Projects
From BiRG
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Metabolomics
| Metabolomics is the exhaustive characterization of metabolite concentrations in biofluids and tissues. The use of NMR and chromatography-linked mass spectrometry to assay metabolic profiles of tissue homogenates and biofluids has been increasingly recognized as a powerful tool for biological discovery. In recent years metabolomics techniques have been applied to a wide variety of diagnostic, preclinical, systems biology, and ecological studies. Working with Dr. Nick Reo's NMR spectroscopy lab at Wright State University, we are developing standards-based tools and web services for the pre-processing, normalization/standardization, exploratory and comparative analysis, and visualization of NMR spectra from biofluids.
NMR-based metabolomics has been used to associate an organism’s health status to its metabolite profile measured in biofluids (e.g, urine, blood, fecal and tissue extracts) or tissue biopsies. Coupled with multivariate data analyses, the 1H NMR-based metabolomics approach is a fast, accurate and reproducible analytical technique for visualization of biochemical changes in biofluids or tissues. This methodology involves correlating observed changes in metabolite levels to the biological effects related to physiological stimuli or genetic modification, toxicological, pathophysiological or environmental conditions. Studies have highlighted its potential for the successful identification and characterization of toxicity, metabolic pathways perturbed in various cancers, disease-related stages in chronic lymphocytic leukemia, and other pathophysiological conditions. Presentations |  |
Cloud Computing
| The recent flood of Web-enabled and Web-based tools for eScience has provided scientists with a wide array of new methods to collect, report, analyze and share their data. These Web-based technologies have demonstrated a great potential to enable broader collaboration and to facilitate the sharing and re-use of experimental data. The wide availability of quality data sets and tested process flows is indeed a welcome addition for an eScientist, yet there are a number of challenges to overcome. In scientific domains, such as bioinformatics, lack of standardization for scientific methods, algorithms, and data sharing can be seen as the greatest challenge for broader adoption.
Many eScience applications, including workflows, have high performance computation requirements. Cloud computing is increasingly seen as a natural choice for such high demand computing tasks because minimal upfront investments in computational resources are required. Using computing clouds also avoids the complications of catering for periodic peak usage, frequent software and hardware updates and the need of trained IT staff. However, these advantages come with its own set of challenges. The need to program for a specific cloud platform, also called vendor lock in, is one of the major hindrances to the wide adoption of clouds, especially in the scientific domains. We present SCALE, an eScience experimental data analysis platform to support the following features to meet above challenges:
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Toxicology
| As metabolomic technology expands, validated techniques for analyzing highly dimensional categorical data are becoming increasingly important. This manuscript presents a novel latent vector-based methodology for analyzing complex data sets with multiple groups that include both high and low doses using orthogonal projections to latent structures (OPLS) coupled with hierarchical clustering. This general methodology allows complex experimental designs (e.g., multiple dose and time combinations) to be encoded and directly compared. Further, it allows for the inclusion of low dose samples that do not exhibit a strong enough individual response to be modeled independently. A dose- and time-responsive metabolomic study was completed to evaluate and demonstrate this methodology. Single doses (0.1 to 100 mg/kg body weight) of α-naphthylisothiocyanate (ANIT), a common model of hepatic cholestasis, were administered orally in corn oil to male Fischer 344 rats. Urine samples were collected predose and daily through day-4 postdose. Blood samples were collected predose and on days 1 and 4 postdose. Urine samples were analyzed by 1H-NMR spectroscopy, and the spectra were adaptively binned to reduce dimensionality. The proposed methodology for NMR-based urinary metabolomics was sensitive enough to detect ANIT-induced effects with respect to both dose and time at doses below the threshold of clinical toxicity. A pattern of ANIT-dependent effects established at the highest dose was seen in the 50 mg/kg and 20 mg/kg dose groups, an effect not directly identifiable with individual PCA. Coupling the pattern found by the OPLS algorithm and hierarchical clustering revealed a relationship between the 100, 50 and 20 mg/kg dose groups, suggesting a characteristic effect of exposure. These studies demonstrate the use of a metabolomics approach with flexible binning of 1H spectra and appropriate application of multivariate analyses can reveal biologically relevant information about the temporal metabolic perturbations caused by exposure and toxicity. |  |
Immunology
| Mechanistically based computational models of the host immune response to biothreat agents are important tools in predicting the human response to infection. These in silico models can also help elucidate virulence mechanisms for high threat pathogens whose mechanisms of pathogenesis have yet to be well characterized. We are currently developing a global model of the host immune response to pathogens which is being linked with lung deposition models we have previously developed to simulate the outcome of in vivo aerosol exposure studies and extrapolate to human response predictions. The global immune system model structure incorporates cellular members of innate and adaptive immunity as well as cytokines. Our computational approach allows the actions of these members to be enhanced or suppressed to simulate mechanisms of immune subversion. We have applied the model to tularemia, simulating the innate response of mice to different Francisella tularensis strains (LVS, U112, and SchuS4). The approach is to train the models with experimental animal response data, and then input the human physiological parameters to extrapolate from the animal data. Time course profiles of macrophages, dendritic cells, neutrophils, monocytes, bacterial load, and pro-inflammatory cytokines were predictive of data from experimental mouse models of tularemia. Among our next steps is the performance of sensitivity analysis in order to identify those model parameters which have the greatest impact on the outcome predictions. This analysis may help generate new hypotheses in elucidating the F. tularensis strain-specific mechanisms of pathogenesis that contribute to the highly variable levels of virulence between type A and type B strains. Ultimately, this in silico model can be used to study questions that are difficult to answer using traditional animal model approaches, such as quantifying the risk posed by a potential human exposure scenario or predicting the efficacy of vaccines or therapeutics in humans. |  |
