How NovaMechanics developed a cloud-based biokinetic modelling platform for predicting nanomaterial uptake and bioaccumulation in aquatic and soil organisms using genetic algorithm-optimized compartment models. Modelling Nanomaterial Bioaccumulation Understanding how engineered nanomaterials accumulate in organisms is critical for ecological risk assessment. Traditional biokinetic models struggle with the nonlinear uptake and elimination kinetics of nanomaterials. Parameter fitting for compartment models requires robust optimization approaches, and model selection between competing formulations demands rigorous statistical criteria. One-Compartment Model (OC) Implemented the classic one-compartment biokinetic model describing nanomaterial uptake and elimination with first-order kinetics. Computes uptake rate (ku), elimination rate (ke), and bioconcentration factor (BCF). OC with Stored Fraction (OC-SF) Extended with an OC-SF model accounting for a fraction of accumulated nanomaterial that is not readily eliminated — representing sequestration in tissues or organelles, important for persistent NMs like Ag and TiO2. Genetic Algorithm Optimization Implemented a genetic algorithm (using the Jenetics library in Java) for nonlinear regression parameter fitting. GA handles the complex, multimodal parameter spaces typical of nanomaterial biokinetic data better than traditional least-squares approaches. Statistical Model Selection Applied Akaike Information Criterion (AIC, AICc) and Akaike weights alongside adjusted R-squared to rigorously select between OC and OC-SF models. This ensures the most parsimonious model is chosen for each organism-NM combination. Multi-Organism Validation Validated across 8+ case studies including D. magna (Ag NMs, TiO2 NMs), earthworms (Ag, SiO2 NMs), and D. rerio (C60, graphene, GO, Au NMs), demonstrating broad applicability across organisms and nanomaterial types.
