Characterisation of the metacaspase gene family in Arabidopsis thaliana

Caspases are known to be involved in animal programmed cell death (PCD). The objective of this thesis was to use gene expression analysis and reverse genetics to determine if Arabidopsis metacaspase (AtMC) genes play a role in plant PCD. The majority of AtMC genes were found to be expressed nearly constitutively in various tissues, developmental stages, and under various inductive treatments. Transgenic Arabidopsis plants generated with AtMCpromoter::AtMCgene::GUS fusions showed expression of the reporter gene in leaves, vasculature, trichomes, siliques, anthers, and during embryo development. Preliminary phenotypic characterization of single and double Arabidopsis AtMC loss-of-function mutants suggested that the expression of the AtMC genes are highly functionally redundant. Nevertheless, our results suggest that AtMC1, 2, 4, 6 and 9 may be directly involved in rosette and/or stem development. Although this study does not provide a definitive role of MCs in plant PCD, it lays the foundation for their further in-depth analysis.

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.