Ion channel models are related to non-equilibrium statistical physics, fluid mechanics and electromagnetism. Some classes of ordinary differential equations that model ion channels can be seen as a limit of finite state-space continuous-time Markov chains. The purpose of this thesis is to qualitatively investigate the numerical results of systems of equations that incorporate ion channels modeled by such Markov chains and an electrical circuit model of a single neuron with isopotential extracellular space. This may be useful for making more detailed micro-physical simulations of neurons. A subset of the Rallpack benchmarks is conducted in order to evaluate the accuracy of the electrical circuit model of the transmembrane voltage propagation. In order to test the tau-leap method employed to simulate the Markov-chain based ion channel models a cylindrical geometry is implemented. Convergence properties are presented in terms of mean interspike intervals of the transmembrane voltages for different time- and spatial discretisations. Accuracy of the tau-leap method is presented in relation to the deterministic versions of the ion channel models. The results show that the method used to simulate the transmembrane voltages is accurate and that while the tau-leap method is convergent in the mean interspike interval sense, it is not conclusive how accurate it is compared to the corresponding ordinary differential equations or how efficient it is.

Continuous time Markov chains are often used in the literature to model the dynamics of a system with low species count and uncertainty in transitions. In this paper, we investigate three particular algorithms that can be used to numerically simulate continuous time Markov chain models (a stochastic simulation algorithm, explicit and implicit tau-leaping algorithms). To compare these methods, we used them to analyze two stochastic infection models with different level of complexity. One of these models describes the dynamics of Vancomycin-Resistant Enterococcus (VRE) infection in a hospital, and the other is for the early infection of Human Immunodeficiency Virus (HIV) within a host. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have similar computational efficiency for the VRE model due to the low number of species and small number of transitions. However, we found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.

In this paper, we investigate three particular algorithms: A sto- chastic simulation algorithm (SSA), and explicit and implicit tau-leaping al- gorithms. To compare these methods, we used them to analyze two infection models: A Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunode ciency Virus (HIV) within host in- fection model. While the rst has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative effciency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational effciency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modication of tau-Leaping methods are preferred.