Dynamic modelling of electronic nose systems

This thesis details research into the modelling of the dynamic responses of electronic nose systems to odour inputs. Most electronic nose systems contain an array of between 4 and 32 odour sensors, each of which respond in varying degrees to a range of different gaseous stimuli. In almost all electronic nose systems in use today, the steady-state responses of the odour sensors are extracted and passed to one of a variety of pattern recognition systems. The primary aim of this thesis is to investigate the use of information contained within the dynamic portion of the sensor response for odour classification. System identification techniques using linear time-invariant black box models are applied to both extracted steady state and full dynamic data sets collected from experiments designed to assess the ability of an electronic nose system to discriminate between the strain and growth phases of samples of cyanobacteria (blue-green algae). The results obtained are compared with those obtained elsewhere using the same data, analysed with nonlinear artificial neural networks. A physical model for the electrochemical mechanisms resulting in the measured responses is translated into a mathematical model. This model consists of a system of coupled nonlinear ordinary differential equations. The model is analysed, and the theoretical structural identifiability of the model is investigated and established. The parametric model is then fitted to data collected from experiments with simple (single chemical species) odours. An odour discrimination method is developed, based upon the extraction of physically significant parameters from experimental data. This technique is evaluated and compared with the previously explored black box modelling techniques. The discrimination technique is then extended to the analysis of complex odours, again using the cyanobacteria data sets. Successful classification rates are compared with those obtained earlier in the thesis, and elsewhere with neural networks applied to steady state data

Mathematical modelling of immune condition dynamics : a clinical perspective

This thesis describes the use of mathematical modelling to analyse the treatment of patients with immune disorders; namely, Multiple Myeloma, a cancer of plasma cells that create excess monoclonal antibody; and kidney transplants, where the immune system produces polygonal antibodies against the implanted organ. Linear and nonlinear compartmental models play an important role in the analysis of biomedical systems; in this thesis several models are developed to describe the in vivo kinetics of the antibodies that are prevalent for the two disorders studied. These models are validated against patient data supplied by clinical collaborators. Through this validation process important information regarding the dynamic properties of the clinical treatment can be gathered. In order to treat patients with excess immune antibodies the clinical staff wish to reduce these high levels in the patient to near healthy concentrations. To achieve this they have two possible treatment modalities: either using artificial methods to clear the material, a process known as apheresis, or drug therapy to reduce the production of the antibody in question. Apheresis techniques differ in their ability to clear different immune complexes; the effectiveness of a range of apheresis techniques is categorised for several antibody types and antibody fragments. The models developed are then used to predict the patient response to alternative treatment methods, and schedules, to find optimal combinations. In addition, improved measurement techniques that may offer an improved diagnosis are suggested. Whilst the overall effect of drug therapy is known, through measuring the concentration of antibodies in the patient’s blood, the short-term relationship between drug application and reduction in antibody synthesis is still not well defined; therefore, methods to estimate the generation rate of the immune complex, without the need for invasive procedures, are also presented

Mathematical modelling of immune condition dynamics : a clinical perspective

This thesis describes the use of mathematical modelling to analyse the treatment of patients with immune disorders; namely, Multiple Myeloma, a cancer of plasma cells that create excess monoclonal antibody; and kidney transplants, where the immune system produces polygonal antibodies against the implanted organ. Linear and nonlinear compartmental models play an important role in the analysis of biomedical systems; in this thesis several models are developed to describe the in vivo kinetics of the antibodies that are prevalent for the two disorders studied. These models are validated against patient data supplied by clinical collaborators. Through this validation process important information regarding the dynamic properties of the clinical treatment can be gathered. In order to treat patients with excess immune antibodies the clinical staff wish to reduce these high levels in the patient to near healthy concentrations. To achieve this they have two possible treatment modalities: either using artificial methods to clear the material, a process known as apheresis, or drug therapy to reduce the production of the antibody in question. Apheresis techniques differ in their ability to clear different immune complexes; the effectiveness of a range of apheresis techniques is categorised for several antibody types and antibody fragments. The models developed are then used to predict the patient response to alternative treatment methods, and schedules, to find optimal combinations. In addition, improved measurement techniques that may offer an improved diagnosis are suggested. Whilst the overall effect of drug therapy is known, through measuring the concentration of antibodies in the patient’s blood, the short-term relationship between drug application and reduction in antibody synthesis is still not well defined; therefore, methods to estimate the generation rate of the immune complex, without the need for invasive procedures, are also presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Nonlinear compartmental model indistinguishability

A version of the local state isomorphism theorem is used to show how nonlinear model structures, within some specified class, can be tested for indistinguishability from some given structure via a suitable choice of their respective parameters. This theory is applied to a two-compartmental structure with an assumed Michaelis-Menten elimination from compartment 1. Models tested for indistinguishability include structures with Michaelis-Menten elimination from compartment 2 in addition to, or instead of, that from compartment 1