The article explores how different regression models can predict metabolic processes without knowing the exact pathways involved. By using isotopic data from a simulated gluconeogenesis pathway, the researchers found that artificial neural networks outperformed linear models in predicting fluxes accurately. This shows that neural networks can better capture the complexities of metabolic systems. The study also identified key isotopomers that influence the predicted flux values, providing valuable insights for metabolomics and future metabolic research using isotopic tracers.