Thesis (Ph.D.) - University of Queensland, 2004. / Includes bibliography.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2005. / Includes bibliographical references (p. 91-94).

Thesis (M.Eng.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 211-214.

Systems metabolic engineering has enabled systematic studying of microbes for modifying their genetic contents, analyzing their metabolism, and designing new capabilities. One of the most commonly used approaches in systems metabolic engineering involves genome-scale metabolic flux modeling. These models allow generation of predictions of the global metabolic flux distribution in the metabolic network of organisms, in silico. With the current advances in genome sequencing technologies and the global demand for bio-based commodity chemicals and fuels, genome-scale models can help metabolic engineers propose design strategies while considering holistic behavior of the organism. In this research, novel tools and methodologies were developed to improve the future prospective of systems metabolic engineering with genome-scale modeling. To do this, an online web application (Synthetic Metabolic Pathway Builder and Genome-Scale Model Database, SyM- GEM) was first developed enabling the construction of synthetic metabolic pathway(s) and addition of those to synchronized genome-scale models. This addresses the need for an easy and universal way of creating models of engineered microbes with improved properties without the time-consuming inconvenience of synchronizing different formats and representations of genome- scale models prepared by different laboratories. The web application is freely available at http:www.mesb.bse.vt.edu/SyM-GEM. Then, a computational framework (Total Membrane Influx-Flux Balance Analysis, ToMI-FBA) was developed to allow for evaluating synthetic pathway use by different models. This enabled, for the first time, a computational guide for optimal host selection (for a specific metabolic engineering problem) and culture media formulation design to achieve the solution. Results showed that (i) L-valine improves isobutanol production by Bacillus subtilis, (ii) cellobiose increases ethanol selectivity by Clostridium acetobutylicum ATCC 824, and (iii) B. subtilis is an optimal host for artimisinate production. To further expand the capability of genome-scale models, an algorithm was developed (Genetic Algorithm-Flux Balance Analysis minimizing Total Unconstrained eXchange Flux, GA-FBA minimizing TUX) to help improve the fitness between metabolic fluxes predicted by genome-scale modeling and those obtained by 13C-tracing methods. Application of this method to the cyanobacterium Synechocystis PCC 6803 improved model accuracy by more than 50% for both heterotrophic and autotrophic growth. To generate even more realistic predictions of metabolic flux from genome-scale modeling, Raman spectroscopy was employed to help design biomass equations of microbial cells in different environmental conditions. To do this, the cellulose- consuming anaerobe Clostridium cellulolyticum ATCC 35319 was grown on cellobiose, and samples were obtained at different points of differentiation due to sporulation. Biomass composition was determined through Raman spectroscopy and traditional chemical analyses. A new genome-scale model of this organism (iCCE557) served as the basis for genome-scale model calculations. Model fitness improved upto 95% with these methods. Finally, to implement metabolic engineering strategies, regulatory RNA molecules (antisense RNAs) were designed to help target desired mRNA molecules in the metabolic network. Thermodynamic binding calculations were found to correlate with the efficiency of asRNA-mRNA binding and inhibition of mRNA translation. / Ph. D.

The research reported in this thesis is focused on the creation of a CAE system to support Reverse Engineering. It is centred around the computational representation of products (Product Model) and manufacturing capabilities (Manufacturing Model). These models are essential for modem and future software systems aimed to assist the design process, enabling data sharing among the participants who use various computational tools. Reverse Engineering is employed as a particular context and motivation for exploring the application of the models. The research builds on the achievements of the recently finished Model Oriented Simultaneous Engineering System (MOSES) project, undertaken jointly by Leeds University and the Department of Manufacturing Engineering of Loughborough University. MOSES' work on information modelling was analysed and combined together with the original proposals of the author to elaborate a suitable support to Reverse Engineering, applicable to redesign in general. A process for Reverse Engineering is proposed and documented and a data model driven CAE system to support it is specified. The CAE system includes a Product Model, a Manufacturing Model and two software application environments. The Product Model of the system is based on the information requirements of the Reverse Engineering process and is suitable for representing multi-component products, from different perspectives through its life cycle. The applications assist the characteristic activities of Reverse Engineering. In particular, the system is used for exploring the application of Product and Manufacturing Models in supporting Design for Manufacture. The theoretical research is tested by the use of a case study which explores the Reverse Engineering of a component. This work is supported by a prototype software instance of the CAE system. The case study component is an axle which forms part of a product designed and manufactured by a collaborating company.

<p>This thesis presents the development of a prototype of the Baseline Oriented Modeling</p><p>(BOM) approach, called protoBOM.</p><p>BOM is a framework that semi-automatically generates Decision Support Systems in a</p><p>specific domain, based on Software Product Lines.</p><p>protoBOM semi-automatically generates applications as PRISMA architectural models by using Model-Driven Architecture and Software Product Line techniques. These models are automatically compiled and the object code (C#, in .NET) is generated obtaining an executable application.</p><p>In protoBOM, the user constructs Decision Support Systems in a simpler way by using the</p><p>ontologies of the diagnosis and the application domains by means of Domain Specific Languages. The interfaces will be closer to the problem domain, which will facilitate user interaction in a manner simple and intuitive.</p>

Thesis (M.S.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

This thesis presents the development of a prototype of the Baseline Oriented Modeling (BOM) approach, called protoBOM. BOM is a framework that semi-automatically generates Decision Support Systems in a specific domain, based on Software Product Lines. protoBOM semi-automatically generates applications as PRISMA architectural models by using Model-Driven Architecture and Software Product Line techniques. These models are automatically compiled and the object code (C#, in .NET) is generated obtaining an executable application. In protoBOM, the user constructs Decision Support Systems in a simpler way by using the ontologies of the diagnosis and the application domains by means of Domain Specific Languages. The interfaces will be closer to the problem domain, which will facilitate user interaction in a manner simple and intuitive.

Whether we want to drive a car, play a ball game, or even enjoy watching a flying bird, we need to track moving objects. This is possible via smooth pursuit eye movements (SPEMs), which maintain the image of the moving object on the fovea (i.e., a very small portion of the retina with high visual resolution). At first glance, performing an accurate SPEM by the brain may seem trivial. However, imperfect visual coding, processing and transmission delays, wide variety of object sizes, and background textures make the task challenging. Furthermore, the existence of distractors in the environment makes it even more complicated and it is no wonder why understanding SPEM has been a classic question of human motor control. To understand physiological systems of which SPEM is an example, creation of models has played an influential role. Models make quantitative predictions that can be tested in experiments. Therefore, modelling SPEM is not only valuable to learn neurobiological mechanisms of smooth pursuit or more generally gaze control but also beneficial to give insight into other sensory-motor functions. In this thesis, I present a neurocomputational SPEM model based on Neural Engineering Framework (NEF) to drive an eye-like robot. The model interacts with the real world in real time. It uses naturalistic images as input and by the use of spiking model neurons controls the robot. This work can be the first step towards more thorough validation of abstract SPEM control models. Besides, it is a small step toward neural models that drive robots to accomplish more intricate sensory-motor tasks such as reaching and grasping.

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Dr. Mavris, Dimitri; Committee Member: Dr. Hollingsworth, Peter; Committee Member: Dr. McMichael, Jim; Committee Member: Dr. Saleh, Joseph; Committee Member: Dr. Schrage, Daniel.