Nutritional systems, such as the net energy system or the ideal amino acid profile are widely developed in swine nutrition. These systems are based on the premise that the feeding value of feed ingredients and nutritional requirements of animals can be expressed on the same scale. However, it is generally acknowledged that nutrient utilisation is a dynamic phenomenon, implying that ‘value’ and ‘requirement’ are in fact the result of an interaction between the feed and the animal. To illustrate the existence of such an interaction, the current INRA-AFZ database of feed values (Sauvant et al., 2002) includes two digestible energy values for each feed ingredient: one for growing pigs and one for sows (Noblet et al., 2002). The systems approach (of which modelling is a formalisation) is ideally suited to account for interactions and to study different aspects of a system. Even though considerable progress has been made in the development of models of nutrient utilisation, application of these models beyond the context of research has been relatively modest. Over the past 25 years, different research programs have been carried out in our laboratory with the objective to develop response curves of animals to nutrient supplies. Animal production is facing new challenges that call for a more integrative approach towards nutrition. The objective of the InraPorc® project is to integrate the current state of knowledge in a nutritional model for growing pigs and sows, and make it available as a decision support tool to end-users. The objective for the growing pig (15–150 kg BW) model is to analyse nutrient utilisation for characterized pig types and to evaluate the effects of using different nutritional strategies in terms of nutrient utilisation, performance and carcass characteristics. As model parameters related to feed intake and growth potential are adjusted by the model user, growth (in an absolute sense) is not predicted. The model is based on the transformation of dietary nutrients to body protein and lipid, which are then used to predict body weight, lean body mass and backfat thickness. The representation of nutrient utilisation is mostly based on concepts used in net energy and ideal protein systems. Driving forces of the model include feed intake, the partitioning of energy between protein and lipid deposition, and availability of dietary protein and amino acids. Using literature data, the model appeared reasonably well capable of predicting the consequence of a nutrient intake restriction. The decision support tool is available at http://www.rennes.inra.fr/inraporc/. Through a user-friendly interface, the tool can be used to visualise different aspects of nutrient utilisation and excretion.