Person:
Wang, May Dongmei

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https://hdl.handle.net/1853/71816
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    An interactive visualization tool and data model for experimental design in systems biology
    (Georgia Institute of Technology, 2008-08) Kapoor, Shray ; Quo, Chang Feng ; Merrill, Alfred H. ; Wang, May Dongmei
    Experimental design is important, but is often under-supported, in systems biology research. To improve experimental design, we extend the visualization of complex sphingolipid pathways to study biosynthetic origin in SphinGOMAP. We use the ganglio-series sphingolipid dataset as a test bed and the Java Universal Network / Graph Framework (JUNG) visualization toolkit. The result is an interactive visualization tool and data model for experimental design in lipid systems biology research. We improve the current SphinGOMAP in terms of interactive visualization by allowing (i) choice of four different network layouts, (ii) dynamic addition / deletion of on-screen molecules and (iii) mouse-over to reveal detailed molecule data. Future work will focus on integrating various lipid-relevant data systematically i.e. SphinGOMAP biosynthetic data, Lipid Bank molecular data (Japan) and Lipid MAPS metabolic pathway data (USA). We aim to build a comprehensive and interactive communication platform to improve experimental design for scientists globally in high-throughput lipid systems biology research.
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    ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses
    (Georgia Institute of Technology, 2008) Stokes, Todd H. ; Torrance, J. T. ; Li, Henry ; Wang, May Dongmei
    Background. A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the "curse-of-dimension" problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create "intelligent" microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources. Results. To address the problems discussed, we have developed a community maintained system called ArrayWiki that unites disparate meta-data of microarray meta-experiments from multiple primary sources with four key features. First, ArrayWiki provides a user-friendly knowledge management interface in addition to a programmable interface using standards developed by Wikipedia. Second, ArrayWiki includes automated quality control processes (caCORRECT) and novel visualization methods (BioPNG, Gel Plots), which provide extra information about data quality unavailable in other microarray repositories. Third, it provides a user-curation capability through the familiar Wiki interface. Fourth, ArrayWiki provides users with simple text-based searches across all experiment meta-data, and exposes data to search engine crawlers (Semantic Agents) such as Google to further enhance data discovery. Conclusions. Microarray data and meta information in ArrayWiki are distributed and visualized using a novel and compact data storage format, BioPNG. Also, they are open to the research community for curation, modification, and contribution. By making a small investment of time to learn the syntax and structure common to all sites running MediaWiki software, domain scientists and practioners can all contribute to make better use of microarray technologies in research and medical practices. ArrayWiki is available at http://www.bio-miblab.org/arraywiki.
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    Computational modeling of a metabolic pathway in ceramide de novo synthesis
    (Georgia Institute of Technology, 2007-08) Dhingra, Shobhika ; Freedenberg, Melissa ; Quo, Chang Feng ; Merrill, Alfred H. ; Wang, May Dongmei
    Studies have implicated ceramide as a key molecular agent in regulating programmed cell death, or apoptosis. Consequently, there is significant potential in targeting intracellular ceramide as a cancer therapeutic agent. The cell’s major ceramide source is the ceramide de novo synthesis pathway, which consists of a complex network of interdependent enzyme-catalyzed biochemical reactions. To understand how ceramide works, we have initiated the study of the ceramide de novo synthesis pathway using computational modeling based on fundamental principles of biochemical kinetics. Specifically, we designed and developed the model in MATLAB SIMULINK for the behavior of dihydroceramide desaturase. Dihydroceramide desaturase is one of three key enzymes in the ceramide de novo synthesis pathway, and it converts a relatively inert precursor molecule, dihydroceramide into biochemically reactive ceramide. A major issue in modeling is parameter estimation. We solved this problem by adopting a heuristic strategy based on a priori knowledge from literature and experimental data. We evaluated model accuracy by comparing the model prediction results with interpolated experimental data. Our future work includes more experimental validation of the model, dynamic rate constants assessment, and expansion of the model to include additional enzymes in the ceramide de novo synthesis pathway.
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    Dynamic pathway modeling of sphingolipid metabolism
    (Georgia Institute of Technology, 2004-09) Henning, Peter A. ; Merrill, Alfred H. ; Wang, May Dongmei
    We report our research results on computational metabolome study. The goal of this research is to extend the integrated experimental modeling methodologies in sphingolipid metabolism study to other complex biological process studies such as signal transduction or gene regulation. Another feature of this work is that the 3-D information representation enables the user orchestrate the simulated pathways in real time.