Developing Predictive Models for Carcinogen-Induced Mutation Pathways in Aquatic Species Using The Mutagenesispredictnet Algorithm

Abstract

Mutations in aquatic species induced by carcinogens represent many difficulties, not limited to complex environmental exposures and a variety of biological responses disrupting ecosystem health and biodiversity. Understanding mutation pathways due to carcinogenic exposure (e.g., polycyclic aromatic hydrocarbons (PAHs), heavy metals, industrial pollutants) for ecological risk assessments and measurements for food safety inspections requires myriad assessments [5] [7]. Individual studies are often limited by the scales of duration for the experiment and/or ethical issues for a particular aquatic species. Predictive computational modelling is a distinct form of assessment because it can fill gaps in existing data by simulating mutation pathways or identifying carcinogenic endpoints or even potential outcomes. This study introduces MutagenesisPredictNet, an algorithm to predict carcinogen-induced mutation pathways in aquatic organisms using environmental pollutant data along with molecular data (e.g., genetics). The algorithms use artificial intelligence to use machine learning and data analysis to model the interdependent nature between carcinogens and the DNA repair system, to predict where something will mutate (the hot spots), and any environmental condition that may affect the pathway. The findings of the study demonstrate that MutagenesisPredictNet can predict species mutation profiles specific to the environmental exposure using products of the pollutant, and give us information and understanding of mechanisms for carcinogenesis for future studies. The predictions can support environmental health surveillance, regulatory and policy efforts, and conservation actions. MutagenesisPredictNet advances aquatic toxicology by developing a modular approach enabling it to analyze multiple species, an approach that is more environmentally friendly than conducting new animal studies, as well as providing a platform to assess environmental exposure and degradation scenarios, as it reduces the potential carcinogenesis risk in aquatic ecosystems and important human food sources.