Among limits that hamper every “omics” to fulfil logical promises they have raised figure the constant problem of quantification. Integration of “omics” results to system biology (combining both experimental data and mathematic modelling) supposes to start with precise and accurate quantitative raw data. Indeed, whereas random biological errors could not be circumvented, introducing errors at beginning of process and accumulating such errors at each step of aggregating data will unavoidably distort mathematic model calculations. As both accuracy and precision of multi-step biological measurement procedures are always the sum of many different elementary errors resulting from either systematic or stochastic influences, at the end of such complex process integration of false results due to error accumulation would possibly generate false predictions. In a simple comparison system where large difference between two studied cases is expected, even if systematic and stochastic errors do occur, it will be still possible to draw up true concluding remarks. In a complex system in which differences can range within a few percent, no clear answer or no answer at all would be made. This adverse error propagation can be approximated by statistical calculations but unfortunately is rarely performed. If these results are sustained by economic considerations, like in the field of drug discovery, or by vital ones, in the medical field, availability of accurate quantitative methods looks like a mandatory challenge.