Published Article / There are several HIV drug resistant interpretation algorithms which produce different resistance measures even if applied to the same resistance profile. This discrepancy leads to confusion in the mind of the physician when choosing the best ARV therapy.

Continuous-time Markov chains (CTMC's) form a convenient mathematical framework for analyzing random systems across many different disciplines. A specific research problem that is often of interest is to try to predict maximum probability sequences of state transitions given initial or boundary conditions. This work shows how to solve this problem exactly through an efficient dynamic programming algorithm. We demonstrate our approach through two different applications - ranking mutational pathways of HIV virus based on their probabilities, and determining the most probable failure sequences in complex fault-tolerant engineering systems. Even though CTMC's have been used extensively to realistically model many types of complex processes, it is often a standard practice to eventually simplify the model in order to perform the state evolution analysis. As we show here, simplifying approaches can lead to inaccurate and often misleading solutions. Therefore we expect our algorithm to find a wide range of applications across different domains.

Continuous-time Markov chains (CTMC's) form a convenient mathematical framework for analyzing random systems across many different disciplines. A specific research problem that is often of interest is to try to predict maximum probability sequences of state transitions given initial or boundary conditions. This work shows how to solve this problem exactly through an efficient dynamic programming algorithm. We demonstrate our approach through two different applications - ranking mutational pathways of HIV virus based on their probabilities, and determining the most probable failure sequences in complex fault-tolerant engineering systems. Even though CTMC's have been used extensively to realistically model many types of complex processes, it is often a standard practice to eventually simplify the model in order to perform the state evolution analysis. As we show here, simplifying approaches can lead to inaccurate and often misleading solutions. Therefore we expect our algorithm to find a wide range of applications across different domains.

Continuous-time Markov chains (CTMC's) form a convenient mathematical framework for analyzing random systems across many different disciplines. A specific research problem that is often of interest is to try to predict maximum probability sequences of state transitions given initial or boundary conditions. This work shows how to solve this problem exactly through an efficient dynamic programming algorithm. We demonstrate our approach through two different applications - ranking mutational pathways of HIV virus based on their probabilities, and determining the most probable failure sequences in complex fault-tolerant engineering systems. Even though CTMC's have been used extensively to realistically model many types of complex processes, it is often a standard practice to eventually simplify the model in order to perform the state evolution analysis. As we show here, simplifying approaches can lead to inaccurate and often misleading solutions. Therefore we expect our algorithm to find a wide range of applications across different domains.

Doctor of Philosophy(PhD) / ABSTRACT Despite the extensive quantitative and qualitative knowledge about therapeutic regimens and the molecular biology of HIV/AIDS, the eradication of HIV infection cannot be achieved with available antiretroviral regimens. HIV drug resistance remains the most challenging factor in the application of approved antiretroviral agents. Previous investigations and existing HIV/AIDS models and algorithms have not enabled the development of long-lasting and preventive drug agents. Therefore, the analysis of the dynamics of drug resistance and the development of sophisticated HIV/AIDS analytical algorithms and models are critical for the development of new, potent antiviral agents, and for the greater understanding of the evolutionary behaviours of HIV. This study presents novel computational methods for the analysis of drug-resistance dynamics, including: viral sequences, phenotypic resistance, immunological and virological responses and key clinical data, from HIV-infected patients at Royal Prince Alfred Hospital in Sydney. The lability of immunological and virological responses is analysed in the context of the evolution of antiretroviral drug-resistance mutations. A novel Bayesian algorithm is developed for the detection and classification of neutral and adaptive mutational patterns associated with HIV drug resistance. To simplify and provide insights into the multifactorial interactions between viral populations, immune-system cells, drug resistance and treatment parameters, a Bayesian graphical model of drug-resistance dynamics is developed; the model supports the exploration of the interdependent associations among these dynamics.

Doctor of Philosophy(PhD) / ABSTRACT Despite the extensive quantitative and qualitative knowledge about therapeutic regimens and the molecular biology of HIV/AIDS, the eradication of HIV infection cannot be achieved with available antiretroviral regimens. HIV drug resistance remains the most challenging factor in the application of approved antiretroviral agents. Previous investigations and existing HIV/AIDS models and algorithms have not enabled the development of long-lasting and preventive drug agents. Therefore, the analysis of the dynamics of drug resistance and the development of sophisticated HIV/AIDS analytical algorithms and models are critical for the development of new, potent antiviral agents, and for the greater understanding of the evolutionary behaviours of HIV. This study presents novel computational methods for the analysis of drug-resistance dynamics, including: viral sequences, phenotypic resistance, immunological and virological responses and key clinical data, from HIV-infected patients at Royal Prince Alfred Hospital in Sydney. The lability of immunological and virological responses is analysed in the context of the evolution of antiretroviral drug-resistance mutations. A novel Bayesian algorithm is developed for the detection and classification of neutral and adaptive mutational patterns associated with HIV drug resistance. To simplify and provide insights into the multifactorial interactions between viral populations, immune-system cells, drug resistance and treatment parameters, a Bayesian graphical model of drug-resistance dynamics is developed; the model supports the exploration of the interdependent associations among these dynamics.