We revisit an application developed originally using Induc- tive Logic Programming (ILP) by replacing the underlying Logic Pro- gram (LP) description with Stochastic Logic Programs (SLPs), one of the underlying Probabilistic ILP (PILP) frameworks. In both the ILP and PILP cases a mixture of abduction and induction are used. The abductive ILP approach used a variant of ILP for modelling inhibition in metabolic networks. The example data was derived from studies of the e®ects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their bio°uids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). The ILP approach learned logic models from non-probabilistic examples. The PILP approach applied in this paper is based on a gen- eral approach to introducing probability labels within a standard sci- enti¯c experimental setting involving control and treatment data. Our results demonstrate that the PILP approach not only leads to a signi¯- cant decrease in error accompanied by improved insight from the learned result but also provides a way of learning probabilistic logic models from probabilistic examples.