You are here:
Fathead Minnow Steroidogenesis: In Silico Analyses Reveals Tradeoffs Between Nominal Target Efficacy and Robustness to Cross-talk
Citation:
SHOEMAKER, J. E., K. GAYEN, F. J. DOYLE, III, N. GARCIA-REYERO, E. J. PERKINS, DAN VILLENEUVE, AND L. LIU. Fathead Minnow Steroidogenesis: In Silico Analyses Reveals Tradeoffs Between Nominal Target Efficacy and Robustness to Cross-talk. BMC Systems Biology. BioMed Central Ltd, London, Uk, 4(89):1-17, (2010).
Impact/Purpose:
The research contributes to on-going efforts to make complementary use of computational models and laboratory toxicity data, measured at multiple levels of biological organization, to understand system-wide responses of the small fish reproductive system to endocrine active chemicals. Such efforts will provide the scientific foundation for greater use of predictive models and endpoints as a basis for quantitative ecological risk assessment.
Description:
This paper presents the formulation and evaluation of a mechanistic mathematical model of fathead minnow ovarian steroidogenesis. The model presented in the present study was adpated from other models developed as part of an integrated, multi-disciplinary computational toxicology research program using small fish models (e.g., the graphical model of the teleost hypothalamic-pituitary-gonadal-axis constructed by Villeneuve et al. [2007], a steady state model of ovarian steroidogenesis constructed by Breen et al. [2007]). However, unlike the previous models, this is the first model associated with the project that considers transcriptomic data and investigates causality between variations in gene transcription and steroid production. The research contributes to on-going efforts to make complementary use of computational models and laboratory toxicity data, measured at multiple levels of biological organization, to understand system-wide responses of the small fish reproductive system to endocrine active chemicals. Such efforts will provide the scientific foundation for greater use of predictive models and endpoints as a basis for quantitative ecological risk assessment.