Beehive: NIH/NIGMS R01GM068946

Grant description

Mathematic descriptions – multifactorial gene expression funded by NIH / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

Beehive and bee related resources


  1. Characterization of gene expression patterns for social behavior as a function of age and genotype
    Whitfield CW, Ben-Shahar Y, Brillet C, Leoncini I, Crauser D, Leconte Y, Rodriguez-Zas S, Robinson GE. “Genomic dissection of behavioral maturation in the honey bee”. Proc Natl Acad Sci U S A. 2006 Oct 31;103(44):16068-75.
  2. Development and implementation of longitudinal linear and nonlinear models to describe changes in gene expression across ages.
    Rodriguez-Zas SL, Southey BR, Whitfield CW, Robinson GE. Semiparametric approach to characterize unique gene expression trajectories across time. BMC Genomics. 2006 Sep 13;7:233.
  3. Development and implementation of hierarchical linear models to investigate the gene expression profiles across bee genotypes.
    Abstract sumbmitted to the 2007 American Statisical Association meeting.

    • Smith, BJ, Southey, BR, Rodriguez-Zas, SL. 2007. Smoothing spline mixed effects modeling of multifactorial gene expression profiles. The analysis of time-course microarray data is challenging because of the wide range of gene expression patterns, multiple sources of variation, and dependency of the measurements. We studied the performance of a hierarchical approach that integrates a flexible smoothing spline description of time-trends with mixed effects modeling of technical and experimental sources of variation to describe gene expression profiles. Overall patterns of gene expression during honey bee behavioral maturation and deviations associated to race and host colony effects were identified. A penalized likelihood-based approach was used and two spline dimensional bases were compared using likelihood-based criterion. A total of XX genes exhibited differential expression across age, race and colony. The flexibility of the spline model component permitted the estimation of multiple unique trajectories in time.
    • H. A. Adams, HA, Rodriguez-Zas, SL, Southey, BR. 2007. Comparison of meta-analytical approaches for gene expression profiling. Meta-analysis allows the integration of information across studies, enhancing the estimation of effects evaluated across experiments. We evaluated two approaches to combine microarray data that have complementary advantages. Results from meta-analysis of summary results and from joint analysis of raw gene expression data pertaining to four studies profiling gene expression of honey bees at two adult maturation stages were compared. A total of 192, 89, 2207, and 5 genes had significant differential expression (P<0.01) within study, XX and 31 genes were significant in the joint and summary meta-analyses, respectively. These comparable results suggest that although joint analysis accounting for within and between studies sources of variation is desirable, the limited availability of raw data and descriptions necessary for adequate modeling make meta-analysis a suitable alternative./p>
  4. Assessment of model fit and confidence intervals using novel graphical approaches
  5. Development of public, user-friendly, free software that integrates the developed analysis and visualization tools with bioinformatic databases and tools.
  6. Communication of general mathematical and biology concepts and project findings through workshops and development of educational materials customized to different audiences