The focus of this thesis is the inclusion of stochasticity into mathematical models of assembly with particular interest to the in vitro polymerisation of intermediate filaments, one of three components of the cytoskeleton. From the chemical master equation (CME), two additional models (the reaction rate equations or RREs and the two-moment approximation equations or 2MA equations) are derived. As analysis of the CME is generally intractable, we present the stochastic simulation algorithm (SSA) as a means of reproducing the most probable state of the CME at a given time. The results from the SSA are compared to simulations of both the RREs and the 2MA equations and we find that the three models are in good agreement. Further, the numerical results are compared to mean lengths and length distributions of experimental data which all models are shown to mimic. Mathematical analyses of the RREs demonstrate the conservation of mass in the system, and the unique positive equilibrium is proven to be globally asymptotically stable. Further, the 2MA equations are also shown to have conservation of mass and to possess an analogous equilibrium to the one found in the case of the RREs. In general, this study illustrates how randomness can be incorporated in polymerisation models and highlights the advantages and disadvantages of the different approaches.

Angiogenesis is the formation of new blood vessels from existing ones, and is a key characteristic of tumour progression. The purpose of antiangiogenic (AA) cancer therapies is to disrupt the tumour's blood supply in order to inhibit the delivery of oxygen and nutrients. However, such therapies have demonstrated limited benet to cancer patients: although they delay tumour progression for some types of cancer, they do not consistently improve survival. Several preclinical experimental studies have reported that AA therapies lead to a period of vessel normalisation, during which vessels transition from the leaky, tortuous state that is typical of tumour vasculature to a more stable state where blood perfusion is increased. It has been suggested that normalisation is the reason why some AA therapies lack effcacy. In this thesis, we develop and study mathematical models of various aspects of angiogenesis and vessel normalisation, and we use our results to suggest effective AA therapy regimens. Our first model represents the biochemical interactions and cellular dynamics involved in neovascularisation: we incorporate biological hypotheses to develop a spatially averaged ODE model of vessel formation, and we show that the model admits a number of vascular phenotypes characterised by their degrees of vessel normalisation. We showed that these phenotypes respond differently to different AA treatments. In our second model, we use preclinical tumour size data to develop and parametrise a mixed effects model of vascular tumour growth including vessel normalisation. We use our prediction about the timing of the transient normalisation window to further predict the potential benefits of combining chemotherapy and AA therapy. Lastly, we extend an existing PDE model of vascular tumour growth to incorporate AA therapy and vessel normalisation. We demonstrate that the oscillatory behaviour that arises in the spatially averaged version of the model induces spatial heterogeneity in the spatially extended version, and show that the vessel kill and normalisation parameters (among other parameters) can modulate tumour heterogeneity.

Polymer electrolyte fuel cells are regarded as one of the most promising alternatives to the depleting and high pollutant fossil fuel energy sources. High temperature Polymer electrolyte fuel cells are especially suitable for stationary power applications. However, the length scale of a PEM fuel cells main components range from the micro over the meso to the macro level, and the time scales of various transport processes range from milliseconds up to a few hours. This combination of various spatial and temporal scales makes it extremely challenging to conduct in-situ measurements or other observations through experimental means. Thus, numerical simulation becomes a very important tool to help understand the underlying electrochemical dynamics and transient transport phenomena within PEM fuel cells. In this thesis research a comprehensive, three- dimensional mathematical model is developed which accounts for the convective and diffusive gas flow in the gas channel, multi-component diffusion in the porous backing layer, electrochemical reactions in the catalyst layers, as well as flow of charge and heat through the solid media. The governing equations which mathematically describe these transport processes, are discretized and solved using the finite-volume based software, Ansys FLUENT, with its in-built CFD-solvers. To handle the significant non-linearity stemming from these transport phenomena, a set of numerical under-relaxation schemes are developed using the programming language C++. Good convergence is achieved with these schemes, though the model is based on a serpentine single-channel flow approach. The model results are validated against experimental results and good agreement is achieved. The result shows that the activation overpotential is the greatest cause of voltage loss in a high temperature PEM fuel cell. The degree of oxygen depletion in the catalyst layer, under the ribs, is identified and quantified for a given set of input parameters. This factor is followed by membrane resistance to protonic migration. The model can thus be suitable applied as a tool to predict cell performance. The results also show that performance is influenced by not just one, but a combination of inter-related factors, thus temperature increases, and flow rate changes will only be effective if simultaneously, the concentration of inlet oxygen, and the mobility of proton-ions in the membrane is increased. Not only does the model results verify these phenomena, but provide a quantitative output for any given set of input parameters. It can therefore be suitably applied as an optimisation tool in high temperature PEM fuel cell design.

Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility. Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance. Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results. From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained. The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.

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