Retinoic acid (RA) exerts important effects in the processes of vertebrate development, cellular growth and differentiation, and homeostasis. However, the mechanisms of action of RA in the control of cellular and developmental processes are incompletely understood, as the retinoid target genes have not been fully characterized. The goal of these studies described herein was to contribute towards a greater understanding of the cellular effects of retinoids through the identification and characterization of an RA-induced gene from mouse P19 embryonal carcinoma cells. The predicted amino acid sequence of GRASP is characterized by several putative protein-protein interaction motifs, suggesting that GRASP may function in cell signaling pathways. Towards the goal of identifying which signaling pathways GRASP may participate in, a yeast two-hybrid screen was performed using GRASP as a bait to identify protein interaction partners. The general receptor for phosphinositides 1 (GRP1), a guanine nucleotide exchange factor for the ADP-ribosylation factor 6 (ARF6) GTPase, was identified as a GRASP interaction partner. GRASP was shown to colocalize with endogenous ARFs in cells and enhance GRP1 association with the plasma membrane, suggesting that GRASP may function as a scaffold protein in the recruitment of GRP1 and ARF6 to plasma membrane loci. Overexpression of GRASP was observed to induce accumulation of GRASP in the endosomal compartment where GTP-binding deficient mutants of ARF6 reside, suggesting that GRASP induced a block in an ARF6 plasma membrane recycling pathway. Coexpression of GRP1, but not a catalytically inactive mutant, dramatically reduced the accumulation of GRASP in this compartment. Furthermore, GRP1 mutants that lack the region of interaction with GRASP failed to prevent accumulation of GRASP in the endosomal compartment, suggesting that GRASP recruits GRP1 to the endosomal compartment where GRP1 stimulates nucleotide exchange on ARF6 and recycling. Results described herein demonstrate that GRASP functions in the ARF6 regulated plasma membrane recycling pathway, and that upon overexpression, induces a block in recycling. Our results suggest a role for GRASP as an adapter or scaffold protein that may link cell surface receptors to the ARF6 recycling pathway, resulting in modulation of signal transduction events at the cell surface. / Graduation date: 2002

In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal solutions. Second, we analyze the relationship between GA control parameters (population size, crossover rate, and mutation rate) and performance. We identify a combination of population, mutation, and crossover which searched the fitness landscape efficiently. The results suggest that smaller populations are able to provide better performance at relatively low mutation rates. More stable outcomes were generated using low mutation rates. Without crossover the quality of solutions were generally impaired, and the search process was lengthened. Aside from crossover rate zero, no other crossover rates significantly differed. The behaviors observed for best, online, offline, and average performances were sensitive to the combined influences control parameters. Interaction among control parameters was strongly indicated. Finally, several adaptive penalty techniques are presented for handling constraints in GAs, and their effectiveness is demonstrated. The constant penalty function suffered from sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints. The adaptive penalty functions utilizes violation distance based metrics and search time based scaling using generation or trials number, and fitness values to penalize infeasible solutions, as the distance from the feasible region or number of generations increases so does the penalty. They were quite successful in providing solutions with minimal effort. They adapt the penalty as the search continues, encouraging feasible solutions to emerge over the time. Adaptive approaches presented here are flexible, efficient, and robust to parameter settings. / Graduation date: 1997

Graduation date: 1994

In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.

Thesis (Ph. D.)--Johns Hopkins University, 2005. / Includes bibliographical references (p. 185-204).

Large corporations have a significant amount of working capital tied into the acquisition and storage of spare parts. In the industry, spare parts inventory policies and strategies are often developed in isolation from reliability centered maintenance practices – this results in significant wasteful direct and indirect cost attached to spare parts management for the equipment operator. This report will focus on developing a methodology for minimizing lifecycle indirect and direct cost that comes from storing long lead time spares. A combined Monte-Carlo and Genetic Algorithm based optimization approach to finding the optimal spare parts storage strategy is proposed. In this study, the indirect and direct cost of having a spare part in the storage facility will be balanced against the cost of lost opportunity that results from decreased availability - a consequence of not having the required spare part available when an equipment failure event occurs. The results of this study present the benefits of optimizing long lead time spares through a joint Monte-Carlo & Genetic Algorithm based approach. / Engineering Management

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 195-210). Also available in electronic version.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Also available online.

Researchers have applied genetic algorithms (GAs) and other heuristic optimization methods to perform truss optimization in recent years. Although a substantial amount of research has been performed on the optimization of truss member sizes, nodal coordinates, and member connections, research that seeks to simultaneously optimize the topology, geometry, and member sizes of trusses is still uncommon. In addition, most of the previous research is focused on the problem domains that are limited to a structured domain, which is defined by a fixed number of nodes, members, load locations, and load magnitudes. The objective of this research is to develop a computational method that can design efficient roof truss systems. This method provides an engineer with a set of near-optimal trusses for a specific unstructured problem domain. The unstructured domain only prescribes the magnitude of loading and the support locations. No other structural information concerning the number or locations of nodes and the connectivity of members is defined. An implicit redundant representation (IRR) GA (Raich 1999) is used in this research to evolve a diverse set of near-optimal truss designs within the specified domain that have varying topology, geometry, and sizes. IRR GA allows a Pareto-optimal set to be identified within a single trial. These truss designs reflect the tradeoffs that occur between the multiple objectives optimized. Finally, the obtained Pareto-optimal curve will be used to provide design engineers with a range of highly fit conceptual designs from which they can select their final design. The quality of the designs obtained by the proposed multi-objective IRR GA method will be evaluated by comparing the trusses evolved with trusses that were optimized using local perturbation methods and by trusses designed by engineers using a trial and error approach. The results presented show that the method developed is very effective in simultaneously optimizing the topology, geometry, and size of trusses for multiple objectives.

Integration of two or more genomic maps provides a higher density of markers and greater genome coverage than can be obtained with the resources available for a single mapping study. Map integration is important in any species for which an annotated complete genome sequence is not available. For organisms currently being sequenced, a pre-sequence integrated map is essential to provide the "backbone" for assembly of the sequence. Map integration also facilitates the identification and resolution of discrepancies among different maps; mapping of QTLs, ESTs, and BACs; and positioning of candidate genes. However, the inconsistencies in markers and populations used in individual mapping studies limit our ability to fully integrate the available data. By concentrating on marker orders rather than marker distances, one can join together published map data to include a majority of markers with the best estimate of their order in the genome. In this study, a comprehensive reference map was constructed from 28 published cotton AD genome maps. The output reference map contains 7,424 markers and represents over 93% of the combined mapping information from the 28 individual AD genome genetic maps. This study applied the use of bioinformatics and computational biology in cotton genome mapping integration. The output will be stored and displayed through CottonDB (http://www.cottondb.org), a public cotton genome database.