Abstract
The multi-factorial nature of adverse reproductive effects mediated by endocrine disrupting compounds (or EDCs) makes understanding the mechanistic basis of reproductive dysfunction a highly pertinent area of research. As a consequence, a main motivator for continued research is to integrate 'multi-leveled' complexity (i.e., from genes to phenotype) using mathematical methods capable of encapsulating properties of physiological relevance. In this study, an in silica stoichiometric model of piscine steroidogenesis was augmented with a 'biomass' reaction associating the underlying stoichiometry of steroidogenesis with a reaction representative of gonad growth. The ability of the in silica model to predict perturbed steroidogenesis and subsequent effects on gonad growth was tested by exposing reproductively active male and female fathead minnows (Pimephales promelas) to 88 ng/L of the synthetic estrogen, 17 alpha-ethynylestradiol (EE2). The in silica model was parameterized (or constrained) with experimentally quantified concentrations of selected steroid hormones (using mass spectrometry) and fold changes in gene expression (using RT-qPCR) for selected steroidogenic enzyme genes, in gonads of male and female fish. Once constrained, the optimization framework of flux balance analysis (FBA) was used to calculate an optimal flux through the biomass reaction (analogous to gonad growth) and associated steroidogenic flux distributions required to generate biomass. FBA successfully predicted effects of EE2 exposure on fathead minnow gonad growth (%gonadosomatic index or %GSI) and perturbed production of steroid hormones. Specifically, FBA accurately predicted no effects of exposure on male %GSI and a significant reduction for female %GSI. Furthermore, in silica simulations accurately identified disrupted reaction fluxes catalyzing productions of androgens (in male fish) and progestogens (in female fish), an observation which agreed with in vivo experimentation. The analyses presented is the first-ever to successfully associate underlying flux properties of the steroidogenic network with gonad growth in fish, an approach which can incorporate in silico predictions with toxicological risk assessments.