<p> Flux analysis techniques, including flux balance analysis (FBA) and 13C-metabolic flux analysis (MFA), can characterize carbon and energy flows through a cell&rsquo;s metabolic network. By employing both 13C-labeling experiments and nonlinear programming, 13C-MFA provides a rigorous way of examining cell flux distributions in the central metabolism. FBA, on the other hand, gives a holistic review of optimal fluxomes on the genome scale. In this dissertation, flux analysis techniques were constructed to investigate the microbial metabolisms. First, an open-source and programming-free platform of 13C-MFA (WUFlux) with a user-friendly interface in MATLAB was developed, which allowed both mass isotopomer distribution (MID) analysis and metabolic flux calculations. Several bacterial templates with diverse substrate utilizations were included in this platform to facilitate 13C-MFA model construction. The corrected MID data and flux profiles resulting from our platform have been validated by other available 13C-MFA software. Second, 13C-MFA was applied to investigate the variations of bacterial metabolism in response to genetic manipulations or changing growth conditions. Specifically, we investigated the central metabolic responses to overproduction of fatty acids in Escherichia coli and the carbon flow distributions of Synechocystis sp. PCC 6803 under both photomixotrophic and photoheterotrophic conditions. By employing the software of isotopomer network compartmental analysis, we performed isotopically non-stationary MFA on Synechococcus elongatus UTEX 2973. The 13C-based analysis was also conducted for other non-model species, such as Chloroflexus aurantiacus. The resulting flux distributions detail how cells manage the trade-off between carbon and energy metabolisms to survive under stressed conditions, support high productions of biofuel, or organize the metabolic routes for sustaining biomass growth. Third, conventional FBA is suitable for only steady-state conditions. To describe the environmental heterogeneity in bioreactors and temporal changes of cell metabolism, we integrated genome-scale FBA with growth kinetics (time-dependent information) and cell hydrodynamic movements (space-dependent information). A case study was subsequently carried out for wild-type and engineered cyanobacteria, in which a heterogeneous light distribution in photobioreactors was considered in the model. The resulting integrated genome-scale model can offer insights into both intracellular and extracellular domains and facilitate the analysis of bacterial performance in large-scale fermentation systems. Both steady-state and dynamic flux analysis models can offer insights into metabolic responses to environmental fluctuations and genetic modifications. They are also useful tools to provide rational strategies of constructing microbial cell factories for industrial applications. </p>

<p> <b>petal</b> is a network analysis method that includes and takes advantage of precise Mathematics, Statistics, and Graph Theory, but remains practical to the life scientist. <b>petal</b> is built upon the assumption that large complex systems follow a scale-free and small-world network topology. One main intention of creating this program is to eliminate unnecessary noise and imprecision introduced by the user. Consequently, no user input parameters are required, and the program is designed to allow the two structural properties, scale-free and small-world, to govern the construction of network models. </p><p> The program is implemented in the statistical language <b>R</b> and is freely available as a package for download. Its package includes several simple <b>R</b> functions that the researcher can use to construct co-expression networks and extract gene groupings from a biologically meaningful network model. More advanced <b>R</b> users may use other functions for further downstream analyses, if desired. </p><p> The <b>petal</b> algorithm is discussed and its application demonstrated on several datasets. <b>petal</b> results show that the technique is capable of detecting biologically meaningful network modules from co-expression networks. That is, scientists can use this technique to identify groups of genes with possible similar function based on their expression information. </p><p> While this approach is motivated by whole-system gene expression data, the fundamental components of the method are transparent and can be applied to large datasets of many types, sizes, and stemming from various fields. </p>

Thesis (Ph.D.)--Drexel University, 2008. / Includes abstract and vita. Includes bibliographical references (leaves 107-118).

The subject of red blood cell (RBC) survival has been discussed in the medical literature for nearly a hundred years. There has been a large amount of experimental work on RBC survival, but the supporting analysis consisted mostly of a number of more or less ad hoc models for the RBC lifespan distribution. In this context, this dissertation makes four key contributions based on the biotin-tagged RBC survival data from healthy subjects: 1. We provide a theory of RBC survival supported by appropriate analysis. Specifically, we apply non-linear mixed effects (NLME) analysis to study the population level and individual level variation in several characteristics of RBC survival, based on random sample survival data. The general approach can be used for data obtained by several different experimental methods. 2. We present a unified analysis of RBC survival data obtained using RBCs labeled at multiple densities of biotin, thus exhibiting, for the first time, the dependence of the estimated RBC survival characteristics as a function of the biotin labeling density. Our results suggest that low-density biotinylation of RBCs does not have a significant effect on RBC survival. 3. We show that, using NLME analysis results from a reference population database, good accuracy in the estimation of clinically relevant parameters from random sample survival data can be achieved with only 2-point or 3-point optimized measurement schedules. 4. We present an argument that RBC survival results obtained from radioactive chromium labeling of RBCs may not be reliable with currently used analysis methods. The analysis presented in the dissertation can potentially be used to study RBC survival in broad range of clinical applications such as drug efficacy, quality of stored blood, and the development of protocols for the management of anemia.

