Detecting gene expression profiles associated with environmental stressors within an ecological context.

Prior to the development of any conservation strategies to mitigate deleterious impacts of environmental change and contamination, there must be a method to make meaningful predictions of the effect of environmental change on organisms. Assessment of relative transcriptomic expression patterns can provide a link between the environment and the physiological response of the organism by identifying genes that respond to environmental stressors; this information could also assist in teasing apart the molecular basis of toxicological effects vs. physiological adaptation. Molecular responses to environmental stressors are probably not restricted to single or few genes, and therefore a more integrative approach is required to examine broad-scale patterns of transcriptomic response. To address this objective, used machine learning tools to link the mechanisms of physiological response to environmental stress; although widely used in clinical applications, such as finding the genetic basis of diseases (), ecological genomics applications of artificial neural networks are just beginning to emerge. Analyses such as these are important to help identify limitations on the adaptive capacity of organisms and to predict impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms. [ABSTRACT FROM AUTHOR]