Need To Be Adored

Faced with a diagnosis of multiple sclerosis, I began with the objective of discovering methods for creating art that were still accessible to me. Along the way, I encountered others who had travelled this road before me. Their experiences led me to examine, not only my art, but also my political orientations, my love obligations and my transitioning self. In my varied art pieces, I conjure something from diverse sources and different worldviews, including contemporary feminist performance art and disability cultural theory. My thesis is a project. I make things: puppets, videos and performances, which included the exhibition, Need to be Adored (2014), staged in the digital media lab of the Isabel Bader Centre for the Performing Arts in Kingston, Ontario, Canada. The exhibition introduced thirteen of my puppets and a thirty-two-minute looped video. Following the exhibition, I put the puppets away and spent two years reading. Finally, taking my inspiration from Carolyn Ellis’s The Autoethnographic I (Ellis 2004), I turned my processes into words. I wrote out my experiences. I created an alternative text of my identity from an able-bodied cis-identified woman into a disabled trans-feminist artist academic. The writing required an uncomfortably intimate examination of my life. Nothing less than complete honesty would allow me to understand my new location. The resulting text is a lyrical and sometimes whimsical flow of consciousness that invites the reader to imagine what it might be like to engage in such a candid review of everything one holds close to one’s heart. Contained within are all my identities. In this text I let some out. This is a story of unsettling. I am working on my art practices, creating a cast of characters from cloth. Puppets. El becomes the exulted main character of a fictional accounting. She uncovers her queer roots and begins to see that she is at the centre of a very strange geography. Her desire to make film is revealed as she re-remembers her childhood through a disability lens.

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.