Chapter Future Work The level of details considered in a model usually depends on the biological question at hand. While a detailed model is comparatively more realistic, inclusion of substantial biochemical steps with unknown mechanisms and kinetics which requires arbitrary assumptions might render the model to be less realistic than expected. Moreover, solving for the model dynamics becomes computationally intensive as the number of model components increases. A more pragmatic approach however is to update a model as and when new biological mechanisms relevant to the study are elucidated. Nonetheless, as a model cannot possibly include all known and unknown details, it is at best an approximation of the actual biological process being studied. Without exceptions, the models developed in this thesis have limitations. These limitations and suggestions to overcome them in future work are discussed below. 9.1 Models of the p53-AKT network Admittedly, the p53-AKT network analyzed is merely a part of a more elaborate control system deciding between cell survival and death. Furthermore, the results would apply only to cells that rely chiefly on the p53-AKT network for regulation of cell survival and death. Other pathways regulating cell survival and death exist in other cell types; for example, ERK could promote AKT-independent cell survival 161 pathways (Nair et al., 2004; Ganguli and Wasylyk, 2003). Nevertheless, they can be incorporated into the p53-AKT models to extend the results to other cell types. On the other hand, while the impingement of general growth factor and DNA damage signaling on the p53-AKT network are taken into account, the models not consider specific and other known or unknown details of the regulation of p53 and AKT. In particular, different forms of p53 are ignored to reduce the number of variables and unknown mechanistic and kinetic parameters in the models. However, specific forms of p53 that are relevant to its apoptotic activity should be considered in future studies when the biochemical activities are characterized. Predictions of various apoptotic thresholds have recently been reported from models that considered the caspase activation cascade only (Fussenegger et al., 2000; Bentele et al., 2004; Eissing et al., 2004, 2005; Hua et al., 2005; Stucki and Simon, 2005; Bagci et al., 2006; Legewie et al., 2006; Aldridge et al., 2006). In these models, the activation of caspases is predicted to be an all-or-none system that is similar to the bistability phenomenon predicted by the p53-AKT models. A challenging future study will also include these published models and all known downstream apoptotic pathways from p53 and AKT (Section 2.5 of Chapter 2) to understand how the predicted apoptotic threshold at the upstream p53-AKT network impinges on the predicted apoptotic thresholds at the downstream caspase network. Finally, it would be interesting to see if a stochastic model of the p53-AKT network could also predict bistability and limit cycles, and if so, whether stochastic effects could cause random switching between the two steady states. Stochastic effects are usually significant under low numbers of molecules per cell such as the 162 number of mRNA molecules. A p53-AKT stochastic model can be converted from the deterministic p53-AKT models developed in this thesis (Turner et al., 2004). 9.2 Model for approximating co-transcriptional binding accessibilities In the event that dystrophin-specific splicing factors significantly affect the mechanism of exon recognition, the results of the analyses might differ and they might have to be considered in the model. Nevertheless, elucidation of such factors, if any, is experimentally difficult. Alternatively, model validation is easier by using insights obtained from the analyses to predict AON target sites and test for their efficiency in exon skipping. Testing of about 30 such sites is ongoing in our collaborator’s lab. As the pre-mRNA co-transcriptional secondary structures are derived from a prediction algorithm, the accuracy of the model is limited by the accuracy of such algorithms. However, the use of vast numbers of predicted secondary structures that averaged 44,582 of them per exon (Appendix A-17) in the analyses is expected to spread out the prediction error of mfold. Nevertheless, an interesting future work is to use other prediction algorithms to predict the pre-mRNA co-transcriptional secondary structures and compare their results of analyses. Lastly, another future work that has therapeutic applications is to apply the model to other genes whose mutations caused genetic diseases. Examples of such 163 genes are beta-globin gene in thalassemia (Suwanmanee et al., 2002; Gorman et al., 2000; Sazani and Kole, 2003; Dominski and Kole, 1993) and OA1 gene in ocular albinism (Vetrini et al., 2006). Analogous to the case of Duchene muscular dystrophy, point mutations causing nonsense codons or shift in reading frame are the main causes of these genetic diseases. Therefore, selective exon skipping mediated by AONs to remove the mutations could be a possible therapeutic strategy. The insights that co-transcriptional binding accessibility is a key factor influencing AON efficiency in inducing exon skipping can be applied to these genes to predict AON target sites for efficient exon skipping. 164 . diseases. Examples of such 164 genes are beta-globin gene in thalassemia (Suwanmanee et al., 2002; Gorman et al., 2000; Sazani and Kole, 2003; Dominski and Kole, 199 3) and OA1 gene in ocular. particular, different forms of p53 are ignored to reduce the number of variables and unknown mechanistic and kinetic parameters in the models. However, specific forms of p53 that are relevant. algorithm, the accuracy of the model is limited by the accuracy of such algorithms. However, the use of vast numbers of predicted secondary structures that averaged 44,582 of them per exon (Appendix