An Investigation into the Failure to Implement a Universal School Lunch Program during WWII and Postwar Reconstruction in Canada: The Case of Ontario

The purpose of this research is to investigate the various social, political and economic factors that contributed to Canada’s failure to implement a universal school lunch program during the 1940s. Although Canada developed several other social welfare programs in the post-war period, it remains one of the only industrialized nations that does not provide hot meals to children in elementary or secondary schools. Data from the province of Ontario, a major site of postwar reconstruction and policy-making, has been taken up to inform the broader national discourse on school lunches from the 1940s. National, Ontario provincial and City of Toronto archival records were collected and analyzed according to common themes, in order to identify key barriers that constrained government support of a hot meal program. Archival records were identified using key words, and were limited to materials created between 1930-1952. Analysis suggests that sufficient need for a hot meal program had not been established during the 1940s. Despite misleading nutrition messages, rates of malnutrition and nutrient-related disease were at an all-time low, and many Ontario school boards did not appear to have the necessary infrastructure required to supply all pupils with hot meals. The Canadian government had already employed significant resources to improve existing social security programs by coupling them with health education. This strategy reflected a shift in understanding malnutrition as a knowledge-based problem, as opposed to income-based. This understanding was further reinforced through the moralized dissemination of nutrition information, which placed blame on women for improperly raising their children. Ultimately, the strong uptake of nutrition as a public health issue in Ontario may have limited prospective responses to solutions already utilized in the public health domain, and directed favour away from a universal school lunch program for Canada.

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.