Chapter Summary and Conclusions In this thesis, the dynamics of two important intracellular processes are investigated. In Part I, the p53-AKT genetic network is modeled in terms of differential equations to simulate the dynamics of the regulation of cell survival and death in the presence of DNA damage and growth factor. In Part II, a new model to approximate pre-mRNA folding during the process of transcription is proposed to study the dynamics of dystrophin pre-mRNA secondary structures during transcription. Despite the numerous coupled interactions among key cancer-relevant genes in the p53-AKT genetic network and the extensive regulatory points at which p53 and AKT impinge on apoptosis, it is somewhat surprising that no prior p53-AKT model has been analyzed. Simulation of the p53-AKT genetic network predicts a bistable switch that regulates pro-survival and pro-death states, i.e., there is a range of values of control parameters where two stable steady states coexist. This prediction is not only novel but also a strong one given that the bistability phenomenon is generally conserved under wide variations of kinetic parameters and under network models with various levels of mechanistic details. In contrast to a monostable switch, a bistable switch prevents arbitrary switching between the two cellular states in the presence of cellular noise. Furthermore, finite perturbations within the bistable region can switch the system from one cellular state to the other, thereby providing an additional mechanism to regulate the switch. Taken together, analysis of the switching 157 dynamics between the two steady states of the system leads to the understanding of how cells regulate the switch between cell survival and death. Interestingly, bistability between apoptotic and non-apoptotic protein activities has also been predicted from models analyzing specific sub-networks of the apoptosis pathway. For instance, bistability is predicted in mutual activation loops between CASPASE-3 and CASPASE-9 and between CASPASE-3 and CASPASE-8 (Legewie et al., 2006; Eissing et al., 2004, 2005). Bagci et al. (2006), on the other hand, predicted bistability arising from the kinetic cooperativity in the formation of the APOPTOSOME complex, which composed of APAF-1, PRO-CASPASE-9 and CYTOCHROME C. The p53-AKT network is also predicted to exhibit oscillatory dynamics under certain conditions. The role of these oscillations of intracellular p53 protein levels upon DNA damage in cell survival and death in the context of the p53-AKT network is investigated. Two key effects of p53 oscillations are elucidated – lowering of proapoptotic threshold level of irradiation intensity at which switching to pro-apoptotic cellular state occurs and ability to express more amounts of p53-target genes products. As a result, oscillatory p53 could commit cells to apoptosis faster and at lower threshold level of irradiation intensity. Therefore, p53 oscillations are predicted to sensitize cells towards apoptosis upon IR. This prediction can also be interpreted as a decrease in tolerance to IR-induced DNA damage in cells that induce p53 oscillations. As the consequences of p53 oscillations have so far remain elusive, the results obtained in this study are significant. Most importantly, the results show for the first time that p53 oscillations have biological functions. It is worth noting that this prediction has the potential to advance the field as the scientific community has not 158 yet explore the consequences of p53 oscillations beyond the p53-MDM2 feedback loop. Given that oscillatory p53 has increased ability to express more amounts of target genes products that includes DNA damage repair and cell cycle arrest genes, this insight is especially noteworthy, as roles of such oscillations in these important biological processes might exist as well. The novel insights gained from this work are facilitated by analyzing the p53-MDM2 feedback loop in tandem with the p53-AKT genetic network. Such insights cannot be deduced from the current models that analyze the p53-MDM2 loop in isolation, which focus on reproducing experimentally observed p53-MDM2 oscillations and explaining the underlying mechanisms (Ciliberto et al., 2005; Ma et al., 2005; Wagner et al., 2005; Geva-Zatorsky et al., 2006; Zhang et al., 2006). In Part II, a computational model to approximate pre-mRNA folding during the process of transcription (co-transcriptional) is used to predict the secondary structures of dystrophin pre-mRNA at each step of transcriptional analysis. Using the predicted results, novel scoring methodologies are developed to quantify and analyze the dynamic changes in the co-transcriptional binding accessibilities of AON (antisense-oligonucleotides) target sites in the pre-mRNA. Remarkably, dynamics of co-transcriptional binding accessibilities of AON target sites could statistically correlate with efficacy and efficiency of 94% of previously reported AONs in their ability to induce selective removal of exons in the dystrophin pre-mRNA. The most efficient AONs consistently bind to target sites on the dystrophin pre-mRNA that are accessible for binding during the entire transcription process. This result is in stark contrast to laboratories working in the same field where they reported no correlation with AON efficiency with target site secondary structures that were obtained without 159 consideration of co-transcriptional effects. In addition, four key novel insights pertaining to AON efficacy and efficiency deduced in this study are used to determine novel AON target sites that could induce the removal of a particular exon in wet experiments; previous efforts by the scientific community have failed to remove this exon successfully. As AON target sites must be customized to treat Duchenne muscular dystrophy patients, the results from this study could be applied to determine target sites systematically in place of current trial-and-error means, which could reduce the cost and waiting time of therapy. In conclusion, the use of models has greatly aided in the understanding of the two intracellular processes studied. In particular, modeling is especially useful in the analysis of the p53-AKT network because of the nonlinear dynamics arising from various interactions and inherent feedback loops. Notably, besides being able to reproduce existing experimental trends, predictions and insights generated from these models are novel and if validated in experiments, they could potentially advance the knowledge of the field. Modeling is also valuable in the analysis of the dynamical cotranscriptional binding accessibilities of an AON target site, as co-transcriptional secondary structures of an elongating pre-mRNA cannot be directly observed by current experimental methods. Therefore, given the expanding knowledge base of interactions data among cellular components, the use of modeling tools to understand and explain cellular processes will be indispensable. 160 . Taken together, analysis of the switching 1 58 dynamics between the two steady states of the system leads to the understanding of how cells regulate the switch between cell survival and death simulate the dynamics of the regulation of cell survival and death in the presence of DNA damage and growth factor. In Part II, a new model to approximate pre-mRNA folding during the process of transcription. systematically in place of current trial -and- error means, which could reduce the cost and waiting time of therapy. In conclusion, the use of models has greatly aided in the understanding of the two