (GlcNAc) and N-acetyl muramic acid\ud (MurNAc) residues crosslinked by peptide bridges anchored by pentapeptide stems attached\ud to the MurNAc moieties. The biosynthesis of peptidoglycan proceeds in three phases. The\ud first, cytoplasmic, phase is catalysed by six enzymes. It forms a uridine diphosphate (UDP)\ud bound MurNAc residue from UDP-GlcNAc and attaches the pentapeptide stem. This phase is\ud a relatively unexploited target for antibacterials, being targeted by a single clinically relevant\ud antibacterial, and is the subject of this thesis.\ud The Streptococcus pneumoniae enzymes were kinetically characterised and in silico models of\ud this pathway were developed for this species and Escherichia coli. These models were used to\ud identify potential drug targets within each species. In addition the potentially clinically relevant\ud interaction between an inhibitor of and feedback loops within this pathway was investigated.\ud The use of direct parameter estimation instead of more traditional approaches to kinetic characterisation\ud of enzymes was found to have significant advantages where it could be successfully\ud applied. This approach required the theoretical analysis of the models used to determine\ud whether unique parameter vectors could be determined. Such an analysis has been completed\ud for a broad range of biologically relevant enzymes. In addition a relatively new approach to\ud such analysis has been developed.
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    • 1.3 In silico modelling of metabolic pathways . . . . . . . . . . . . . . . . . . . . . . 19 1.3.1 Mathematical modelling of metabolism . . . . . . . . . . . . . . . . . . . 20 1.3.2 Kinetic characterisation of enzymes . . . . . . . . . . . . . . . . . . . . . 22 1.3.3 Numerical solution of differential equations . . . . . . . . . . . . . . . . . 23 1.4 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.4.1 Outline of the remainder of the thesis . . . . . . . . . . . . . . . . . . . . 24 2.3.10 Coomassier staining of gels . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.3.11 Determination of protein concentration . . . . . . . . . . . . . . . . . . . 35 2.4 Biochemical spectrophotometric assays . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.1 Determination of concentration of substrates . . . . . . . . . . . . . . . . 35 2.4.2 Continuous ADP production assay . . . . . . . . . . . . . . . . . . . . . . 37 2.4.3 Continuous phosphate production assay . . . . . . . . . . . . . . . . . . . 38 2.4.4 Continuous assay of MurB activity . . . . . . . . . . . . . . . . . . . . . 40 2.4.5 Continuous assay of Lactate dehydrogenase activity . . . . . . . . . . . . 41 2.4.6 Determination of pathway fluxes . . . . . . . . . . . . . . . . . . . . . . . 41 2.4.7 Pre-steady state kinetics experiments . . . . . . . . . . . . . . . . . . . . 42
    • 3. Theoretical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.2 Structural identifiability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.1 Taylor series approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Pohjanpalo's Jacobian rank test . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.3 Input-output relationship approach . . . . . . . . . . . . . . . . . . . . . 52 3.2.4 Application of the identifiability analysis techniques to a simple model . 59 3.3 Structural indistinguishability analysis . . . . . . . . . . . . . . . . . . . . . . . 62 3.4 Numerical parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5 Simulation of reaction species concentrations . . . . . . . . . . . . . . . . . . . . 67 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
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