Manipulation of adenovirus early region 1 rescue plasmids by homologous recombination in Saccharomyces cerevisiae

The manipulation of large (>10 kb) plasmid systems amplifies problems common to traditional cloning strategies. Unique or rare restriction enzyme recognition sequences are uncommon and very rarely located in opportunistic locations. Making site-specific deletions and insertions in larger plasmids consequently leads to multiple step cloning strategies that are often limited by time-consuming, low efficiency linker insertions or blunt-end cloning strategies. Manipulation ofthe adenovirus genome and the genomes ofother viruses as bacterial plasmids are systems that typify such situations. Recombinational cloning techniques based on homologous recombination in Saccharomyces cerevisiae that circumvent many ofthese common problems have been developed. However, these techniques are rarely realistic options for such large plasmid systems due to the above mentioned difficulties associated with the addition ofrequired yeast DNA replication, partitioning and selectable marker sequences. To determine ifrecombinational cloning techniques could be modified to simplify the manipulation of such a large plasmid system, a recombinational cloning system for the creation of human adenovirus EI-deletion rescue plasmids was developed. Here we report for the first time that the 1,456 bp TRP1/ARS fragment ofYRp7 is alone sufficient to foster successful recombinational cloning without additional partitioning sequences, using only slight modifications of existing protocols. In addition, we describe conditions for efficient recombinational cloning involving simultaneous deletion of large segments ofDNA (>4.2 kb) and insertion of donor fragment DNA using only a single non-unique restriction site. The discovery that recombinational cloning can foster large deletions has been used to develop a novel recombiliational cloillng technique, selectable inarker 'kilockouf" recombinational cloning, that uses deletion of a yeast selectable marker coupled with simultaneous negative and positive selection to reduce background transformants to undetectable levels. The modification of existing protocols as described in this report facilitates the use of recombinational cloning strategies that are otherwise difficult or impractical for use with large plasmid systems. Improvement of general recombinational cloning strategies and strategies specific to the manipulation ofthe adenovirus genome are considered in light of data presented herein.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.