published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

Ovarian cancer is the most lethal gynecological malignancy. Most of ovarian cancer patients relapse and subsequently die due to the development of resistance to chemotherapy. P73 belongs to the tumor suppressor p53 family. Like p53, the transcriptionally active TAp73 can bind specifically to p53 responsive elements and transactivates some of the p53 target genes, and finally leads to cell cycle arrest and apoptosis. TAp73 can be induced by DNA damage to enhance cellular sensitivity to anticancer agents in human cancer cells. However, the functions of TAp73 in ovarian cancer cells and the role in the regulation of cellular response to commonly used chemotherapeutic agents cisplatin are still poorly understood. The aims of this study were to examine the functions of TAp73 in ovarian cancer cells and its role in cellular response to cisplatin, as well as the relationship between TAp73 and p53 in ovarian cancer cells. Functional studies showed that over-expression of TAp73alpha (TAp73α) inhibited cell proliferation, colony formation ability and anchorage-independent growth of ovarian cancer cells, and this was irrespective of p53 expression status. In addition, TAp73α inhibited cell growth by arresting cell cycle at G2/M phase and up-regulating the expressions of G2/M regulators of p21, 14-3-3sigma and GADD45α. TAp73α enhanced the cellular sensitivity to cisplatin through the activation of JNK signaling pathway, at least partially, in ovarian cancer cells. TAp73α activated the JNK pathway through the up-regulation of its target gene GADD45α and subsequent activation of MKK4, the JNK up-stream kinase. Inhibition of JNK activity by a specific inhibitor (SP600125) or small interfering RNAs (siRNAs) significantly abrogated TAp73-mediated apoptosis induced by cisplatin. Moreover, the activations of MKK4, JNK and c-Jun were abolished when GADD45α was knocked down by siRNAs, and the JNK-dependent apoptosis was not observed. Collectively, these results supported that TAp73α was able to mediate apoptotic response to cisplatin through the GADD45α/MKK4/JNK signaling pathway, which was respective of p53 expression status. Further investigation on the relationship between TAp73α and p53 demonstrated that TAp73α increased p53 protein, but not mRNA expression by attenuating p53 protein degradation in wild-type p53 ovarian cancer cells. TAp73α could directly interact with p53 protein, which might interfere with the binding ability of MDM2 to p53, and consequently block the p53 protein degradation. In addition, TAp73α inactivated the Akt and ERK pathways and activated the p38 pathway in response to cisplatin in wild-type p53 OVCA433, but not in null-p53 SKOV3 cells, suggesting that the effect of TAp73α on these pathways might be p53-dependent. These results indicated that a functional cooperation of TAp73α and p53, to some extent, existed in ovarian cancer cells. In conclusion, this study demonstrated that TAp73α acted as a tumor suppressor in ovarian carcinogenesis. It promoted the cellular sensitivity to cisplatin via, at least partially, the activation of JNK signaling pathway. These TAp73α functions were irrespective of p53 expression. In addition, TAp73α was able to bind to p53 and increase p53 expression. / published_or_final_version / Obstetrics and Gynaecology / Doctoral / Doctor of Philosophy

Computational and mathematical models of cellular processes promise great benets in important elds such as molecular biology and medicine. Increasingly, researchers are incorporating the fundamentally discrete and stochastic nature of biochemical processes into the mathematical models that are intended to represent them. This has led to the formulation of models for genetic networks as continuous-time, discrete state, Markov processes, giving rise to the so-called Chemical Master Equation (CME), which is a discrete, partial dierential equation, that governs the evolution of the associated probability distribution function (PDF). While promising many insights, the CME is computationally challenging, especially as the dimension of the model grows. In this thesis, novel methods are developed for computing the PDF of the Master Equation. The problems associated with the high-dimensional nature of the Chemical Master Equation are addressed by adapting Krylov methods, in combination with Finite State Projection methods, to derive algorithms well-suited to the Master Equation. Variations of the approach that incorporate the Strang splitting and a stochastic analogue of the total quasi-steady-state approximation are also derived for chemical systems with disparate rates. Monte Carlo approaches, such as the Stochastic Simulation Algorithm, that simulate trajectories of the process governed by the CME have been a very popular approach and we compare these with the PDF approaches developed in this thesis. The thesis concludes with a discussion of various implementation issues along with numerical results for important applications in systems biology, including the gene toggle, the Goldbeter-Koshland switch and the Mitogen-Activated Protein Kinase Cascade.

Computational and mathematical models of cellular processes promise great benets in important elds such as molecular biology and medicine. Increasingly, researchers are incorporating the fundamentally discrete and stochastic nature of biochemical processes into the mathematical models that are intended to represent them. This has led to the formulation of models for genetic networks as continuous-time, discrete state, Markov processes, giving rise to the so-called Chemical Master Equation (CME), which is a discrete, partial dierential equation, that governs the evolution of the associated probability distribution function (PDF). While promising many insights, the CME is computationally challenging, especially as the dimension of the model grows. In this thesis, novel methods are developed for computing the PDF of the Master Equation. The problems associated with the high-dimensional nature of the Chemical Master Equation are addressed by adapting Krylov methods, in combination with Finite State Projection methods, to derive algorithms well-suited to the Master Equation. Variations of the approach that incorporate the Strang splitting and a stochastic analogue of the total quasi-steady-state approximation are also derived for chemical systems with disparate rates. Monte Carlo approaches, such as the Stochastic Simulation Algorithm, that simulate trajectories of the process governed by the CME have been a very popular approach and we compare these with the PDF approaches developed in this thesis. The thesis concludes with a discussion of various implementation issues along with numerical results for important applications in systems biology, including the gene toggle, the Goldbeter-Koshland switch and the Mitogen-Activated Protein Kinase Cascade.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 137-154).

Diss. (sammanfattning) Stockholm : Karol. Inst., 2002. / Härtill 4 uppsatser.

Thesis (M. Med. Sc.)--University of Hong Kong, 2007.

Thesis (Ph. D.)--Case Western Reserve University, 2008. / [School of Medicine] Department of Neurosciences. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 143-174). Also available in print.

Thesis (Ph. D.)--Case Western Reserve University, 2005. / [School of Medicine] Department of Genetics. Includes bibliographical references. Available online via OhioLINK's ETD Center.