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.

Gross and net nitrogen transformation rates and availability in late summer in tall shrub and birch hummock ecosystems of the Canadian low Arctic

Climate change is occurring most rapidly in the Arctic where warming has been twice as fast as the rest of the globe over the last few decades. Arctic soils contain a vast store of carbon and warmer arctic soils may mediate current atmospheric CO2 concentrations and global warming trends. Warmer soils could increase nutrient availability to plants, leading to increased primary production and sequestration of CO2. Presumably because of these effects of warming on shrub ecosystems, shrubs have been expanding across the arctic over the last 50 years, Arctic shrub expansion may track or cause changes in nutrient cycling and availability that favour growth of larger, denser shrubs. This study aimed at measuring gross and net nitrogen cycling rates, major soil nitrogen and carbon pool sizes, and elucidating controls on nutrient cycling and availability between a mesic birch (Betula nana) hummock tundra ecosystem and an ecosystem of dense, tall, birch (B. nana) shrubs. Nitrogen cycling and availability was enhanced at the tall shrub ecosystem compared to the birch hummock ecosystem. Net nitrogen immobilization by microbes was approximately threefold greater at the tall shrub ecosystem. This was in part because of larger microbial biomass nitrogen and carbon (interpreted as a larger microbial community) at the tall shrub ecosystem. Nitrogen inputs via litter were significantly larger at the tall shrub ecosystem and were hypothesized to be the major contributor to the higher dissolved organic and inorganic nitrogen pools in the soil at the tall shrub ecosystem. The results from this study suggest a positive feedback mechanism between litter nitrogen inputs and the enhancement of nitrogen cycling and availability as a driver of shrub expansion across the Arctic.