Discrimination of Dynamical System Models for Biological and Chemical Processes

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics

Mathematical modelling of drug metabolism : using in silico techniques to investigate the cytochrome P450 enzyme system in hepatic reductase null mice

In silico modelling approaches are useful since they can minimise experimental costs and once set up can be used to replace animal testing for drugs. As such modelling techniques need to be developed to reduce dependence on animals for validation of pharmacological efficacy. The work within this thesis shows that computational methods can be used to model biological and medical problems effectively. The main aim of this thesis was to investigate Cytochrome P450 enzymes and their effect on drug metabolism through the use of the Hepatic Reductase Null(HRN) mouse. This was done through using a number of computational models and compared with drug data provided by CXR Biosciences. These models ranged from solely ODE (for comparison to experimental data) to multiscale cellular automata and spatial models when analysing the dynamics on the tumour and cellular level. Once these models were developed the parametric sensitivity was derived in order to see whether there were any needless parameters so that the models were streamlined and to test the model`s robustness against error. The novel three-compartment model was developed in order to explain dynamics within the Hepatic Reductase Null mouse was able to explain much of the behaviour in the supplied data. As well as this it was discovered that the transgenic mouse showed reduced speed in metabolism for many of the drugs analysed which meant that different models were necessary.The cellular automaton program presented is applicable to other areas other than the one stated in Chapter 5. For example any area that deals with interactions between tissue media and drugs as in toxicology and drug studies. The cell cycle inside the code deals with tumour cells but this code can be re-parameterised to concentrate on other types of cell including normal cells, hepatic tissue etc. The inclusion of spatial e ects to the deterministic models like the Cytochrome P450 cycle allows for greater realism in predictions of drug passage through the body or across certain tissue media. Due to this it is useful to include both deterministic and spatial modelling with a multiscale approach in models for drug metabolism.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research CouncilCXR BiosciencesGBUnited Kingdo