This paper presents a method for enabling the relational learning or inductive logic programming (ILP) frame- work to deal with quantitative information from experimental data in systems biology. The study of systems biology through ILP aims at improving the understanding of the physiological state of the cell and the interpre- tation of the interactions between metabolites and signaling networks. A logical model of the glycolysis and pentose phosphate pathways of E. Coli is proposed to support our method description. We explain our original approach to building a symbolic model applied to kinetics based on Michaelis-Menten equation, starting with the discretization of the changes in concentration of some of the metabolites over time into relevant levels. We can then use them in our ILP-based model. Logical formulae on concentrations of some metabolites, which could not be measured during the dynamic state, are produced through logical abduction. Finally, as this re- sults in a large number of hypotheses, they are ranked with an expectation maximization algorithm working on binary decision diagrams.