This dissertation examines some important physiological and pheromonal factors regulating foraging and colony growth in honey bee colonies. The first study analyzed effects of soybean trypsin inhibitor (SBTI) on the development of hypopharyngeal gland, midgut enzyme activity and survival of the honey bee. In this study newly emerged caged bees were fed pollen diets containing three different concentrations of SBTI. Bees fed 1% SBTI had significantly reduced hypopharyngeal gland protein content. This study indicated that nurse bees fed a pollen diet containing at least 1% SBTI would be poor producers of larval food. In the second study nurse bee biosynthesis of brood food was manipulated using SBTI, and the resulting effects on pollen foraging were measured. Experimental colonies were given equal amounts of SBTI treated and untreated pollen. SBTI treatments had significantly lower hypopharyngeal gland protein content than controls. There was no significant difference in the ratio of pollen to non-pollen foragers and pollen load weights collected between the treatments. These results supported the pollen foraging effort predictions generated from the direct independent effects hypothesis. In the third study we tested whether brood pheromone (BP) regulated queen egg laying via modulation of worker-queen interactions and nurse bee rearing behaviors. This experiment had BP and control treatments. Queens in the BP treatment laid greater number of eggs, were fed for a greater amount of time and were less idle. Significantly more time was spent in cell cleaning by the bees in BP treatments. The results suggest that brood pheromone regulated queen egg-laying rate by modulating worker-queen interactions and nurse bee rearing behavior. The final study of this dissertation focused on how dose-dependent BP-mediated division of labor affected the partitioning of non-foraging and foraging work forces and the amount of brood reared. Triple cohort colonies were used and there were three treatments, Low BP, High BP and Control. Low BP treatments had significantly higher ratio of pollen to non-pollen foragers and greater pollen load weights. Low BP treatment bees foraged at a significantly younger age. This study has shown that BP elicits dose-dependent modulation of foraging and brood rearing behaviors.

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specificc time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach.

Recombinant human macrophage colony stimulating factor beta (rhm-CSFβ) is a glycoprotein that stimulates the proliferation, differentiation and survival of cells belonging to the monocyte-macrophage lineage. It contains nine inter-subunit and intra-subunit disulfide bonds and represents an excellent model system for studying disulfide bond formation during protein folding because the assembly of its monomeric subunits and the maturation of its biological activity depend on the progressive formation of the correct disulfide structure during in vitro folding. Knowledge obtained from these studies can be potentially useful in understanding the roles of disulfide bond formation during protein folding in general. rhm-CSF8 was modified by partial reduction of disulfide bonds, yielding CN¹⁵⁷'¹⁵⁹-modified rhm-CSFβ. The modification did not affect the biological activity, stability, or the overall conformation of the protein. However, the C-terminal regions near the modification sites were shown to exhibit faster deuterium exchange behavior as a result of the chemical modification, indicating that the C-terminal regions became more flexible. Folding kinetics of rhm-CSFβ and CN¹⁵⁷'¹⁵⁹-modified rhm-CSFβ were shown to be essentially the same, suggesting that the modification did not affect the folding kinetics of the oxidized rhm-CSFβ. The denatured and reduced rhm-CSFβ was refolded with the aid of a chemical oxidant. The data indicated that the in vitro folding rhm-CSFβ proceeded via multiple pathways involving monomeric and dimeric intermediates. Disulfide bond shuffling catalyzed by GSH/GSSG represented an important isomerization step in folding. A dimeric intermediate, D-SS8-cam2, was isolated and identified as a kinetic trap, perhaps requiring significant structural arrangement to convert to the native protein. The heterogeneous folding mixture detected by both disulfide bond quenching and H/D pulsed labeling indicate that rhm -CSFβ folding is a diffusion like process as described by the folding funnel model. / Graduation date: 2003

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specificc time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach.

All life is organized hierarchically. Lower levels, such as cells and zooids, are nested within higher levels, such as multicellular organisms and colonial animals. The process by which a higher-level unit forms from the coalescence of lower-level units is known as “individuation”. Individuation is defined by the strength of functional interdependencies among constituent lower-level units. Interdependency results from division of labor, which is evidenced in colonial metazoans as zooid polymorphism. As lower-level units specialize for certain tasks, they become increasing dependant on the rest of the collective to perform other tasks. In this way, the evolution of division of labor drives the process of individuation. This study explores several ways in which polymorphism evolves in colonial marine invertebrates such as cnidarians, bryozoans, and urochordates. A previous study on the effect of environmental stability on polymorphism is revisted and reinterpreted. A method for quantifying colonial-level individuation by measuring the spatial arrangement of polymorphic zooids is proposed and demonstrated. Most significantly, a comparison across all colonial marine invertebrate taxa reveals that polymorphism only appears in those colonial taxa with moderately to strongly compartmentalized zooids. Weakly compartmentalized and fully compartmentalized taxa are universally monomorphic. This pattern is seen across all colonial marine invertebrate taxa and is interpreted as a “rule” governing the evolution of higher-level individuation in the major taxa of colonial marine invertebrates. The existence of one rule suggests that there may be others, including rules that transcend levels of biological hierarchy. The identification of such rules would strongly suggest that new levels in the hierarchy of life evolve by a universal pattern that is independent of the type of organism involved. / Dissertation

