Linking molecular responses to organismal outcomes under complex contaminant stress: a systems biology approach for predictive ecotoxicology

However, real-world ecosystems are subject to multiple, interacting stressors, particularly from anthropogenic sources, that challenge the predictive power of these conventional approaches. To address this gap, we investigated the effects of coal ash, a complex mixture of heavy metals, on the freshwater invertebrate Daphnia magna.

We quantified both suborganismal (transcriptomic) and individual-level (survival, growth, reproduction) responses to coal ash exposure. These data were integrated into a Dynamic Energy Budget (DEB) model to simulate physiological modes of action (pMoAs). Using machine learning, we identified gene sets predictive of DEB state variables and prioritized differentially expressed genes to determine the most plausible bioenergetic disruption.