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.

Enhancement of a novel gene therapy approach for Sandhoff disease through complimentary drug therapy

GM2 gangliosidoses is a family of severe, neurodegenerative disorders resulting from a deficiency in the β-hexosaminidase A (Hex A) enzyme. This disorder is typically caused by a mutation to either the HEXA gene, causing Tay Sachs disease, or a mutation to the HEXB gene, causing Sandhoff disease. The HEXA and HEXB genes are required to produce the α and β subunits of the Hex A enzyme respectively. Using a Sandhoff disease (SD) mouse model (Hexb-/-) we tested the potential of a low dose of systemically delivered single stranded adeno-associated virus 9 (ssAAV9) expressing human HEXB and human HEXA cDNA under the control of a single promoter through the use of a bicistronic vector design with a P2A linker to correct the neurological phenotype. Neonatal mice were injected with either this ssAAV9-HexB-P2A-HexA vector (HexB-HexA) or a vehicle solution via the superficial temporal vein. HexB-HexA treatment alone conferred an increase in survival of 56% compared to vehicle-injected controls and biochemical analysis of the brain tissue and serum revealed an increase in HexA activity and a decrease in brain GM2 ganglioside buildup. Additionally, treatments with the non-steroidal anti-inflammatory drug indomethacin (Indo), the histone deactylase inhibitor ITF2357 (ITF) and the pharmacological chaperone pyrimethamine (Pyr) were tested. The anti-inflammatory treatments of Indo and ITF conferred an increase in survival of 12% and 8% respectively while causing no alteration in the HexA activity or GM2 ganglioside buildup. Pyr had no observable effect on disease progression. Lastly HexB-HexA treatment was tested in conjunction with Indo, ITF and Pyr individually. Additive increases in survival and behavioural testing results were observed with Indo and ITF treatments while no additional benefit to HexA activity or GM2 ganglioside levels in the brain tissue was observed. This indicates the two treatments slowed the progression of the disease through a different mechanism than the reduction of the GM2 ganglioside substrate. Pyr treatment was shown to have no effect when combined with HexB-HexA treatment. This study demonstrates the potential amelioration of SD with a novel AAV9 gene therapy approach as well as helped to identify the additive potential of anti-inflammatory treatments in gene therapy of GM2 gangliosidoses.