How to cluster gene expression dynamics in response to environmental signals
Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanisti...
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| Veröffentlicht in: | Briefings in bioinformatics 2012-03, Vol.13 (2), p.162-174 |
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| container_title | Briefings in bioinformatics |
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| creator | YAQUN WANG MENG XU ZHONG WANG MING TAO JUNJIA ZHU LI WANG RUNZE LI BERCELI, Scott A RONGLING WU |
| description | Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments. |
| doi_str_mv | 10.1093/bib/bbr032 |
| format | Article |
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The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. 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| source | Oxford Journals Open Access Collection; MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Business Source Complete; PubMed Central |
| subjects | Algorithms Biological and medical sciences Cell Differentiation Cluster Analysis Computer Simulation Databases, Genetic Fundamental and applied biological sciences. Psychology Gene Expression Gene Expression Profiling - methods Gene-Environment Interaction General aspects Identification Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Normal distribution Proteins |
| title | How to cluster gene expression dynamics in response to environmental signals |
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