Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.

EXAMINATION OF THE EFFECTS OF VALPROIC ACID EXPOSURE OR P300 PROTEIN INHIBITION ON THE BALANCE OF APOPTOSIS AND PROLIFERATION IN P19 EMBRYONAL CARCINOMA CELLS

Valproic acid (VPA), a commonly-used anticonvulsant drug, is associated with increased risk of fetal malformations, including neural tube defects (NTDs). Previous in vivo studies determined that VPA-exposed embryos with a NTD had altered expression of several proteins regulated by p300, a histone acetyltransferase (HAT) protein. p300 is capable of acetylating histones and non-histone proteins through its HAT activity, allowing it to transcriptionally regulate genes as well as modulate the stability and activity of specific proteins. NFκB, Stat3 and Egr1, all of which function as transcription factors, are regulated by p300 through its HAT activity. Together, these proteins all play an important role in maintaining the balance of apoptosis, proliferation and differentiation, the regulation of which is extremely important for proper embryonic development. The studies in this thesis utilized P19 embryonal carcinoma (EC) cells in order to determine the effects of VPA exposure on the expression of p300 and the aforementioned transcription factors, as well as apoptosis and proliferation, in vitro. P19 EC cells were exposed to C646, a selective p300 inhibitor, in order to assess whether the effects observed as a result of VPA exposure were due to p300 protein degradation. It was found that VPA exposure for 24 hours in P19 EC cells in vitro resulted in a significant decrease in p300 protein expression. VPA exposure also significantly decreased NFκB protein expression, while resulting in increased Stat3 protein expression. However, Stat3 acetylation and phosphorylation, which both contribute to Stat3 activation, were significantly decreased as a result of VPA exposure. p300 inhibition resulted in a significant decrease in NFκB, similar to what was observed as a result of VPA exposure, which suggests that VPA-mediated degradation of p300 may play a role in reduced NFκB protein expression following VPA exposure. Conversely, Stat3 protein expression, acetylation and phosphorylation were not significantly changed as a result of p300 inhibition, suggesting that p300 degradation does not play a role in VPA’s effects on Stat3 protein expression and activation. VPA exposure also resulted in a significant increase in apoptosis, while p300 inhibition did not significantly increase apoptosis. These data suggest that p300 degradation plays a role in VPA-mediated teratogenicity, and that VPA may target other cellular mechanisms in order to exert its teratogenic effects.