This dissertation examines some important physiological and pheromonal factors regulating foraging and colony growth in honey bee colonies. The first study analyzed effects of soybean trypsin inhibitor (SBTI) on the development of hypopharyngeal gland, midgut enzyme activity and survival of the honey bee. In this study newly emerged caged bees were fed pollen diets containing three different concentrations of SBTI. Bees fed 1% SBTI had significantly reduced hypopharyngeal gland protein content. This study indicated that nurse bees fed a pollen diet containing at least 1% SBTI would be poor producers of larval food. In the second study nurse bee biosynthesis of brood food was manipulated using SBTI, and the resulting effects on pollen foraging were measured. Experimental colonies were given equal amounts of SBTI treated and untreated pollen. SBTI treatments had significantly lower hypopharyngeal gland protein content than controls. There was no significant difference in the ratio of pollen to non-pollen foragers and pollen load weights collected between the treatments. These results supported the pollen foraging effort predictions generated from the direct independent effects hypothesis. In the third study we tested whether brood pheromone (BP) regulated queen egg laying via modulation of worker-queen interactions and nurse bee rearing behaviors. This experiment had BP and control treatments. Queens in the BP treatment laid greater number of eggs, were fed for a greater amount of time and were less idle. Significantly more time was spent in cell cleaning by the bees in BP treatments. The results suggest that brood pheromone regulated queen egg-laying rate by modulating worker-queen interactions and nurse bee rearing behavior. The final study of this dissertation focused on how dose-dependent BP-mediated division of labor affected the partitioning of non-foraging and foraging work forces and the amount of brood reared. Triple cohort colonies were used and there were three treatments, Low BP, High BP and Control. Low BP treatments had significantly higher ratio of pollen to non-pollen foragers and greater pollen load weights. Low BP treatment bees foraged at a significantly younger age. This study has shown that BP elicits dose-dependent modulation of foraging and brood rearing behaviors.

Previous research on DNA computing has shown that DNA algorithms are useful to solve some combinatorial problems, such as the Hamiltonian path problem and the traveling salesman problem. The basic concept implicit in previous DNA algorithms is the brute force method. That is, all possible solutions are created initially, then inappropriate solutions are eliminated, and finally the remaining solutions are correct or the best ones. However, correct solutions may be destroyed while the procedure is executed. In order to avoid such an error, we recommend combining the conventional concepts of DNA computing with a heuristic optimization method and apply the new approach to design strategies. In this thesis, we present a DNA algorithm based on ant colony optimization (ACO) for solving the traveling salesman problem (TSP). Our method manipulates DNA strands of candidate solutions initially. Even if the correct solutions are destroyed during the process of filtering out, the remaining solutions can be reconstructed and correct solutions can be reformed. After filtering out inappropriate solutions, we employ control of melting temperature to amplify the surviving DNA strings proportionally. The product is used as the input and the iteration is performed repeatedly. Accordingly, the concentration of correct solutions will be increased. Our results agree with that obtained by conventional ant colony optimization algorithms and are better than that obtained by genetic algorithms. The same idea can be applied to design methods for solving other combinatorial problems with DNA computing.

To realize efficient parallel processing, which is one of effective methods that deal with computing intensive applications, the technology of solving the problems of task matching and scheduling becomes extremely important. In this thesis, an Ant Colony Optimization (ACO) approach is employed for allocating task graphs onto a heterogeneous computing system. The approach uses a new state transition rule to reduce the time needed for finding a satisfactory solution. And a local search procedure is designed to improve the obtained solution. Furthermore, by applying the Taguchi Method in the technology of Quality Engineering, and further utilizing the Orthogonal Array (OA) to reduce the number of experiments and find the optimal combination of parameters, which allows the Ant Colony Algorithm to find solutions more efficient. The proposed algorithm is compared with the genetic-algorithm-based approach and the dynamic priority scheduling (DPS) heuristic. Experimental results show that the ACO approach outperforms two computing approaches in solving the task matching and scheduling problem.

Functional expression of a protein in life form is decided by its tertiary structure. In the past few decades, a significant number of studies have been made on this subject. However, the folding rules of a protein still stay unsolved. The challenge is to predict the three-dimensional tertiary structure of a protein from its primary amino acid sequence. We propose a hybrid method combining homology model and the folding approach to predict protein three-dimensional structure from amino acid sequence. The previous researches on folding problem mostly take the HP (Hydrophobic-Polar) model, which is not able to simulate the native structure of proteins. We use a more exquisite model, the sliced lattice model, to approximate the native forms. Another essential factor influencing protein structures is disulfide bonds, which are ignored in the HP model. We use the ant colony optimization algorithm to approximate the folding problem with the constrained disulfide bond on the sliced lattice HP model. We show that the prediction results are better than previous methods by the measurement of RMSD(Root Mean Square Deviation).

The Human Genome Project completed in 2003 and the draft of human genome sequences were also yielded. It has been known that any two human gnomes are almost identical, and only very little difference makes human diversities. Single nucleotide polymorphism (SNP) means that a single-base nucleotide changes in DNA. A SNP sequence from one of a pair of chromosomes is called a haplotype. In this thesis, we study how to reconstruct a pair of chromosomes from a given set of fragments obtained by DNA sequencing in an individual. We define a new problem, the chromosome pair assembly problem, for the chromosome reconstruction. The goal of the problem is to find a pair of sequences such that the pair of output sequences have the minimum mismatch with the input fragments and their lengths are minimum. We first transform the problem instance into a directed multigraph. And then we propose an efficient algorithm to solve the problem. We apply the ACO algorithm to optimize the ordering of input fragments and use dynamic programming to determine SNP sites. After the chromosome pair is reconstructed, the two haplotypes can also be determined. We perform our algorithm on some artificial test data. The experiments show that our results are near the optimal solutions of the test data.