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Conceptual Systems Model as a Tool for Hypothesis Generation and Testing in Ecotoxicological Research
Citation:
VILLENEUVE, D. L., A. L. MIRACLE, P. LARKIN, K. M. JENSEN, M. D. KAHL, E. A. MAKYNEN, E. J. DURHAN, I. KNOEBL, AND G. T. ANKLEY. Conceptual Systems Model as a Tool for Hypothesis Generation and Testing in Ecotoxicological Research. Presented at SETAC-Europe, Lille, FRANCE, May 22 - 26, 2005.
Impact/Purpose:
We conclude that conceptual systems models in -omics research has potential to generate more accurate and predictive systems models to support basic research as well as ecological risk assessments.
Description:
Microarray, proteomic, and metabonomic technologies are becoming increasingly accessible as tools for ecotoxicology research. Effective use of these technologies will depend, at least in part, on the ability to apply these techniques within a paradigm of hypothesis driven research. We propose that conceptual systems models can serve as powerful tools that facilitate hypthesis-based conduct of -omics research. In cases where some a priori knowledge of likely mechanism of action (MOA) is available, conceptual systems models can be used to develop testable hypotheses regarding the effects of chemical exposure on tens to hundreds of genes, proteins, and/or metabolic products. Conceptual systems models can also be used to cluster genes or proteins into functional groups representing various regulatory nodes within the system of interest. Examination of effects on such functional clusters can be used to generate hypotheses regarding the likely effect of the -omic changes on higher level function (i.e. at the tissue, organ, or individual level) and/or the likely MOA of unknown or poorly characterized compounds. Examples of these applications are provided using a conceptual model of the teleost brain-pituitary-gonadal (BPG) axis and empirical results from fathead minnow 17B-estradiol or the aromatase inhibitor, fadrozole, for 24 h and 7 d. We conclude that conceptual systems models in -omics research has potential to generate more accurate and predictive systems models to support basic research as well as ecological risk assessments.