Integrating gene expression and metabolic profiles

Zheng Li, Christina Chan

    Research output: Contribution to journalArticle

    • 21 Citations

    Abstract

    Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.

    Original languageEnglish (US)
    Pages (from-to)27124-27137
    Number of pages14
    JournalJournal of Biological Chemistry
    Volume279
    Issue number26
    DOIs
    StatePublished - Jun 25 2004

    Profile

    Metabolome
    Transcriptome
    Genes
    Metabolic Networks and Pathways
    Least-Squares Analysis
    Gene Expression
    Proteins
    Cyclic AMP Receptor Protein
    Gene expression
    Gene Regulatory Networks
    Urea
    Triglycerides
    Technology
    Insulin Coma
    Acyclic Acids
    Etimizol
    Buccal Administration
    Cerebellar Ataxia
    Throughput

    ASJC Scopus subject areas

    • Biochemistry

    Cite this

    Integrating gene expression and metabolic profiles. / Li, Zheng; Chan, Christina.

    In: Journal of Biological Chemistry, Vol. 279, No. 26, 25.06.2004, p. 27124-27137.

    Research output: Contribution to journalArticle

    Li, Zheng; Chan, Christina / Integrating gene expression and metabolic profiles.

    In: Journal of Biological Chemistry, Vol. 279, No. 26, 25.06.2004, p. 27124-27137.

    Research output: Contribution to journalArticle

    @article{67883e66eb4249e28722880f6403cb8f,
    title = "Integrating gene expression and metabolic profiles",
    abstract = "Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.",
    author = "Zheng Li and Christina Chan",
    year = "2004",
    month = "6",
    doi = "10.1074/jbc.M403494200",
    volume = "279",
    pages = "27124--27137",
    journal = "Journal of Biological Chemistry",
    issn = "0021-9258",
    publisher = "American Society for Biochemistry and Molecular Biology Inc.",
    number = "26",

    }

    TY - JOUR

    T1 - Integrating gene expression and metabolic profiles

    AU - Li,Zheng

    AU - Chan,Christina

    PY - 2004/6/25

    Y1 - 2004/6/25

    N2 - Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.

    AB - Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.

    UR - http://www.scopus.com/inward/record.url?scp=3042692962&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=3042692962&partnerID=8YFLogxK

    U2 - 10.1074/jbc.M403494200

    DO - 10.1074/jbc.M403494200

    M3 - Article

    VL - 279

    SP - 27124

    EP - 27137

    JO - Journal of Biological Chemistry

    T2 - Journal of Biological Chemistry

    JF - Journal of Biological Chemistry

    SN - 0021-9258

    IS - 26

    ER -