This dissertation focuses on the reduction of biological complexity using marine cyanobacteria Prochlorococcus as the model system. New computational methods have been developed for the understanding of genomic characteristics, for the exploration of environmental metagenomic data, and for the inference of evolutionary forces driving genome reduction in Prochlorococcus. The first part of the dissertation presents basic genomic characteristics in Prochlorococcus MED4. Known as the smallest and the most numerically abundant photosynthetic organism in the ocean, it shares many genomic characteristics with chloroplasts and bacterial endosymbionts. Orthologous genes from Prochlorococcus and a closely related marine cyanobacteria group Synechococcus, are profiled to show the gradients in genome sizes, GC% content, and the genome-wide acceleration of protein sequence evolution. The second part of the dissertation introduces new computational approaches for exploring environmental metagenomic data. The profiling of relative sequence abundance in the Sargasso Sea data has motivated the development of a phylogenetic focus group-based sequence filtering framework that takes into account of limitations in general purposed sequence similarity search, variations in evolutionary rates, as well as the context of phylogeny. A sequence trimming and segmentation mechanism has been proposed to facilitate downstream analysis. The integrated framework of sequence filtering and trimming performs better than general purpose methods, and benefits the exploration study of environmental metagenomic data. The third part of the dissertation tests a hypothesis about the relative strength of genetic drift vs. natural selection, formulated based on similarities between Prochlorococcus and endosymbionts. The hypothesis conjectures that Prochlorococcus has been experiencing a relative higher level of genetic drift, resulting in a relaxation in selection efficiency, leading to genome reduction and genome wide accelerated protein evolution. The evaluation of the hypothesis is performed by comparing the evolutionary profiles of Prochlorococcus with Synechococcus. Results from the complete genomes and the metagenomic data indicate that the average pairwise dN/dS ratios in the high-light adapted Prochlorococcus ecotypes are significantly lower than that in Synechococcus, i.e., Prochlorococcus is actually experiencing stronger selection genome-wide. The hypothesis is thus rejected, opening up space for constructing new hypotheses regarding the evolution of Prochlorococcus.

<p> Shaking palsy, or tremors are one of the four cardinal symptoms of Parkinson&rsquo;s Disease (PD). Tremors are a quantifiable symptom that should be detected to understand the severity of tremors in a patient suffering from PD. The proposed project has developed a prototype that measures tremor acceleration for twenty-four hours and saves the average acceleration per hour values in a text file of a computer. The tremor acceleration data saved from a patient&rsquo;s tremors is then sent to a healthcare provider for daily monitoring of the patient. </p><p> The tremor acceleration is measured using the accelerometer. The data from the accelerometer is processed by the Arduino board. The programming in the Arduino board is performed using C programming. The automatic emailer in the computer is programmed using Python programming language. On testing the prototype, the system demonstrated the capability to monitor tremors and transmit the data to the desired email address.</p>

<p> Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the other hand, the grid may be costly to use due to the dedicated resources used to execute the tasks and lack of elasticity. With increasing availability of cloud-based big data frameworks, such as Apache Hadoop, cloud-based services for executing medical imaging studies have shown promise.</p><p> Despite this promise, our studies have revealed that existing big data frameworks illustrate different performance limitations for medical imaging applications, which calls for new algorithms that optimize their performance and suitability for medical imaging. For instance, Apache HBases data distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). Big data medical image processing applications involving multi-stage analysis often exhibit significant variability in processing times ranging from a few seconds to several days. Due to the sequential nature of executing the analysis stages by traditional software technologies and platforms, any errors in the pipeline are only detected at the later stages despite the sources of errors predominantly being the highly compute-intensive first stage. This wastes precious computing resources and incurs prohibitively higher costs for re-executing the application. To address these challenges, this research propose a framework - Hadoop &amp; HBase for Medical Image Processing (HadoopBase-MIP) - which develops a range of performance optimization algorithms and employs a number of system behaviors modeling for data storage, data access and data processing. We also introduce how to build up prototypes to help empirical system behaviors verification. Furthermore, we introduce a discovery with the development of HadoopBase-MIP about a new type of contrast for medical imaging deep brain structure enhancement. And finally we show how to move forward the Hadoop based framework design into a commercialized big data / High performance computing cluster with cheap, scalable and geographically distributed file system.</p><p>

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references.

Thesis (Ph. D.)--Drexel University, 2004. / Includes abstract and vita. Includes bibliographical references (leaves 80-88).

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 88-100).