Comparative flux control through the cytoplasmic phase of cell wall biosynthesis

The introduction of antibacterial drugs in the middle of the last century heralded a new era in the treatment of infectious disease. However the parallel emergence of antibiotic resistance and decline in new drug discovery threatens these advances. The development of new antibacterials must therefore be a high priority. The biosynthesis of the bacterial cell wall is the target for several clinically important antibacterials. This extracellular structure is essential for bacterial viability due to its role in the prevention of cell lysis under osmotic pressure. Its principal structural component, peptidoglycan, is a polymer of alternating N-acetyl-glucosamine (GlcNAc) and N-acetyl muramic acid (MurNAc) residues crosslinked by peptide bridges anchored by pentapeptide stems attached to the MurNAc moieties. The biosynthesis of peptidoglycan proceeds in three phases. The first, cytoplasmic, phase is catalysed by six enzymes. It forms a uridine diphosphate (UDP) bound MurNAc residue from UDP-GlcNAc and attaches the pentapeptide stem. This phase is a relatively unexploited target for antibacterials, being targeted by a single clinically relevant antibacterial, and is the subject of this thesis. The Streptococcus pneumoniae enzymes were kinetically characterised and in silico models of this pathway were developed for this species and Escherichia coli. These models were used to identify potential drug targets within each species. In addition the potentially clinically relevant interaction between an inhibitor of and feedback loops within this pathway was investigated. The use of direct parameter estimation instead of more traditional approaches to kinetic characterisation of enzymes was found to have significant advantages where it could be successfully applied. This approach required the theoretical analysis of the models used to determine whether unique parameter vectors could be determined. Such an analysis has been completed for a broad range of biologically relevant enzymes. In addition a relatively new approach to such analysis has been developed

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed

A systems biology analysis of feedback control in pheromone signalling of fission yeast

Cell signalling comprises the systems used by cells to detect changes in their environment and to transduce the information into appropriate adjustments enforced by regulatory proteins. Due to its central role in all life processes, the study of cell signalling is a major focus of current biomedical research. The fission yeast Schizosaccharomyces pombe (S. pombe) is a single-celled organism used as a model to simplify the study of eukaryotic cell signalling, as it shares many features of interest with human cells. In this thesis a systems biology approach was used to investigate the roles of feedback regulation to control the dynamics of pheromone signalling in S. pombe. To this end, a quantitative dynamical model was built describing the pheromone-induced activation of the master transcription factor Ste11, as well as the coupled positive and negative feedback loops that arise from Ste11 activity. To constrain the model, a collection of data sets were generated by performing absolute quantification measurements of pheromone-dependent changes in the concentration of the model species. Structural identifiability analyses were used to select the measured species, while confidence intervals of the estimated parameters were determined through profile likelihood estimation. Analysis of the resulting model revealed a role for the pheromone signalling feedback loops to aid in the discrimination of different pheromone input doses. Through their combined action, feedback control defines the concentration and time thresholds in Ste11 activity that must be satisfied for the cell to commit to a sexual development fate

Structural identifiability and indistinguishability analyses of glucose-insulin models

In this thesis, the structural identifiability analyses of established and novel glucose-insulin models was performed, to determine whether the models are globally structurally identifiable with the observations available. Structural identifiability analysis is an essential step in the modelling process and a key prerequisite to experimental design and parameter estimation. Analyses were performed assuming observations of both glucose and insulin concentrations on two versions of the well-cited Minimal Model (MM), the Original Minimal Model (OMM) and Extended Minimal Model (EMM) for the modelling of the responses to an Intravenous Glucose Tolerance Test (IVGTT); a Euglycemic Hyperinsulinemic Clamp model and two novel modified versions of the MM, a Closed-Loop Minimal Model (CLMM) and a Double-Pole in Closed-Loop Minimal Model (DPCLMM), when the models describe a complete course of glucose-insulin dynamics during an IVGTT. The CLMM proved to be unidentifiable so a reparameterisation procedure was performed on this model, yielding a globally structurally identifiable reparameterised model. Parameter estimation using these models was also performed for sets of IVGTT and glucose clamp data. The results of the parameter estimation demonstrated that global structural identifiability does not as always guarantee numerical identifiability, or vice versa. A structural indistinguishability analysis was also performed to compare the MM and the CLMM, given the same observations, where it was shown that both models are distinguishable over both pre- and post- insulin switching phases. This is the first time that all such analyses have been performed on these specific model structures. The generic and numerical results obtained demonstrate issues that may arise in practice when attempting to calculate insulin sensitivity when using such models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo